Security patches play an important role in detecting and fixing one-day vulnerabilities. However, collecting abundant security patches from diverse data sources is not a simple task. This is because (1) each data source provides vulnerability information in a different way and (2) many security patches cannot be directly collected from Common Vulnerabilities and Exposures (CVE) information (e.g., National Vulnerability Database (NVD) references). In this paper, we propose a high-coverage approach that collects known security patches by tracking multiple data sources. Specifically, we considered the following three data sources: repositories (e.g., GitHub), issue trackers (e.g., Bugzilla), and Q&A sites (e.g., Stack Overflow). From the data sources, we gather even security patches that cannot be collected by considering only CVE information (i.e., previously untracked security patches). In our experiments, we collected 12,432 CVE patches from repositories and issue trackers, and 12,458 insecure posts from Q&A sites. We could collect at least four times more CVE patches than those collected in existing approaches, which demonstrates the efficacy of our approach. The collected security patches serves as a database on a public website (i.e., IoTcube) to proceed with the detection of vulnerable code clones.
Cryptojacking is often used by attackers as a means of gaining profits by exploiting users' resources without their consent, despite the anticipated positive effect of browser-based cryptomining. Previous approaches have attempted to detect cryptojacking websites, but they have the following limitations:(1) they failed to detect several cryptojacking websites either because of their evasion techniques or because they cannot detect JavaScript-based cryptojacking and (2) they yielded several false alarms by focusing only on limited characteristics of cryptojacking, such as counting computer resources. In this paper, we propose CIRCUIT, a precise approach for detecting cryptojacking websites. We primarily focuse on the JavaScript memory heap, which is resilient to script code obfuscation and provides information about the objects declared in the script code and their reference relations. We then extract a reference flow that can represent the script code behavior of the website from the JavaScript memory heap. Hence, CIRCUIT determines that a website is running cryptojacking if it contains a reference flow for cryptojacking. In our experiments, we found 1,813 real-world cryptojacking websites among 300K popular websites. Moreover, we provided new insights into cryptojacking by modeling the identified evasion techniques and considering the fact that characteristics of cryptojacking websites now appear on normal websites as well.
Smart home automation is part of the Internet of Things that enables house remote control via the use of smart devices, sensors, and actuators. Despite its convenience, vulnerabilities in smart home devices provide attackers with an opportunity to break into the smart home infrastructure without permission. In fact, millions of Z-Wave smart home legacy devices are vulnerable to wireless injection attacks due to the lack of encryption support and the lack of firmware updates. Worse yet, recent Z-Wave secure S2 devices with built-in encryption are also vulnerable to specific targeted attacks, i.e., attacking S2 devices is possible via vulnerable legacy devices or injecting malicious unencrypted packets that alter S2 devices normal operations. In this paper, we present ZMAD, a lightweight anomaly-based intrusion detection system (IDS) for monitoring and detecting wireless attacks on Z-Wave smart home devices. ZMAD uses a technique called packet formalization to address heterogeneous packets coming from various Z-Wave devices. ZMAD also uses a centralized learning approach to profile normal communication patterns of devices to increase Z-Wave Command Class coverage. By constructing a lightweight artificial neural network built from scratch in consideration of packet formalization and centralized learning, ZMAD can effectively detect abnormal behaviors in Z-Wave networks and runs on an external device to avoid network overhead. We applied ZMAD to an evaluation testbed constructed using 17 top-rated real-world Z-Wave smart home devices.From our experiments, we confirmed that ZMAD could effectively discover wireless injected packets with an accuracy of 98% for its artificial neural network. Our further analysis demonstrated that ZMAD is more effective than existing approaches, increasing the coverage of Z-Wave Command Classes by 663% while reducing five to 47 times the size of the trained model (23.1 KB) compared to existing deep learning architectures.
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