The research investigates machine learning-based techniques for detecting Android malware and app-collusion. Existing methods are reviewed, detailing their strengths and limitations. For instance, while current techniques effectively identify malware, they overlook colluding Android apps and single-app malware. To improve detection accuracy and computational efficiency, the research proposes a novel approach that emphasizes a more limited set of essential app permissions and distinguishes between generic and colluding malware.
Cybersecurity Threats Remain a Problem for Older Medical Devices, House Members Say
The House Energy & Commerce Oversight and Investigations Subcommittee discussed how to protect medical devices from cyber threats amid layoffs at the FDA and other
