Binary function clone search is an essential capability that enables multiple applications and use cases, including reverse engineering, patch security inspection, threat analysis, vulnerable function detection, etc. As such, a surge of interest has been expressed in designing and implementing techniques to address function similarity on binary executables and firmware images. Although existing approaches have merit in fingerprinting function clones, they present limitations when the target binary code has been subjected to significant code transformation resulting from obfuscation, compiler optimization, and/or cross-compilation to multiple-CPU architectures. In this regard, we design and implement a system named BinFinder, which employs a neural network to learn binary function embeddings based on a set of extracted features that are resilient to both code obfuscation and compiler optimization techniques. Our experimental evaluation indicates that BinFinder outperforms state-of-the-art approaches for multi-CPU architectures by a large margin, with 46% higher Recall against Gemini, 55% higher Recall against SAFE, and 28% higher Recall against GMN. With respect to obfuscation and compiler optimization clone search approaches, BinFinder outperforms the asm2vec (single CPU architecture approach) with higher Recall and BinMatch (multi-CPU architecture approach) with higher Recall. Finally, our work is the first to provide noteworthy results with respect to binary clone search over the tigress obfuscator, which is a well-established open-source obfuscator.

According to International Electrotechnical Commission (IEC) 61850-90-4, most smart grid substations are designed with redundancy in order to improve their availability in case of failures. Redundancy usually takes the form of having multiple subsystems with identical functionality based on the assumption that failures in one subsystem are isolated from other subsystems. However, this is not necessarily true in the case of failures caused by malicious attacks, because attackers can easily reuse their skills and tools across different subsystems under similar configurations. Taking this into consideration, this article introduces the factor of security (FoS) metrics to quantify the security effectiveness of redundant subsystems in smart grids. Specifically, we first apply the attack graph model to capture various threats in smart grids and substations; we then formally define the FoS metric and the probabilistic FoS metric, and finally we evaluate those metrics through simulations.

Industrial Control Systems (ICSs) are cyber-physical systems that offer attractive targets to threat actors due to the scale of damages, both physical and cyber, that successful exploitation can cause. As such, ICSs often find themselves victims to reconnaissance campaigns - coordinated scanning activity that targets a wide subset of the Internet - that aim to discover vulnerable systems. As these campaigns likely scan broad netblocks of the Internet, some traffic is directed to network telescopes, which are routable, allocated, and unused IP space. In this paper, we explore the threat landscape of ICS devices by analyzing and investigating network telescope traffic. Our network traffic analysis tool takes darknet traffic and generates threat intelligence on scanning campaigns targeting ICSs in the form of campaign fragments, which we leverage in new ways to get more in-depth knowledge of the cybersecurity threats. We investigate the payloads of the identified campaigns using a custom Deep Packet Inspection (DPI) technique to dissect and analyze the packets. We found 13 distinct payload templates and deduced their purpose, and by extension the campaign goals. We use machine learning to classify the sources behind the campaigns and identify threat actors such as botnets, malicious attackers, or researchers, and establish a methodology to rank our campaigns to prioritize our analysis. To conduct our analysis of the threats targeting ICSs, we have leveraged 12.85 TB (330 days) of network traffic received by our observed darknet IP space. Combining these investigative threads, we provide a thorough overview of the threat landscape targeting ICS systems.