Generative Threat Variants for Detection Tools Assessment

Advanced persistent threats (APTs) and ransomware attacks are sophisticated cyberattacks orchestrated by skilled threat actors, capable of persisting undetected within a victim’s network for extended periods. These attacks pose significant challenges to network defense, often resulting in substantial financial and reputational losses for targeted enterprises. Typically involving multiple stages, these attacks leverage highly sophisticated techniques and stealth to effectively impact their targets. Enterprises targeted by such attacks continually strengthen their defenses against these critical cyber threats. Common solutions like antivirus software, extended detection and response (XDR), and security information and event management (SIEM) systems are dedicated to detecting and responding to potential threats. Through early threat identification, effective response strategies, and mitigation efforts, enterprises aim to minimize damage to their networks and sensitive data. However, hackers have become adept at generating threat variants to evade detection through indicators of compromise (IOCs) or indicators of behavior (IOBs)
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. For instance, software packers and obfuscators can alter a file’s hash value, circumventing IOC-based detection methods. Additionally, hackers may disguise themselves as legitimate browser applications, utilizing domain name system (DNS) or hyper text transfer protocol secure (HTTPS) protocols for command and control (C&C) communication. These evasion techniques often render detection tools ineffective in lab environments, leading to a barrage of false alarms and alert fatigue. Furthermore, investigations by Microsoft and Symantec reveal that hackers can operate undetected for over six months in most APT incidents. This discrepancy underscores the misalignment between existing detection assessment methods and the complex realities of real-world attacks, highlighting a significant gap in current detection capabilities
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.