We often receive questions about our decision to anchor network visibility to network metadata as well as how we choose and design the algorithmic models to further enrich it for data lakes and even SIEMs.
As the threat landscape evolves, the Vectra team sees budgets used to double down on larger security teams and expand perimeter defenses. It stems from an effort to increase threat detections and accelerate triage.
Unfortunately, this is a false premise.
A major challenge of a good incident response program is balancing the need for visibility, detection and response with the cost and complexity of building and maintaining a usable and effective security stack.
Imagine having a security tool that thinks the way you teach it to think, that takes action when and how you have trained it to act. No more adapting your work habits to generic rules written by a third party and wondering how to fill in security gaps that the rules did not tell you about.
In February 2014, journalist Martin Wolf wrote a piece for the London Financial Times titled Enslave the robots and free the poor. He began the piece with the following quote:
“In 1955, Walter Reuther, head of the US car workers’ union, told of a visit to a new automatically operated Ford plant. Pointing to all the robots, his host asked: How are you going to collect union dues from those guys? Mr. Reuther replied: And how are you going to get them to buy Fords?”
The United States has not been hit by a paralyzing cyberattack on critical infrastructure like the one that sidelined Ukraine in 2015. That attack disabled Ukraine's power grid, leaving more than 700,000 people in the dark.
But the enterprise IT networks inside energy and utilities networks have been infiltrated for years. Based on an analysis by the U.S. Department of Homeland Security (DHS) and FBI, these networks have been compromised since at least March 2016 by nation-state actors who perform reconnaissance activities looking industrial control system (ICS) designs and blueprints to steal.
Microsoft unveiled the Azure Virtual Network TAP, and Vectra announced its first-mover advantage as a development partner and the demonstration of its Cognito platform operating in Azure hybrid cloud environments.
The frenetic pace at which artificial intelligence (AI) has advanced in the past few years has begun to have transformative effects across a wide variety of fields. Coupled with an increasingly (inter)-connected world in which cyberattacks occur with alarming frequency and scale, it is no wonder that the field of cybersecurity has now turned its eye to AI and machine learning (ML) in order to detect and defend against adversaries.
The use of AI in cybersecurity not only expands the scope of what a single security expert is able to monitor, but importantly, it also enables the discovery of attacks that would have otherwise been undetectable by a human. Just as it was nearly inevitable that AI would be used for defensive purposes, it is undeniable that AI systems will soon be put to use for attack purposes.
2018 Black Hat survey: It’s about time and talent
We love Black Hat. It’s the best place to learn what information security practitioners really care about and what is the truth of our industry. Because we want to always be relevant to customers, we figured Black Hat is an ideal event to ask what matters.
In the last blog post, we alluded to the No-Free-Lunch (NFL) theorems for search and optimization. While NFL theorems are criminally misunderstood and misrepresented in the service of crude generalizations intended to make a point, I intend to deploy a crude NFL generalization to make just such a point.
You see, NFL theorems (roughly) state that given a universe of problem sets where an algorithm’s goal is to learn a function that maps a set of input data X to a set of target labels Y, for any subset of problems where algorithm A outperforms algorithm B, there will be a subset of problems where B outperforms A. In fact, averaging their results over the space of all possible problems, the performance of algorithms A and B will be the same.
With some hand waving, we can construct an NFL theorem for the cybersecurity domain: Over the set of all possible attack vectors that could be employed by a hacker, no single detection algorithm can outperform all others across the full spectrum of attacks.
Recently, Vectra published the 2018 Black Hat Edition of the Attacker Behavior Industry Report, which covers the period from January through June 2018. While there are plenty of threat-research reports out there, this one offers unique insights about real-world cyberattacker behaviors found in cloud, data center and enterprise networks.
Topics: attacker behavior
In fact, this notion has been formalized and shown mathematically in a result known as the No Free Lunch theorem (Wolpert and Macready 1997).
It’s me again – Cognito. As always, I’ve been hard at work with Vectra to automate cyberattack detection and threat hunting. Recently, we made an alarming discovery: hackers are using hidden tunnels to break into and steal from financial services firms!
Clearly, this is serious business if it involves bad guys targeting massive amounts of money and private information. But what exactly are we dealing with? Let’s dig into what hidden tunnels are and how I find them to uncover the answer.
Deep learning refers to a family of machine learning algorithms that can be used for supervised, unsupervised and reinforcement learning.
These algorithms are becoming popular after many years in the wilderness. The name comes from the realization that the addition of increasing numbers of layers typically in a neural network enables a model to learn increasingly complex representations of the data.
If you’re joining me for the first time, I want to introduce myself. I am Cognito, the AI cybersecurity platform from Vectra. My passion is hunting-down cyberattackers – whether they’re hiding in data centers and cloud workloads or user and IoT devices.
Cybersecurity analysts are overwhelmed with security events that need to be triaged, analyzed, correlated and prioritized. If you’re an analyst, you probably have some incredible skills but are being held back by tedious, manual work.
There are numerous techniques for creating algorithms that are capable of learning and adapting over time. Broadly speaking, we can organize these algorithms into one of three categories – supervised, unsupervised, and reinforcement learning.
Supervised learning refers to situations in which each instance of input data is accompanied by a desired or target value for that input. When the target values are a set of finite discrete categories, the learning task is often known as a classification problem. When the targets are one or more continuous variables, the task is called regression.
