20 research outputs found
Anomaly Detection in LAN with ARP Request Monitoring
学位の種別: 修士University of Tokyo(東京大学
OFFER: A Motif Dimensional Framework for Network Representation Learning
Aiming at better representing multivariate relationships, this paper
investigates a motif dimensional framework for higher-order graph learning. The
graph learning effectiveness can be improved through OFFER. The proposed
framework mainly aims at accelerating and improving higher-order graph learning
results. We apply the acceleration procedure from the dimensional of network
motifs. Specifically, the refined degree for nodes and edges are conducted in
two stages: (1) employ motif degree of nodes to refine the adjacency matrix of
the network; and (2) employ motif degree of edges to refine the transition
probability matrix in the learning process. In order to assess the efficiency
of the proposed framework, four popular network representation algorithms are
modified and examined. By evaluating the performance of OFFER, both link
prediction results and clustering results demonstrate that the graph
representation learning algorithms enhanced with OFFER consistently outperform
the original algorithms with higher efficiency
Reviewing the effectiveness of artificial intelligence techniques against cyber security risks
The rapid increase in malicious cyber-criminal activities has made the field of cybersecurity a crucial research discipline. Over the areas, the advancement in information technology has enabled cybercriminals to launch increasingly sophisticated attacks that can endanger cybersecurity. Due to this, traditional cybersecurity solutions have become ineffective against emerging cyberattacks. However, the advent of Artificial Intelligence (AI) – particularly Machine Learning (ML) and Deep Learning (DL) – and cryptographic techniques have shown promising results in countering the evolving cyber threats caused by adversaries. Therefore, in this study, AI's potential in enhancing cybersecurity solutions is discussed. Additionally, the study has provided an in-depth analysis of different AI-based techniques that can detect, analyse, and prevent cyber threats. In the end, the present study has also discussed future research opportunities that are linked with the development of AI systems in the field of cybersecurity