Lucene search
+L

5 matches found

Kitploit
Kitploit
added 2026/08/25 9:56 p.m.2 views

CVE-2026-5281

CVE-2026-5281 2026/06/27:Questa vulnerabilità è stata divulgata pubblicamente. Sebbene i punti di patch differiscano, l'approccio generale e il percorso di attivazione sono completamente identici a quelli del mio PoC. Commit di Chromium commit e00a64ead1abef9447943efede7bc26362ac3797 HEAD -...

8.8CVSS7AI score0.04938EPSS
SaveExploits0
Packet Storm News
Packet Storm News
added 2026/04/23 12:00 a.m.11 views

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...

5.3AI score
SaveExploits0
Packet Storm News
Packet Storm News
added 2025/11/07 12:00 a.m.11 views

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...

6.8AI score
SaveExploits0
Packet Storm News
Packet Storm News
added 2025/11/05 12:00 a.m.11 views

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...

6.8AI score
SaveExploits0
Packet Storm News
Packet Storm News
added 2025/08/26 12:00 a.m.8 views

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...

6.8AI score
SaveExploits0
Rows per page
Query Builder