Multi-step malware is designed to evade detection through a series of sophisticated tactics. Unlike simpler malware that can be detected by signature-based detection systems, multi-step malware employs a layered approach. Initially, it might enter a system through a benign-looking file or a trusted application. Once inside, it executes in stages, each step potentially involving different methods such as code obfuscation, encryption, and the use of legitimate processes to mask malicious activity. This step-by-step execution makes it challenging for traditional antivirus programs to detect its presence early on.
Human reverse engineers play a critical role in dissecting and understanding multi-step malware. The process begins with the identification of the initial infection vector. Engineers use tools like debuggers and disassemblers to analyze the malware’s code. They look for patterns, signatures, and behaviors that can reveal the malware’s intentions and methods. By creating a controlled environment, often referred to as a sandbox, reverse engineers can observe the malware's behavior without risking the integrity of their systems. They meticulously document each step the malware takes, noting how it propagates, communicates with command-and-control servers, and executes its payload. This labor-intensive process requires a deep understanding of programming, operating systems, and networking that is notably tough and expensive to find considering cybersecurity’s current skills gap.
Endpoint Detection and Response (EDR) systems are designed to provide real-time visibility and protection against sophisticated threats like multi-step malware. Unlike traditional antivirus software, EDR tools continuously monitor endpoints for suspicious activities. When multi-step malware attempts to execute, the EDR system can detect anomalies based on behavioral analysis. For instance, if a process that normally does not access the network suddenly starts communicating with an external server, the EDR system flags this as suspicious. These systems can also isolate affected endpoints to contain the threat, but often struggle to aid in understanding and mitigating the attack due to the overwhelming volume of alerts EDRs generate daily. Cybersecurity teams often have to rely on their own judgment to determine alert priority, which can increase the time seemingly inconspicuous multi-step malware sits on a system.
CodeHunter's automated threat hunting engine brings a new level of efficiency to malware detection and analysis. Intelligence that takes reverse malware engineers days to compile can be gathered at speed and at scale. Multi-step malware relies upon obfuscation and previously unknown code to remain undetected for as long as possible. Even when EDRs correctly identify a suspicious behavior, it often takes security researchers even more time to further investigate the warning. During this time the malware is able to execute its next level, worming its way deeper into the system and exponentially worsening the eventual damage. CodeHunter identifies all possible behaviors a file could exhibit, using machine learning rules to correctly identify and assess never-before-detected malware. Furthermore, it provides actionable insights and detailed reports, enabling security teams to respond promptly and effectively.
Learn more about how CodeHunter can empower your cybersecurity team’s talent and resources here.