Biblio

The correctness of user traffic forwarding paths is an important goal of trusted transmission. Many network security issues are related to it, i.e., denial-of-service attacks, route hijacking, etc. The current path-aware network architecture can effectively overcome this issue through path verification. At present, the main problems of path verification are high communication and high computation overhead. To this aim, this article proposes a lightweight real-time verification mechanism of intradomain forwarding paths in autonomous systems to achieve a path verification architecture with no communication overhead and low computing overhead. The problem situation is that a packet finally reaches the destination, but its forwarding path is inconsistent with the expected path. The expected path refers to the packet forwarding path determined by the interior gateway protocols. If the actual forwarding path is different from the expected one, it is regarded as an incorrect forwarding path. This article focuses on the most typical intradomain routing environment. A few routers are set as the verification routers to block the traffic with incorrect forwarding paths and raise alerts. Experiments prove that this article effectively solves the problem of path verification and the problem of high communication and computing overhead.

The rapid developments of mobile communication and wearable devices greatly improve our daily life, while the massive entities and emerging services also make Cyber-Physical System (CPS) much more complicated. The maintenance of CPS security tends to be more and more difficult. As a ”gamechanging” new active defense concept, Moving Target Defense (MTD) handle this tricky problem by periodically upsetting and recombining connections between users and servers in the protected system, which is so-called ”shuffle”. By this means, adversaries can hardly obtain enough time to compromise the potential victims, which is the indispensable condition to collect necessary information or conduct further malicious attacks. But every coin has two sides, MTD also introduce unbearable high energy consumption and resource occupation in the meantime, which hinders the large-scale application of MTD for quite a long time. In this paper, we propose a novel deep reinforcement learning-based MOTAG system called DQ-MOTAG. To our knowledge, this is the first work to provide self-adaptive shuffle period adjustment ability for MTD with reinforcement learning-based intelligent control mechanism. We also design an algorithm to generate optimal duration of next period to guide subsequent shuffle. Finally, we conduct a series of experiments to prove the availability and performance of DQ-MOTAG compared to exist methods. The result highlights our solution in terms of defense performance, error block rate and network source consumption.