“The original question ‘Can machines think?’ I believe to be too meaningless to deserve discussion. Nevertheless, I believe that at the end of the century, the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.” – Alan Turing
Can machines think?
The question itself is deceptively simple in so far as the human ability to introspect has made each of us intimately aware of what it means to think.
While ransomware attacks like NotPetya and WannaCry were making headlines (and money) in 2017, cryptocurrency mining was quietly gaining strength as the heir apparent when it comes to opportunistic behaviors for monetary gain.
“We may compare a man in the process of computing a real number to a machine which is only capable of a finite number of conditions…” – Alan Turing
It is difficult to tell the history of AI without first describing the formalization of computation and what it means for something to compute. The primary impetus towards formalization came down to a question posed by the mathematician David Hilbert in 1928.
In my last blog, I spoke about a financial customer performing pen testing and how I helped the blue team detect the red team as it carried-out an attack. I’m back again today with another story from the trenches.
This time, I’ve been working with a customer in the manufacturing sector who recently deployed me. As before, this customer prefers to remain anonymous to keep cybercriminals in the dark about their newly developed security capabilities. To stay on top of their game, they routinely run red team exercises.
Vectra® was recently positioned as the sole Visionary in the Gartner 2018 Magic Quadrant for Intrusion Detection and Prevention Systems (IDPS). I’m pretty ecstatic about that.
Over the years, intrusion detection systems (IDS) have converged with intrusion prevention systems (IPS) and the two are now known collectively as IDPS. This convergence occurred as the security industry focused more on preventing external threat actors.
Traffic sent to and from major internet sites was briefly rerouted to an ISP in Russia by an unknown party. The likely precursor of an attack, researchers describe the Dec. 13 event as suspicious and intentional.
According to BGPMON, which detected the event, starting at 04:43 (UTC) 80 prefixes normally announced by several organizations were detected in the global BGP routing tables with an Origin AS of 39523 (DV-LINK-AS), out of Russia.
Random forest, an ensemble method
The random forest (RF) model, first proposed by Tin Kam Ho in 1995, is a subclass of ensemble learning methods that is applied to classification and regression. An ensemble method constructs a set of classifiers – a group of decision trees, in the case of RF – and determines the label for each data instance by taking the weighted average of each classifier’s output.
The learning algorithm utilizes the divide-and-conquer approach and reduces the inherent variance of a single instance of the model through bootstrapping. Therefore, “ensembling” a group of weaker classifiers boosts the performance and the resulting aggregated classifier is a stronger model.
The United States has not been the victim of a paralyzing cyber-attack on critical infrastructure like the one that occurred in the Ukraine in 2015. That attack disabled the Ukrainian power grid, leaving more than 700,000 people helpless.
But the United States has had its share of smaller attacks against critical infrastructure. Most of these attacks targeted industrial control systems (ICS) and the engineering personnel who have privileged access.
Hey everyone. For my first blog, I want to share a story about my role on the blue team during a recent red team exercise.
But first, I want to introduce myself to those of you who might not know me. I am Cognito, the artificial intelligence in the Vectra cybersecurity platform. My passion in life is hunting-down cyber attackers – whether they’re hiding in data centers and cloud workloads or user and IoT devices.
This blog was originally published on LinkedIn.
The security industry is rampant with vendors peddling anomaly detection as the cure all for cyber attacks. This is grossly misleading.
The problem is that anomaly detection over-generalizes: All normal behavior is good; all anomalous behavior is bad – without considering gradations and context. With anomaly detection, the distinction between user behaviors and attacker behaviors is nebulous, even though they are fundamentally different.
Over lunch last week, a customer who recently deploy our Cognito™ platform told me that his SIEM sales person said “We can do what Vectra does with our analytics package. I simply looked at him and said, “No body, no murder – no they can’t.”
He was puzzled, so I explained.
Many security teams are overwhelmed with the scale and ferociousness of digital threats. Threats are sneakier and more damaging, and security operations centers (SOCs) are being worn down investigating and stomping out incidents.
As enterprises migrate to the cloud, strong perimeter defenses are not enough to stop cyber attackers from infiltrating the network. Together, Gigamon and Vectra enable organizations to gain network visibility and automate threat management - providing continuous monitoring of network traffic to pinpoint cyber attacks that evaded perimeter defenses.
Chris Morales, Head of Security Analytics at Vectra joins us to discuss what challenges he sees customers facing when moving to Amazon Web Services (AWS) and how Gigamon and Vectra can help them.
Attacks never really go away
Many enterprise organizations are currently evaluating the Vectra Cognito platform, and over the past weeks, several customers detected WannaCry attacker behaviors. Just because the headlines stopped, doesn’t mean that the attack did.
WannaCry was first reported by the media in May of this year and we had customers who detected and responded to outbreaks within minutes. A couple of days after the initial impact, it was reported that stopping the WannaCry command and control server limited the effectiveness of WannaCry in the wild. While that may have been be true, organizations are still detecting instances of WannaCry within their enterprise networks. While this is a smaller scale than the attack in May, it is important that enterprises continue to monitor their networks for what is proven to be a fast propagating ransomware attack with the potential to cause damage very quickly.
