Zobrazeno 1 - 10
of 123
pro vyhledávání: '"JANG-JACCARD, JULIAN"'
Identifying technological convergence among emerging technologies in cybersecurity is crucial for advancing science and fostering innovation. Unlike previous studies focusing on the binary relationship between a paper and the concept it attributes to
Externí odkaz:
http://arxiv.org/abs/2403.01601
DDoS attacks involve overwhelming a target system with a large number of requests or traffic from multiple sources, disrupting the normal traffic of a targeted server, service, or network. Distinguishing between legitimate traffic and malicious traff
Externí odkaz:
http://arxiv.org/abs/2306.17190
Autor:
Wei, Yuanyuan, Jang-Jaccard, Julian, Sabrina, Fariza, Xu, Wen, Camtepe, Seyit, Dunmore, Aeryn
A Distributed Denial-of-service (DDoS) attack is a malicious attempt to disrupt the regular traffic of a targeted server, service, or network by sending a flood of traffic to overwhelm the target or its surrounding infrastructure. As technology impro
Externí odkaz:
http://arxiv.org/abs/2305.09475
Since their proposal in the 2014 paper by Ian Goodfellow, there has been an explosion of research into the area of Generative Adversarial Networks. While they have been utilised in many fields, the realm of malware research is a problem space in whic
Externí odkaz:
http://arxiv.org/abs/2302.08558
Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the perf
Externí odkaz:
http://arxiv.org/abs/2208.09711
To enhance the efficiency of incident response triage operations, it is not cost-effective to defend all systems equally in a complex cyber environment. Instead, prioritizing the defense of critical functionality and the most vulnerable systems is de
Externí odkaz:
http://arxiv.org/abs/2207.10242
Autor:
Wei, Yuanyuan, Jang-Jaccard, Julian, Xu, Wen, Sabrina, Fariza, Camtepe, Seyit, Boulic, Mikael
Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, traditional statistics and shallow machine learning-based approaches in ano
Externí odkaz:
http://arxiv.org/abs/2204.06701
Autor:
Singh, Amardeep, Jang-Jaccard, Julian
The massive growth of network traffic data leads to a large volume of datasets. Labeling these datasets for identifying intrusion attacks is very laborious and error-prone. Furthermore, network traffic data have complex time-varying non-linear relati
Externí odkaz:
http://arxiv.org/abs/2204.03779
Autor:
Yin, Yuhua, Jang-Jaccard, Julian, Xu, Wen, Singh, Amardeep, Zhu, Jinting, Sabrina, Fariza, Kwak, Jin
The effectiveness of machine learning models is significantly affected by the size of the dataset and the quality of features as redundant and irrelevant features can radically degrade the performance. This paper proposes IGRF-RFE: a hybrid feature s
Externí odkaz:
http://arxiv.org/abs/2203.16365