Detection Strategy Beyond Signature-Based—The Critical Role of Behavior-Based Detection 

What Is Signature-Based Detection? 

Signature-based detection is a method used in cybersecurity to identify threats based on known attributes. In this model, specific atomic indicators such as file hashes, IP addresses, and domain names are extracted from suspected threat activity and turned into signature-based detections.  These signatures are often deployed in situations where the indicators are easily observable and able to be quickly identified as a potential match.  In many cases, these signature-based detections also form the basis for tactical mitigations, as in the case of host-based file protections, firewall rules, email-based blocks, etc. 

Limitations of Signature-Based Detection

Historically, signature-based detection has been the default standard in Threat Detection & Response. But solely relying on signature-based detection in today’s environment is no longer sufficient. The method is inherently reactive—it requires specific intelligence about a threat before creating a mechanism to detect it.  

Let’s compare signature-based detection to a bank robbery scenario. For example, if a detection system is designed to identify a robber wearing a ski mask and holding a bag of money, it might miss another robber with a baseball cap and a jacket. This analogy illustrates that by focusing on specific known attributes, the system can easily be defeated if these attributes change.  

For instance, a malware author can make minute alterations to a file, rendering the signature-based detection useless. Plus, many security platforms write rules for a wide array of environments and threats, leading to broad generalizations that might miss the finer nuances. While these detections are considered a foundational part of security operations, they’re just the beginning.  

To be truly effective, the detection methodology needs to consider changing threat parameters and context.  

What Is Behavior-Based Detection? 

Behavior-based detection focuses on sequences and relationships of actions, rather than relying on known attributes like file names or hash values. For example, when a file is executed on a system, certain calls are made at the system application layer. Behavior-based detection examines these calls, noting how the file interacts with the system, and alerting when certain parameters are observed. This method looks at the consistent, inherent behaviors that are harder to alter, making it challenging for malicious files to evade detection simply by changing their appearance or attributes. 

Understanding the Differences Between Signature and Behavior-Based Detection

While signature-based detection identifies threats based on predefined attributes or “signatures,” behavior-based detection observes how a threat behaves and interacts with their activities.  

Going back to the bank robber analogy, signature-based detection would identify a robber based on specific appearances, like a ski mask or a bag of money. So, again, if the robber changes the mask or the bag, the signature-based system might miss them. In contrast, behavior-based detection focuses on the robber’s actions or quirks—like a specific way of walking, a sequence of activities in how they approach the teller, or a suspicious pattern of conversation. Even if the robber changes outfits, these behavioral patterns remain a point of potential identification, making it difficult for threats to bypass detection.  

Behavior-based detection is proactive, understanding the underlying actions of a threat rather than its superficial attributes. 

The Importance of Behavior-Based Detection Methods in Cybersecurity Today

Given the rapid evolution and volatile nature of modern cybersecurity, relying solely on fixed signatures isn’t enough anymore. We need methods that can identify threats not just by their appearance but by their actions. Enter behavior-based detection.  

This approach zeroes in on the consistent and inherent actions a file or process takes. For instance, even if an adversary tweaks a file’s name or other easily changed attributes, its fundamental interaction with an operating system remains consistent, allowing behavior-based detections to identify it. 

Adopting behavior-based detection methods is especially crucial in today’s cloud-driven environments. Unlike traditional fixed-asset setups, modern enterprises now lean heavily on cloud services, which often lack physical file interactions. In these cloud-rich settings, threats often revolve around subverting tokens, bypassing two-factor authentication, or accessing cloud resources with compromised accounts. Here, behavioral patterns become the primary red flags, making them indispensable for effective threat detection. 

The fluidity of in-memory attacks is another reason behavioral-based techniques are necessary. Since such attacks leave minimal artifacts on systems, the focus shifts from searching for obvious traces to discerning suspicious behaviors. By harnessing threat intelligence and understanding the intricacies of attacker methodologies, behavior-based detection equips cybersecurity professionals to preemptively identify new threats, even before they become mainstream. 

Benefits of Behavior-Based Detection in Identifying and Mitigating Threats 

Behavior-based detection is revolutionizing the way we understand and combat cybersecurity threats. Instead of relying solely on predefined signatures that may easily be bypassed by merely renaming a file, behavior-based detection delves deeper. This distinction brings a lot of benefits to the table. 

