Zobrazeno 1 - 10
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pro vyhledávání: '"Yang, Shanchieh Jay"'
In cybersecurity, security analysts face the challenge of mitigating newly discovered vulnerabilities in real-time, with over 300,000 Common Vulnerabilities and Exposures (CVEs) identified since 1999. The sheer volume of known vulnerabilities complic
Externí odkaz:
http://arxiv.org/abs/2410.17406
Recently, large language models (LLMs) have expanded into various domains. However, there remains a need to evaluate how these models perform when prompted with commonplace queries compared to domain-specific queries, which may be useful for benchmar
Externí odkaz:
http://arxiv.org/abs/2407.11006
Tactics, Techniques, and Procedures (TTPs) outline the methods attackers use to exploit vulnerabilities. The interpretation of TTPs in the MITRE ATT&CK framework can be challenging for cybersecurity practitioners due to presumed expertise and complex
Externí odkaz:
http://arxiv.org/abs/2401.00280
Autor:
Corsini, Andrea, Yang, Shanchieh Jay
Machine learning (ML) has become increasingly popular in network intrusion detection. However, ML-based solutions always respond regardless of whether the input data reflects known patterns, a common issue across safety-critical applications. While s
Externí odkaz:
http://arxiv.org/abs/2308.14376
Autor:
Fayyazi, Reza, Yang, Shanchieh Jay
The volume, variety, and velocity of change in vulnerabilities and exploits have made incident threat analysis challenging with human expertise and experience along. Tactics, Techniques, and Procedures (TTPs) are to describe how and why attackers exp
Externí odkaz:
http://arxiv.org/abs/2306.14062
Autor:
Moskal, Stephen, Yang, Shanchieh Jay
With growing sophistication and volume of cyber attacks combined with complex network structures, it is becoming extremely difficult for security analysts to corroborate evidences to identify multistage campaigns on their network. This work develops
Externí odkaz:
http://arxiv.org/abs/2212.13941
Attack graphs (AG) are used to assess pathways availed by cyber adversaries to penetrate a network. State-of-the-art approaches for AG generation focus mostly on deriving dependencies between system vulnerabilities based on network scans and expert k
Externí odkaz:
http://arxiv.org/abs/2107.02783
Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequential learning models, due to their ability to extract the temporal cha
Externí odkaz:
http://arxiv.org/abs/2106.07961
Autor:
Yang, Shanchieh Jay, Okutan, Ahmet, Werner, Gordon, Su, Shao-Hsuan, Goel, Ayush, Cahill, Nathan D.
Critical and sophisticated cyberattacks often take multitudes of reconnaissance, exploitations, and obfuscation techniques to penetrate through well protected enterprise networks. The discovery and detection of attacks, though needing continuous effo
Externí odkaz:
http://arxiv.org/abs/2103.13902
Autor:
Moskal, Stephen, Yang, Shanchieh Jay
The techniques and tactics used by cyber adversaries are becoming more sophisticated, ironically, as defense getting stronger and the cost of a breach continuing to rise. Understanding the thought processes and behaviors of adversaries is extremely c
Externí odkaz:
http://arxiv.org/abs/2002.07838