Clust-IT:Clustering-Based Intrusion Detection in IoT Environments

Low-powered and resource-constrained devices are forming a greater part of our smart networks. For this reason, they have recently been the target of various cyber-attacks. However, these devices often cannot implement traditional intrusion detection systems (IDS), or they can not produce or store the audit trails needed for inspection. Therefore, it is often necessary to adapt existing IDS systems and malware detection approaches to cope with these constraints. We explore the application of unsupervised learning techniques, specifically clustering, to develop a novel IDS for networks composed of low-powered devices. We describe our solution, called Clust-IT (Clustering of IoT), to manage heterogeneous data collected from cooperative and distributed networks of connected devices and searching these data for indicators of compromise while remaining protocol agnostic. We outline a novel application of OPTICS to various available IoT datasets, composed of both packet and flow captures, to demonstrate the capabilities of the proposed techniques and evaluate their feasibility in developing an IoT IDS

Certified PUPP: abuse in authenticode code signing

Code signing is a solution to verify the integrity of software and its publisher\u2019s identity, but it can be abused by malware and potentially unwanted programs (PUP) to look benign. This work performs a systematic analysis of Windows Authenticode code signing abuse, evaluating the effectiveness of existing defenses by certification authorities. We identify a problematic scenario in Authenticode where timestamped signed malware successfully validates even after the revocation of their code signing certificate. We propose hard revocations as a solution. We build an infrastructure that automatically analyzes potentially malicious executables, selects those signed, clusters them into operations, determines if they are PUP or malware, and produces a certificate blacklist. We use our infrastructure to evaluate 356 K samples from 2006-2015. Our analysis shows that most signed samples are PUP (88%-95%) and that malware is not commonly signed (5%\u201312%). We observe PUP rapidly increasing over time in our corpus. We measure the effectiveness of CA defenses such as identity checks and revocation, finding that 99.8% of signed PUP and 37% of signed malware use CA-issued certificates and only 17% of malware certificates and 15% of PUP certificates have been revoked. We observe most revocations lack an accurate revocation reason. We analyze the code signing infrastructure of the 10 largest PUP operations exposing that they heavily use file and certificate polymorphism and that 7 of them have multiple certificates revoked. Our infrastructure also generates a certificate blacklist 9x larger than current ones