In the fight against cyber-attacks, time is money. According to the Ponemon institute, the average cost of a data breach is $3.62 million. Reducing the time to detect and time contain an incident can significantly mitigate the cost of a breach, and possibly prevent it.
Maturity level and effectiveness are two of the most important measurements of SOC performance. Maturity reflects an enterprise’s development level regarding its approach to managing cybersecurity risk, including risk and threat awareness, repeatability, and adaptiveness. Effectiveness is a measurement of the SOC’s ability to detect and respond to an incident as it happens.
We conducted a survey.
The European Union (EU) General Data Protection Regulation (GDPR) is set to come into force on 25 May 2018. However, many IT, security and compliance leaders in the EU and globally still have a long way to go before they can truly describe themselves as "GDPR-ready." Artificial intelligence (AI) can make valuable contributions toward GDPR preparations and operational compliance.
We are seeing another outbreak of ransomware that appears to be a combination of previous other ransomware campaigns. As is always the case, criminal gangs learn from each other.
Petya was successful in 2016 using email attack campaigns and a ransomware-as-a-service business model. Wannacry introduced new worm propagation techniques proving highly successful in hitting thousands of systems in a short time span last month.
Cisco recently announced the term “intent-based networking” in a press release that pushes the idea that networks need to be more intuitive. One element of that intuition is for networks to be more secure without requiring a lot of heavy lifting by local network security professionals. And a featured part of that strategy is Cisco ETA:
"Cisco's Encrypted Traffic Analytics solves a network security challenge previously thought to be unsolvable," said David Goeckeler, senior vice president and general manager of networking and security. "ETA uses Cisco's Talos cyber intelligence to detect known attack signatures even in encrypted traffic, helping to ensure security while maintaining privacy."
Earlier this month Vectra announced plans to leverage the capabilities of VMware NSX to accelerate the detection and mitigation of hidden cyber attackers in virtualized data centers.
Vectra currently applies artificial intelligence to automatically detect attacker behaviors inside virtualized data centers. Vectra also integrates with endpoint and network response tools to automate the workflow.
Vectra Networks last week published the 2017 Post-Intrusion Report, which covers the period from January through March. While there are plenty of threat research reports out there, this one offers unique insights about real-world cyber attacks against actual enterprise networks.
Most industry security reports focus on statistics of known threats (exploits and malware families) or give a post-mortem look back at breaches that were successful. The first one looks at threats that network perimeter defenses were able to block and the second lists attacks that were missed entirely.
Vectra Threat Labs analyzed the WannaCry ransomware to understand its inner workings. They learned that while the way it infects computers is new, the behaviors it performs are business as usual.
WannaCry and its variants behave similarly to other forms of ransomware that Vectra has detected and enabled customers to stop before experiencing widespread damage. This is a direct benefit of focusing on detecting ransomware behaviors rather than specific exploits or malware. Many of WannaCry’s behaviors are reconnaissance and lateral movement on the internal network, within the enterprise perimeter.
What just happened?
A ransomware attack is spreading very rapidly among unpatched Windows systems worldwide. This morning, the attack was initially believed to target the UK National Health Service, but throughout the day, it has become apparent this is a global attack.
Kaspersky labs reported on Friday afternoon that at least 45,000 hosts in 74 countries were infected. Avast put the tally at 57,000 infections in 99 countries. All this, during just 10 hours. Of those infected hosts, Russia, Ukraine and Taiwan were the top targets.
There is some startling data in the 2017 Verizon Data Breach Investigation Report. What stood out to me as most concerning is that more breaches occurred in healthcare this year than last year. After reviewing the report, I see three key trends.
- The real threat is already inside healthcare networks in the form of privileged access misuse
- When healthcare organizations are hit from the outside, it is usually ransomware extorting them for money
- The growth in healthcare IoT is overwhelming and dangerous
Whether the task is driving a nail, fastening a screw, or detecting a hidden HTTP tunnel, it pays to have the right tool for the job. The wrong tool can increase the time to accomplish a task, waste valuable resources, or worse. Leveraging the power of machine learning is no different.
Vectra has adopted the philosophy of implementing the most optimal machine learning tool for each attacker behavior detection algorithm. Each method has its own strengths.
Confusion reigns on the origin of the term "bullseye." Some say it started when English archers showed off their accuracy by shooting arrows through the empty eye socket of a bull skull. Others contend it was a reference to a blemish in the center of a glass window pane.
This blog was originally published on LinkedIn.
Vendors who are trapped in a time warp often tout traffic flow analysis as a great way to detect and analyze behavior anomalies inside networks. I have a problem with that because it’s decades-old technology dressed in a new suit.
Sometimes science fiction becomes less fantastic over time than the actual reality. Take the film Ghost in the Shell, for example, which hits the big screen this week. It’s an adaptation of the fictional 28-year-old cult classic Japanese manga about human and machine augmentation.
It seems like a new variant or victim of ransomware is in the news every day. It’s newsworthy because it works so well and causes widespread destruction.
So when the recent wave of stories hit about PetrWrap, a variation of the widely known Petya ransomware strain, it was easy to miss the significance. The “no-honor-among-thieves” narrative crowded out its true importance.
The U.S. Computer Emergency Readiness Team (US-Cert) issued a warning last week stating HTTPS interception weakens TLS security. As the use of encryption for privacy has increased, the security industry has responded by intercepting and decrypting SSL sessions to perform deep-packet inspection (DPI).
