利用额外的CPU内核检测NOP Sleds使用沙盒执行

Nopphon Phringmongkol, P. Ratanaworabhan
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摘要

目前,基于病毒特征库的杀毒软件是解决恶意软件检测问题最常用的方法。尽管它的缺点是众所周知的——它需要大型数据库,需要不断更新,而且容易受到零日漏洞的攻击——安全社区还没有成功地提出更好的替代方案。然而,多核的出现使我们能够重新审视这个问题,并寻找在前几代硬件中被认为效率低下的替代方案。本文提出了一种轻量级的动态分析方案,该方案扫描并执行分配在主存中的对象。我们的方案寻找NOP雪橇的存在,这表明存在恶意软件。在沙盒环境中,生成或唤醒单独的线程来执行对象执行。每当应用程序在内存中分配对象时,都会发生此操作。额外的CPU内核可以独立并行地执行这些线程,从而提供接近理想的加速。我们的解决方案消除了对病毒数据库的需求,并且可以防止零日漏洞利用。结果表明,该动态分析方法开销低,假阳性率高,并能在设计上保持零假阴性。
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Exploiting Extra CPU Cores to Detect NOP Sleds Using Sandboxed Execution
At present, antivirus software backed by database of virus signatures is the most popular solution to malware detection problem. Even though its shortfalls are well-known - it requires large database that needs to be updated constantly and it is vulnerable to zero-day exploit - the security community has not successfully come up with better alternatives to it. However, the advent of multicores allows us to revisit this problem and look for alternatives that were deemed inefficient with previous generations of hardware.This paper proposes a lightweight dynamic analysis scheme that scans and executes objects allocated in the main memory. Our scheme looks for the presence of NOP sleds, which signals the existence of malware. Separate threads are spawn or woken up to perform object execution in sandboxed environment. This action takes place whenever applications allocate objects in memory. Extra CPU cores can execute these threads independently in parallel, providing close to ideal speedup. Our solution obviates the need for the virus database and can protect against zero-day exploit. We show that our dynamic analysis approach incurs low overhead, offers attractive false positive rate, and maintains zero false negative rate by design.
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