An Adaptive IP Hopping Approach for Moving Target Defense Using a Light-Weight CNN Detector

Abstract

Scanning attack is normally the first step of many other network attacks such as DDoS and propagation worm. Because of easy implementation and high returns, scanning attack especially cooperative scanning attack is widely used by hackers, which has become a serious threat to network security. In order to defend against scanning attack, this paper proposes an adaptive IP hopping in software defined network for moving target defense (MTD). In order to accurately respond to attacker’s behavior in real time, a light-weight convolutional neural network (CNN) detector composed of three convolutional modules and a judgment module is proposed to sense scanning attack. Input data of the detector is generated via designed packets sampling and data preprocess. The detection result of the detector is used to trigger IP hopping. In order to provide some fault tolerance for the CNN detector, IP hopping can also be triggered by a preset timer. The CNN driving adaptability is applied to a three-level hopping strategy to make the MTD system optimize its behavior according to real time attack. Experiments show that compared with existing technologies, our proposed method can significantly improve the defense effect to mitigate scanning attack and its subsequent attacks which are based on hit list. Hopping frequency of the proposed method is also lower than that of other methods, so the proposed method shows lower system overhead.