Return-oriented programming (ROP) has been crucial for attackers to evade the security mechanisms of recent operating systems. Although existing ROP detection approaches mainly focus on host-based intrusion detection systems (HIDSes), network-based intrusion detection systems (NIDSes) are also desired to protect various hosts including IoT devices on the network. However, existing approaches are not enough for network-level protection due to two problems: (1) Dynamic approaches take the time with second- or minute-order on average for inspection. For applying to NIDSes, millisecond-order is required to achieve near real time detection. (2) Static approaches generate false positives because they use heuristic patterns. For applying to NIDSes, false positives should be minimized to suppress false alarms. In this paper, we propose a method for statically detecting ROP chains in malicious data by learning the target libraries (i.e., the libraries that are used for ROP gadgets). Our method accelerates its inspection by exhaustively collecting feasible ROP gadgets in the target libraries and learning them separated from the inspection step. In addition, we reduce false positives inevitable for existing static inspection by statically verifying whether a suspicious byte sequence can link properly when they are executed as a ROP chain. Experimental results showed that our method has achieved millisecond-order ROP chain detection with high precision.

Nowadays, the number of web-browser targeted attacks that lead users to adversaries' web sites and exploit web browser vulnerabilities is increasing, and a clarification of their methods and countermeasures is urgently needed. In this paper, we introduce the design and implementation of a new client honeypot for drive-by-download attacks that has the capacity to detect and investigate a variety of malicious web sites. On the basis of the problems of existing client honeypots, we enumerate the requirements of a client honeypot: 1) detection accuracy and variety, 2) collection variety, 3) performance efficiency, and 4) safety and stability. We improve our system with regard to these requirements. The key features of our developed system are stepwise detection focusing on exploit phases, multiple crawler processing, tracking of malware distribution networks, and malware infection prevention. Our evaluation of our developed system in a laboratory experiment and field experiment indicated that its detection variety and crawling performance are higher than those of existing client honeypots. In addition, our system is able to collect information for countermeasures and is secure and stable for continuous operation. We conclude that our system can investigate malicious web sites comprehensively and support countermeasures.