This blog was originally published on LinkedIn.
To know SIEM is to love it. And hate it.
Security information and event management (SIEM) is a ubiquitous cybersecurity tool. It’s used by probably every security analyst who works in a security operations center (SOC).
This blog was originally published on Medium.
Growing up in Kenya, I shared a one-bedroom apartment with my family. In fact, I slept in the laundry/storage room in the constant presence of family laundry and stacks of suitcases. You might say I’ve been sensitive to the invasive presence of others from an early age.
Earlier this week I was at TEISS hosting a round table session titled “Artificial Intelligence – Fancy maths or a pragmatic answer to cyber security gaps and challenges?”
We explored human, threat, and technical dimensions to the current drivers and role of AI in cybersecurity. Here's a summary of our group's discussion.
Integration decreases cost and increases effectiveness. For this reason, Vectra is adaptive by design. Everything we do considers how to help our customers be more efficient and faster at fighting attacks. Sometimes it involves determining where to deliver sophisticated threat intelligence beyond the Vectra. Working with Splunk is a great example of this integration.
According to Gartner, “The goal is not to replace traditional SIEM systems, but rather to provide high-assurance, domain specific, risk-prioritized actionable insight into threats, helping enterprises to focus their security operations response processes on the threats and events that represent the most risk to them."
Saudi officials recently warned organizations in the kingdom to be on the alert for the Shamoon 2 malware, which cripples computers by wiping their hard disks. In 2012, Shamoon crippled Saudi Aramco and this new variant was reportedly targeted at the Saudi labor ministry as well as several engineering and manufacturing companies.
During a recent analysis, Vectra Networks came across a malicious component that appears to be used in conjunction with spear-phishing-delivered malicious documents.
As long as I can recall, enterprises have always relied on prevention and policy-based controls for security, deploying products such as antivirus software, IDS/IPS and firewalls.
But as we now know, and industry research firms have stated, they aren’t enough to adequately deal with today’s threat environment, which is flooded by a dizzy array of advanced and targeted attacks.
Shamoon is back, although we are not entirely sure it ever left.
On Monday, Saudi Arabia warned organizations in the kingdom to be on alert for the Shamoon virus, which cripples computers by wiping their disks. The labor ministry said it had been attacked and a chemicals firm reported a network disruption. This has been dubbed Shamoon 2 by some news outlets.
Here is a simple explanation of what is likely to be happening.
The adversary is using a combination of social engineering and email phishing to infect one or a number of computers on an organization’s networks. Either downloading a file or clicking a link downloads an exploit kit.
The computers infected with the exploit kit rapidly perform port sweeps across the subnet to which hosts are connected. Using automated replication, it then attempts to move laterally via remote procedure calls (RPCs). To cover an organization’s entire network, the adversary needs to infect machines on many subnets.
Shamoon 2, like Shamoon that struck Saudi Aramco in 2012, moves extremely fast with the sole objective of destroying systems and bringing businesses to their knees.
Healthcare organizations are prime targets of cyber attackers because they are reliant on vulnerable legacy systems, medical IoT devices with weak security and have a life or death need for immediate access to information.
This blog was originally published on The Hill.
If I didn’t deal daily with the mechanics of cybersecurity, I might be captivated by Washington’s focus on whether the Russians penetrated the Democratic National Committee and why they did it. As a citizen, I follow politics and geopolitics, too.
But here’s what bothers me:
The hacking tools identified by the FBI and Department of Homeland Security are freely available on the internet. The Russians can use them. So can the Iranians, the Chinese, the North Koreans and any other nation-state which wants to penetrate the networks that serve our political parties and government. There is nothing special or even uniquely “Russian” about them. And they often work.
I am not surprised that such common tools are employed against us. We should expect it. In the cybersecurity business we know the focus should be on our ineffective defense, rather than on finding the guilty country.
Whoever got inside the DNC networks had seven months to plumb about, pilfer embarrassing material, package it for shipping and make off with it, all without detection. The DNC had no way to detect the penetration while it was happening.
Why not? After all, the technology to spot and interrupt hacking while it is in progress exists. We can literally watch hackers and their tools move around inside our networks, probing our vulnerabilities, locating our most sensitive data and setting up private tunnels to take it out of our systems.
This blog was orignially published on ISACA Now.
In many spheres of employment, the application of Artificial Intelligence (AI) technology is creating a growing fear. Kevin Maney of Newsweek vividly summarized the pending transformation of employment and the concerns it raises in his recent article "How artificial intelligence and robots will radically transform the economy."
In the Information Security (InfoSec) community, AI is commonly seen as a savior – an application of technology that will allow businesses to more rapidly identify and mitigate threats, without having to add more humans. That human factor is commonly seen as a business inhibitor as the necessary skills and experience are both costly and difficult to obtain.
As a consequence, over the last few years, many vendors have re-engineered and re-branded their products as employing AI – both as a hat-tip to their customer’s growing frustrations that combating every new threat requires additional personnel to look after the tools and products being sold to them, and as a differentiator amongst “legacy” approaches to dealing with the threats that persist despite two decades of detection innovation.
The rebranding, remarketing, and inclusion of various data science buzzwords – machine intelligence, machine learning, big data, data lakes, unsupervised learning – into product sales pitches and collateral have made it appear that security automation is the same as AI security.
