CVE-2026-5281

CVE-2026-5281 2026/06/27：This vulnerability has been publicly disclosed. Although the patch points differ, the overall approach and the triggering path are completely identical to those in my PoC. Chromium commit commit e00a64ead1abef9447943efede7bc26362ac3797 HEAD - 146.0.7680.71, tag:...

CVE-2026-17052: Out-of-bounds Write

The Time-aware GPIO syscall verification handler zvrfytgpiopinreadtsec in drivers/timeawaregpio/timeawaregpiohandlers.c validated only the port device object and passed the caller-supplied timestamp and eventcount output pointers to the driver without a KSYSCALLMEMORYWRITE check. The other handle...

A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation

The proliferation of Internet of Things IoT devices has significantly expanded attack surfaces, making IoT ecosystems particularly susceptible to sophisticated cyber threats. To address this challenge, this work introduces A-THENA, a lightweight early intrusion detection system EIDS that...

A Secured Intent-Based Networking (SIBN) with Data-Driven Time-Aware Intrusion Detection

While Intent-Based Networking IBN promises operational efficiency through autonomous and abstraction-driven network management, a critical unaddressed issue lies in IBN's implicit trust in the integrity of intent ingested by the network. This inherent assumption of data reliability creates a blin...

Temporal Analysis Framework for Intrusion Detection Systems: A Novel Taxonomy for Time-Aware Cybersecurity

Most intrusion detection systems still identify attacks only after significant damage has occurred, detecting late-stage tactics rather than early indicators of compromise. This paper introduces a temporal analysis framework and taxonomy for time-aware network intrusion detection. Through a...

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift

Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation...