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Self-addressable memory-based FSM: a scalable intrusion detection engine

机译:基于内存的自寻址 FSM:可扩展的入侵检测引擎

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摘要

One way to detect and thwart a network attack is to compare each incoming packet with predefined patterns, also called an attack pattern database, and raise an alert upon detecting a match. This article presents a novel pattern-matching engine that exploits a memory-based, programmable state machine to achieve deterministic processing rates that are independent of packet and pattern characteristics. Our engine is a self-addressable memory-based finite state machine (SAMFSM), whose current state coding exhibits all its possible next states. Moreover, it is fully reconfigurable in that new attack patterns can be updated easily. A methodology was developed to program the memory and logic. Specifically, we merge ??non-equivalent?? states by introducing ??super characters?? on their inputs to further enhance memory efficiency without adding labels. SAM-FSM is one of the most storage-efficient machines and reduces the memory requirement by 60 times. Experimental results are presented to demonstrate the validity of SAM-FSM.
机译:检测和阻止网络攻击的一种方法是将每个传入数据包与预定义的模式(也称为攻击模式数据库)进行比较,并在检测到匹配项时发出警报。本文介绍了一种新颖的模式匹配引擎,该引擎利用基于内存的可编程状态机来实现独立于数据包和模式特征的确定性处理速率。我们的引擎是一个基于内存的自寻址有限状态机(SAMFSM),其当前状态编码展示了其所有可能的下一个状态。此外,它是完全可重新配置的,因为可以轻松更新新的攻击模式。开发了一种对存储器和逻辑进行编程的方法。具体来说,我们合并??不等价??通过引入 ??超级角色??在他们的输入上进一步提高内存效率,而无需添加标签。SAM-FSM 是存储效率最高的机器之一,可将内存需求降低 60 倍。实验结果验证了SAM-FSM的有效性。

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