This is a prediction made by Gartner analyst Avivah Litan in her latest blog entry, The Disappearing UEBA Market. Of course it caught our attention here at Vectra. We are not a standalone UEBA company, nor do we want to be. First and foremost, we are an AI company that empowers threat hunters. But we often find ourselves in this discussion with people who believe UEBA alone will solve the world's problems (and possibly make coffee in the morning, too).
Sen. John McCain, chairman of the Senate Armed Services Committee, held a hearing today with top intelligence officials on Russian cyber-attacks, after many remarks by President-elect Donald Trump called into question conclusions by U.S. intelligence community that Kremlin-backed hackers meddled in the 2016 election.
On the cybersecurity website ThirdCertainty.com, Byron Acohido makes some very important points about the use of encryption by hackers to avoid detection tools and the need to detect these attacks. This is a water cooler discussion at Vectra headquarters. Encrypted traffic is an easy hiding place for attackers and difficult for organizations to deal with.
However, trying to monitor this traffic by decrypting first, performing deep-packet inspection, and then encrypting again at line-rate speeds is problematic, even with dedicated SSL decryption, especially in the long term. There are several factors at play here.
With an increasing global desire for privacy, more traffic is encrypted by default. It is becoming a standard for cloud applications. The Sandvine Internet Phenomena Report states that encryption doubled last year in North America.
This is actually great news, especially for consumer privacy. Enterprises have a strategy to encrypt everything. With this encryption however, attempts to perform SSL decryption mean there will be large volumes of encrypted data to process.
In previous research from the Vectra Threat Labs, we learned that seemingly innocuous vulnerabilities can become serious problems in the context of the Internet of Things (IoT). IoT is the unattended attack surface, and more IoT devices means bigger clone armies.
The recent public release of source code for malware named "Mirai" has proven exactly that. Mirai continuously scans the Internet for IoT devices using factory default usernames and passwords, primarily CCTV and DVRs.
We live in the age where big data and data science are used to predict everything from what I might want to buy on Amazon to the outcome of an election.
The results of the Brexit referendum caught many by surprise because pollsters suggested that a “stay” vote would prevail. And we all know how that turned out.
History repeated itself on Nov. 8 when U.S. president-elect Donald Trump won his bid for the White House. Most polls and pundits predicted there would be a Democratic victory, and few questioned their validity.
The Wall Street Journal article, Election Day Forecasts Deal Blow to Data Science, made three very important points about big data and data science:
- Dark data, data that is unknown, can result in misleading predictions.
- Asking simplistic questions yields a limited data set that produces ineffective conclusions.
- “Without comprehensive data, you tend to get non-comprehensive predictions.”
When asked a poorly bounded question such as “What is the biggest threat to Internet security?”, the majority of quick-fire answers can likely be represented by the flags of a handful of nation states. Certainly the front-of-mind answer – identifying a cluster of hackers – represents a constant and escalating threat to business continuity and potential compromise.
Yet, if we introspectively examine the nature of our industry, we can easily argue that the biggest risk that Internet security faces is in fact our general inability to respond and counter the attacks launched by adversaries from around the world.
It is estimated that today there are over 1 million InfoSec positions unfilled – growing to over 1.5 million by 2019 – and more than 200,000 of those vacancies are in the U.S. This global shortage of expertise and experience lies at the very heart of the InfoSec world’s ability to respond to cyber attacks – affecting vendors and consumers alike.
Vectra Threat Labs researchers have uncovered the activities of a group of individuals currently engaged in targeted attacks against entities in the Middle East. We identified over 200 samples of malware generated by the group over the last two years. These attacks are themed around Middle Eastern political issues and the motivation appears to relate to espionage, as opposed to opportunistic or criminal intentions.
The need to analyze the patch for MS16-030 recently presented itself to us due to some related product research. After the analysis was complete, we realized that the attack surface of the patch was pretty interesting and determined it may be beneficial to share part of the analysis. This post will focus on triggering a patched bug from MS16-030.
Sitting at the edge of the network and rarely configured or monitored for active compromise, the firewall today is a vulnerable target for persistent and targeted attacks
In extending the Vectra cybersecurity platform to enterprise data centers and public clouds, we wanted to do more than simply port the existing product into a virtualized environment. So, Vectra security researchers, data scientists, and developers started with a fresh sheet of paper to address the real-world challenges and threats that are unique to the enterprise data centers and clouds.
Visibility and intelligence that spans the enterprise
First, it was important to remember that the data center can be both integrally connected, yet in some ways separated from the physical enterprise. For example, attacks can spread from the campus environment to the data center environment, and security teams absolutely need to know how these events are connected. On the other hand, 80% of data center traffic never leaves the data center, making it invisible to traditional security controls.
While digging and reversing my way through the Equation Group dump, I’ve come across a few interesting pieces that probably are not getting the attention they deserve. While a lot of the initial research has focused on the potential 0-days, the dump also gives a glimpse into the backbone tools and operational methods of a serious hacking group.
