Kieran McLaughlin

Peter Maynard, Kieran McLaughlin

We describe a new class of packet injection attacks called Man-on-the-Side Attacks (MotS), previously only seen where state actors have "compromised" a number of telecommunication companies. MotS injection attacks have not been widely investigated in scientific literature, despite having been discussed by news outlets and security blogs. MotS came to attention after the Edward Snowden revelations, which described large scale pervasive monitoring of the Internet's infrastructure. For an advanced adversary attempting to interfere with IT connected systems, the next logical step is to adapt this class of attack to a smaller scale, such as enterprise or critical infrastructure networks. MotS is a weaker form of attack compared to a Man-in-the-Middle (MitM). A MotS attack allows an adversary to read and inject packets, but not modify packets sent by other hosts. This paper presents practical experiments where we have implemented and performed MotS attacks against two testbeds: 1) on HTTP connections, by redirecting a victim to a host controlled by an adversary; and 2) on an Industrial Control network, where we inject falsified command responses to the victim. In both cases, the victims accept the injected packets without generating a suspiciously large number of unusual packets on the network. We then perform an analysis of three leading Network IDS to determine whether the attacks are detected, and discuss mitigation methods.

BooJoong Kang, Suleiman Y. Yerima, Sakir Sezer et al.

Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends.