The widespread integration of Internet of Things (IoT) and Industrial IoT (IIoT) devices in respectively home and business environments offers both benefits and perils. While these devices, such as IP cameras and network routers improve operational efficiency with their user-friendly web interfaces, they also broaden the potential for cybersecurity vulnerabilities. Recent studies highlight the vulnerability of these devices to taint-based attacks, demonstrating that even attackers with limited permissions can gain control of a device. Current state-of-the-art solutions for mitigating these risks primarily utilize Dynamic Symbolic Execution (DSE). Although effective, DSE is computationally costly and challenging for large-scale analysis. Besides, during inspection, these approaches typically exhibit over-taint behavior by producing a large number of alerts, many of which are false positives due to ineffective handling of sanitization measures that might be in place. To overcome these limitations, we introduce OctopusTaint, an innovative static-based taint analysis approach that integrates advanced data flow analysis with backtracking techniques. OctopusTaint is distinguished by its integration of a sanitization inspection module and sophisticated post-processing filters. These features are specifically designed to minimize false positives effectively while ensuring the accurate identification of genuine security threats. OctopusTaint also excels in tracking transformed tainted inputs across NVRAM, identifying new user-defined taint source functions while addressing the challenges associated with indirect calls and aliasing. Through comparative performance evaluations, OctopusTaint demonstrates superior performance over the current state-of-the-art solutions, SaTC, EmTaint, and MangoDFA. It reports genuine extra tainted sinks in considerable less time (24% faster). Furthermore, OctopusTaint identifies 82% of tainted sinks within EmTaint 's labeled dataset while exhibiting its advanced capability in sanitization inspection. It correctly flags as sanitized 320 sinks, which were misidentified as genuine alerts by EmTaint. Furthermore, OctopusTaint uncovers additional candidates overlooked by EmTaint, leveraging its enhanced detection mechanisms for new taint sources. OctopusTaint successfully identifies 142 n -day vulnerabilities previously reported by SaTC and EmTaint, in addition to discovering dozens of potential 0-day candidates.

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.

Abstract Traffic analysis is the process of extracting useful/sensitive information from observed network traffic. Typical use cases include malware detection and website fingerprinting attacks. High accuracy traffic analysis techniques use machine learning algorithms (e.g. SVM, kNN) and require to split the traffic into correctly separated blocks. Inspired by digital forensics techniques, we propose a new network traffic analysis approach based on similarity digest. The approach features several advantages compared to existing techniques, namely, fast signature generation, compact signature representation using Bloom filters, efficient similarity detection between packet traces of arbitrary sizes, and in particular dropping the traffic splitting requirement altogether. Experimental results show very promising results on VPN and malware traffic, but low results on Tor traffic due mainly to the single-size cells feature.