Certainly 0-days are great, but in my opinion the previously disclosed Fortidoor exploit is 100x more significant than the Fortigate HTTP 0-days disclosed in the dump. And while most of the dump is around firewall implants and a few exploits, there are some very interesting ops tools in the drop. One of those tools is NOPEN. It is referenced multiple times in their script/ops/doc, and seem to be one of the cornerstones of their infrastructure. It is significant both as a backdoor in a target network or as a tool deployed on a listening post to let you create a multi-layer call-back network. So let's take a closer look at this software.
It is likely self-evident to many that the security industry’s most overused buzzword of the year is “machine learning.” Yet, despite the ubiquity of the term and its presence in company marketing literature, most people – including those working for many of the vendors using the term – don’t actually know what it means.
Security researchers with Vectra Threat Labs recently uncovered a critical vulnerability affecting all versions of Microsoft Windows reaching all the way back to Windows 95. The vulnerability allows an attacker to execute code at system level either over a local network or the Internet. As a result, attackers could use this vulnerability both to infect an end-user from the Internet, and then spread through the internal network.
Vectra and Microsoft collaborated during the investigation of this issue, and Microsoft has delivered a fix as part of Security Bulletin MS16-087, which is available here.
The vulnerabilities, CVE-2016-3238 (MS16-087), and CVE-2016-3239, stem from the way users connect to printers in the office and over the Internet. This vulnerability could enable a relatively unsophisticated attacker to incorporate IoT devices as part of an attack and quickly infiltrate and spread through a network without detection. While this blog provides an overview of the vulnerability, you can read the in-depth technical analysis here. In addition, a video summary of the vulnerability is available here.
The vulnerability in question centers around the ways that network users find and use printers on a network. Needless to say, modern organizations often have many users, and likewise often have many different makes and models of printers. Users expect to connect to and use whatever printer is most convenient, and likewise, mobile users expect to be able to come in to the office and print.
Printers present an interesting case in the world of IoT (Internet of Things), as they are very powerful hardware compared to most IoT devices, yet are not typically thought of as a “real” computer by most administrators. Over the years, many security researchers have studied and reported on printer vulnerabilities. However, the vast majority of this research focused on how to hack the printer itself in order to do things such as change the display on the printer or steal the documents that were printed. In this case, we investigate how to use the special role that printers have within most networks to actually infect end-user devices and extend the footprint of their attack within the network.
A summary of this analysis and video is available here.
Network-based malware detection addresses increasing complexity in the malware ecosystem but doesn’t make attribution a key priority.
Conventional wisdom about malware infection paints a picture that hapless users click on something they shouldn’t, that in turn takes their Web browsers to a drive-by-download website. It then exploits a vulnerability to install a botnet agent that eventually steals all their personal data and uploads it to cybercriminals in another country.
That conventional wisdom isn’t completely wrong, but it needs some serious updating. Today’s malware infections are more typically multi-stage events, wherein a user visits a favorite website with a banner advertisement supplied by a third-party ad network that was supplied by an affiliate ad network.
Ransomware, encryption and machine learning – Three key takeaways from Infosecurity 2016
Last week was a long one. Vectra participated for the first time at Infosecurity Europe in London. Now that my feet have recovered from our very busy booth I thought I shared a few of the recurring themes I noticed at the show.
Ransomware. Definitely the threat de rigueur with vendors coming at the problem from various angles, including DNS management and client based solutions. Vectra was part of the buzz too, offering a network-centric approach with our newly announced ransomware file activity detection.
They obliged and in doing so bought into a view of Caesar as superior to them. Caesar exploited this to good effect: he acted as the leader of the pirates, he practiced combat exercises with them and even read them poetry.
Eventually, Caesar’s associates returned with the silver and he was let go. He vowed to return to collect his money and kill the pirates and he went to great lengths to make good on his promise.
Caesar kept his cool, survived the hostage situation, and recovered his belongings because he had a plan and a strategy.
Insights from inside the kill chain
This week we are proud to announce the release of the third edition of the Vectra Post-Intrusion Report. And while there are plenty of reports from security vendors out there, this one provides something that is unique.
A quick no-frills solution to ransomware inside the enterprise
Ransomware is clearly the scourge of 2016. Every week there is a new and notable enterprise-level outbreak of this insidious class of malware – crippling and extorting an ever widening array of organizations.
For a threat that is overwhelmingly not targeted, it seems to be hitting large and small businesses with great success.
The malware infection can come through the front door of a failed “defense-in-depth” strategy or the side door of a mobile device latched to the corporate network on a Monday morning.
The ongoing Apple versus the FBI debate has me thinking more about the implications of encryption. Whether or not national governments around the globe choose to go down the path of further regulating encryption key lengths, requiring backdoors to encryption algorithms, mandating key escrow for law enforcement purposes, or generally weakening the implementations of encrypted communications and data storage in consumer technologies, the use of encryption will increase – and in parallel – network visibility of threats will decrease.
In light of Apple’s response to the FBI’s request to gain access to San Bernardino shooter Syed Farook’s iPhone, I thought I would share some of my thoughts on this. It appears that there is some confusion in the connection of this request from the FBI with the bigger government debate on providing backdoors and encryption.
Let me attempt to break this down a little in the hopes of clearing some of that confusion:
- Apple has positioned the request from the FBI to be a request to install a “backdoor” in their product. This is not correct. The FBI request is pretty specific and is not asking for a universal key or backdoor to Apple products.
