Zobrazeno 1 - 10
of 200
pro vyhledávání: '"Camtepe, Seyit"'
MTDSense: AI-Based Fingerprinting of Moving Target Defense Techniques in Software-Defined Networking
Moving target defenses (MTD) are proactive security techniques that enhance network security by confusing the attacker and limiting their attack window. MTDs have been shown to have significant benefits when evaluated against traditional network atta
Externí odkaz:
http://arxiv.org/abs/2408.03758
Considering the ever-evolving threat landscape and rapid changes in software development, we propose a risk assessment framework SRiQT (Software Risk Quantification through Trust). This framework is based on the necessity of a dynamic, data-driven, a
Externí odkaz:
http://arxiv.org/abs/2408.02876
Large machine-learning training datasets can be distilled into small collections of informative synthetic data samples. These synthetic sets support efficient model learning and reduce the communication cost of data sharing. Thus, high-fidelity disti
Externí odkaz:
http://arxiv.org/abs/2408.02266
Autor:
Liu, Shigang, Cao, Di, Kim, Junae, Abraham, Tamas, Montague, Paul, Camtepe, Seyit, Zhang, Jun, Xiang, Yang
Recently, deep learning has demonstrated promising results in enhancing the accuracy of vulnerability detection and identifying vulnerabilities in software. However, these techniques are still vulnerable to attacks. Adversarial examples can exploit v
Externí odkaz:
http://arxiv.org/abs/2407.19216
This paper addresses a significant gap in Autonomous Cyber Operations (ACO) literature: the absence of effective edge-blocking ACO strategies in dynamic, real-world networks. It specifically targets the cybersecurity vulnerabilities of organizational
Externí odkaz:
http://arxiv.org/abs/2406.19596
Autor:
Shao, Wei, Zhu, Rongyi, Yang, Cai, Thapa, Chandra, Ahmed, Muhammad Ejaz, Camtepe, Seyit, Zhang, Rui, Kim, DuYong, Menouar, Hamid, Salim, Flora D.
Spatiotemporal data is prevalent in a wide range of edge devices, such as those used in personal communication and financial transactions. Recent advancements have sparked a growing interest in integrating spatiotemporal analysis with large-scale lan
Externí odkaz:
http://arxiv.org/abs/2406.03404
AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and re
Externí odkaz:
http://arxiv.org/abs/2405.15258
The exploitation of publicly accessible data has led to escalating concerns regarding data privacy and intellectual property (IP) breaches in the age of artificial intelligence. As a strategy to safeguard both data privacy and IP-related domain knowl
Externí odkaz:
http://arxiv.org/abs/2405.03316
Autor:
Doan, Bao Gia, Nguyen, Dang Quang, Montague, Paul, Abraham, Tamas, De Vel, Olivier, Camtepe, Seyit, Kanhere, Salil S., Abbasnejad, Ehsan, Ranasinghe, Damith C.
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at t
Externí odkaz:
http://arxiv.org/abs/2403.18309
Autor:
Kish, Sebastian P., Thapa, Chandra, Sayat, Mikhael, Suzuki, Hajime, Pieprzyk, Josef, Camtepe, Seyit
Publikováno v:
Phys. Rev. Research 6, 023301 (2024)
Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying new attack vectors and countermeasures for CV-QKD implementations is
Externí odkaz:
http://arxiv.org/abs/2401.15898