Take the example of Mimikatz, a widely recognized tool often used in cyber attacks. While signature-based methods may try to identify the tool by its filename, behavior-based approaches scrutinize how Mimikatz operates at its core. Regardless of file name changes, the behavior-based method zeroes in on the file’s interactions, like its direct calls to system functions essential for its malicious operation. This approach not only makes it incredibly challenging for attackers to skirt detection but also ensures defenses remain robust against even slight alterations in attack methods. 

Behavior-based detection is also extremely useful when it comes to zero-day exploits. These threats capitalize on vulnerabilities that are not yet known to software vendors or the public, making them particularly menacing. While it’s true that identifying a specific threat as a zero-day requires in-depth analysis, behavior-based detection methods can recognize the suspicious patterns associated with such exploits. For instance, an exploit might target an external application, intending to dump credentials from an operating system. Even if the exploit is novel, its behavior of accessing specific system processes remains a red flag for behavior-based detection systems. 

Coupled with threat intelligence and comprehensive analysis, behavior-based detection offers organizations the confidence to identify, understand, and tackle not just the threats of today but also anticipate those of tomorrow. By focusing on the behavior, we shift our defenses from being perpetually reactive to strategically proactive, ensuring we stay one step ahead in the ever-evolving cybersecurity landscape. 

The Roles of Artificial Intelligence and Machine Learning in Behavior-Based Detection

The potential of Artificial Intelligence (AI) and Machine Learning (ML) often spark excitement and speculation in the cybersecurity world. When it comes to behavior-based detection, the journey is still unfolding. 

Currently, AI excels in tasks with known variables, like signature-based detections. In such cases, AI can process large volumes of data rapidly and identify patterns that match known malicious signatures. But when we venture into the nuanced world of behavioral-based detections, the terrain gets more intricate. Such detections involve discerning subtle deviations in normal behavior, often requiring an analytical rigor that, as of now, seems uniquely human. 

As we’ve established, the main benefit of behavior-based detection lies in its ability to evolve with ever-changing threat landscapes. It doesn’t just look for predefined patterns; it seeks out anomalies, irregularities, and unexpected actions. AI, despite its prowess, still requires a foundation…models to be trained on, in other words. These models need continuous updates and adjustments based on emerging threat intelligence. 

While AI and ML have certainly made strides in cybersecurity, we can’t remove the human touch entirely, especially in areas demanding intricate analysis. Human experts bring contextual understanding, adaptability, and an instinctual sense of when something ‘just doesn’t feel right’. 

How to Effectively Implement Behavior-Based Detection

Here’s how you can effectively implement behavior-based detection in your organization: 

Stay updated with threat intelligence.

• Start by consistently monitoring threat intelligence. Whenever your team discovers a new technique or an unfamiliar threat actor activity, use this intelligence to inform your detection strategy. 

Gather and refine requirements.

• After obtaining intelligence, your detection engineering team should dissect the attack’s methodology. This deep dive enables them to understand the core behaviors they should detect. The primary goal is to identify new detections based on behavioral activity. 

Development and testing.

• Once the attack is understood, write the code for detecting such behaviors. Before deploying this detection logic, test it in a controlled environment. Ensure it identifies precisely what it should while minimizing false positives. 

Balance precision with breadth.

• Behavioral detections inherently cast a wide net. It’s vital that these detections are precise to avoid overwhelming your Security Operations Center (SOC) with false alarms. While you want comprehensive coverage, you also need to ensure that the alerts are meaningful. 

Deploy and monitor.

• After rigorous testing, deploy the detection to your live environment. But the work doesn’t stop there. Maintain a feedback loop with your SOC to gauge the effectiveness of the detection. If there’s a high volume of false positives, loop back, adjust, and fine-tune the detection. 

Adapt to risk tolerance.

• Understand that implementing behavior-based detection is a balancing act. Depending on your organization’s risk tolerance, you might cast a wider or narrower net. Some organizations, due to low risk tolerance, will choose to investigate every single alert, even if it means sifting through many false positives. Others might decide to focus only on the most severe threats to optimize their resources. 

Re-evaluate continuously.

• The threat landscape is ever evolving. As such, your behavior-based detection strategy should also be dynamic. Regularly assess whether your strategy is too stringent or too relaxed. Aim for a balanced approach but be ready to pivot based on the changing threat environment and organizational priorities.