- The FBI request should be interpreted as a lawful request to Apple to help construct a forensics recovery tool for a specific product with a unique serial number.
- The phone in question is an Apple 5C, and the method of access requested by the FBI is actually an exploitation of a security vulnerability in this (older) product. The vulnerability does not exist in the current generation of Apple iPhones.
In the rapidly expanding world of threat intelligence, avalanches of static lists combine with cascades of streaming data to be molded by evermore sophisticated analytics engines the output of which are finally presented in a dazzling array of eye-candy graphs and interactive displays.
For many of those charged with securing their corporate systems and online presence, the pressure continues to grow for them to figure out some way to incorporate this glitzy wealth of intelligence into tangible and actionable knowledge.
It seems that this last holiday season didn’t bring much cheer or goodwill to corporate security teams. With the public disclosure of remotely exploitable vulnerabilities and backdoors in the products of several well-known security vendors, many corporate security teams spent a great deal of time yanking cables, adding new firewall rules, and monitoring their networks with extra vigilance.
It’s not the first time that products from major security vendors have been found wanting.
It feels as though some vendor’s host-based security defenses fail on a monthly basis, while network defense appliances fail less frequently – maybe twice per year. At least that’s what a general perusal of press coverage may lead you to believe. However, the reality is quite different. Most security vendors fix and patch security weaknesses on a monthly basis. Generally, the issues are ones that they themselves have identified (through internal SDL processes or the use of third-party code reviews and assessment) or they are issues identified by customers. And, every so often, critical security flaws will be “dropped” on the vendor by an independent researcher or security company that need to be fixed quickly.
The Internet is chock full of really helpful people and autonomous systems that silently probe, test, and evaluate your corporate defenses every second of every minute of every hour of every day. If those helpful souls and systems aren’t probing your network, then they’re diligently recording and cataloguing everything they’ve found so others can quickly enumerate your online business or list systems like yours that are similarly vulnerable to some kind of attack or other.
Why do this?
Reports of successful hacks against Internet of Things (IoT) devices have been on the rise. Most of these efforts have involved demonstrating how to gain access to such a device or to break through its security barrier. Most of these attacks are considered relatively inconsequential because the devices themselves contain no real data of value (such as credit card numbers or PII). The devices in question generally don't provide much value to a botnet owner as they tend to have access to lots bandwidth, but have very little in terms of CPU and RAM.
Cybersecurity is a rapidly evolving landscape and this new year will be no different. Attackers will come up with new ways to infiltrate corporate networks and businesses, security vendors will be tasked with staying ahead of them, and governments will talk a lot, yet do very little. Here are some of the ways we see the industry changing shape over the course of 2016:
Mind the gap
87% of U.K. senior IT and business professionals believe there is a shortage of skilled cybersecurity staff, the same percentage of UK security leaders also want to hire CISSP credentialed staff into their teams. Nothing of real surprise in that there’s a gap; let’s fill it with demonstrable high calibre professionals, right? Well, not quite. That skills gap also includes a “CISSP” gap. With 10,000+ UK security positions out there but just over 5,000 UK CISSPs, the math simply doesn’t add up. We should also consider that credentials like CISSP demonstrate excellent existing domain knowledge but does not help hiring managers understand soft skills, attitudes and other characteristics that combine to form the overall “talent and capabilities” of a candidate.
A pragmatic approach is therefore to hire on traits such as adaptability, collaboration and innovation alongside evidence of requisite technical capabilities. After all, in a rapidly changing digital landscape you’re hiring for tomorrow’s battle not yesterday’s, so agility is essential. Today’s security teams need to be ready to handle the new risks, challenges and the increased pace of change that Internet of Things (IoT) [Read more on IoT security], cloud, mobility and social media all bring to the security challenge. The talent pool is limited, as are organisations' overall cyber security resources. It’s time to develop and support from within and broaden recruitment methodologies for those hard-to-fill open positions.
Incidents of fraud, theft and abuse enacted by rogue insiders present organisations with the ultimate in targeted threats. These are executed against them from highly motivated actors, operating with a high degree of internal organisational knowledge and comparative ease of access. Such threats have the ability to create sizable risks in relation to digital assets and are also the most challenging to manage.
Security leaders have to understand their organisation’s context and operations in order to strike a balance between protection, control and creating value.
Users tied up in complex and over-controlling systems are unable to perform. Too light a touch sees key assets and resources too easy to misuse, alter or steal. Blending layers of organisational, physical and technical policy and management can provide a meaningful way of reducing internal cyber attacks, but no solution can be perfect. Organisations must also enable themselves to identify and recognise illegitimate internal actions and make timely interventions.
IDS has been around for decades and has long been a cornerstone of network security. But over the years, IDS was gradually absorbed by IPS, and IDS simply became thought of as a deployment option of IPS.
However, this subservient role of IDS in relation to IPS introduces a subtle but important compromise – detection takes a backseat to prevention. Because IPS is deployed in-line with network traffic, performance concerns are paramount. Prevention cannot slow the speed or flow of business, and that meant detections must be near-instantaneous.
The need to block threats within milliseconds locks IDS/IPS into using signatures for detections. While signatures can detect a wide variety of threats, they rely on the fast-pattern-matching of known threats.
Today’s cyber attackers are patient, as they infiltrate and steadily persist within an organization’s network over time. These long-term attacks require ongoing communication to orchestrate the various phases of attack.
By understanding how attackers conceal their communications, we can rob attackers of the persistence and coordination that makes modern attacks so successful.
Today, Vectra researchers were again credited with discovering critical vulnerabilities that impact the security of Adobe Reader, VBScript, and Internet Explorer.
I attended the Gartner Security and Risk Management Summit in London on Sept. 14 and 15 and would like to share some key takeaways from presentations by analysts Earl Perkins, Jeremy D’Hoinne and Neil MacDonald. The following are messages that resonated with me:
Researchers from Vectra Threat Labs recently performed an in-depth analysis of vulnerabilities found in a common Belkin wireless repeater. Today in an article on Dark Reading, Vectra CTO Oliver Tavakoli digs into why seemingly innocuous vulnerabilities can become serious problems in the context of the Internet of Things (IoT). Read the full article here.
Of particular importance to security teams, IoT is not only bringing far more devices into the network, but they are also devices that very rarely get patches and updates. This means that vulnerabilities can be left unaddressed for months or even years. Likewise, these devices are unlikely to be protected by signatures and will almost assuredly be unable to run client-based security.
Big data is around us. However, it is common to hear from a lot of data scientists and researchers doing analytics that they need more data. How is that possible, and where does this eagerness to get more data come from?
Very often, data scientists need lots of data to train sophisticated machine-learning models. The same applies when using machine-learning algorithms for cybersecurity. Lots of data is needed in order to build classifiers that identify, among many different targets, malicious behavior and malware infections. In this context, the eagerness to get vast amounts of data comes from the need to have enough positive samples — such as data from real threats and malware infections — that can be used to train machine-learning classifiers.
Is the need for large amounts of data really justified? It depends on the problem that machine learning is trying to solve. But exactly how much data is needed to train a machine-learning model should always be associated with the choice of features that are used.
For years, security professionals have become increasingly aware of the limitations of signatures. And yet for all this awareness, the industry is still focused on making signatures faster instead of addressing the fundamental problem.
Threat feeds deliver signatures faster and faster and malware sandboxes generate new signatures for newly discovered malware. Nonetheless, attackers continue to evade them and are wining at an ever-increasing rate.
Recently, it came to our attention that HP DVLabs has uncovered at least ten vulnerabilities in the Belkin N300 Dual-Band Wi-Fi Range Extender (F9K1111). In response to this, Belkin released firmware version 1.04.10. As this is the first update issued for the F9K1111 and there were not any public triggers for the vulnerabilities, we thought it would be interesting to take a deeper look.
Unpacking the Update
To begin our analysis, we downloaded the firmware update from the vendor . We used a firmware tool called binwalk  to unpack the update:
On July 6th, information spread that the Italian company known as the Hacking Team were themselves the victims of a cyber attack. In the aftermath of this leak, Vectra researchers have analyzed the leaked data, and identified a previously unknown vulnerability in Internet Explorer 11 that impacts a fully patched IE 11 on both Windows 7 and Windows 8.1.
The hunt for the vulnerability began when we noticed an email from an external researcher who attempted to sell a proof-of-concept exploit to Hacking Team. The email was sent on 02/06/2015 and described an exploitable use-after-free bug in Internet Explorer 11. While Hacking Team ultimately declined to buy the PoC exploit, the email gave enough information for Vectra researchers to find and analyze the vulnerability.
While Hacking Team declined to purchase the PoC exploit, there is a chance the researcher went elsewhere to sell it, meaning that it may have been exploited in the wild.
The Internet of Things (IoT) is beginning to have a huge impact on our daily lives, and it will grow by orders of magnitude. However, the multitude of IoT devices with zero, limited or outdated security could produce disastrous results. It will be a formidable task to secure every small IoT device or toy. Security solutions that watch device behavior and identify anomalies might be our only hope.
The IoT is on the rise...
The genesis of IoT goes back to the early ’90s when PARC chief scientist Mark Weiser came up with the vision of Ubiquitous Computing and Calm Technology. In this vision, computing becomes “your quiet, invisible servant” and disappears from conscious actions and the environment of the user.
As cyber attacks increase in frequency and complexity, organizations continue to invest in prevention-centric technologies to secure their network perimeter. However, prevention-centric technologies are less than prefect. They protect networks from known threats using a combination of security rules, signatures and reputation lists.
A critical component of today’s network perimeter security is the file-based sandbox. They were created to analyze suspicious files on isolated hosts – many with different operating systems – in a contained environment.
Today, Vectra Networks published its second edition Post-Intrusion Report that offers a first-hand look at modern threats that get past perimeter security and spread inside the network.
In the latest report, we analyzed behaviors and techniques across the entire lifecycle of real-world cyber attacks. We also looked back and saw alarming changes in the threat landscape and observed emerging trends in attack techniques.
Recently, Kasperky Labs disclosed that it was the victim of a sophisticated cyber attack, which they have named Duqu 2.0. The team at Kaspersky Labs has published a detailed analysis of Duqu 2.0 and it’s definitely worth a read.
The original Duqu threat actor was a family of malware that most researchers believe was created by a nation-state and it’s related to the infamous Stuxnet worm. While Stuxnet was used to damage centrifuges used to enrich uranium, the original Duqu appeared more intent on surveillance and collecting information within a compromised network.