Automated detection of malware activities using nonnegative matrix factorization

C Han, J Takeuchi, T Takahashi… - 2021 IEEE 20th …, 2021 - ieeexplore.ieee.org
Malware is increasingly diversified and sophisti-cated. It is essential to rapidly and
accurately detect malware activities when malware infection spreads. However, accurately …

Darknet Analysis-Based Early Detection Framework for Malware Activity: Issue and Potential Extension

C Han, A Tanaka, T Takahashi - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Most packets arriving in the darknet (or network telescope), which is unused IP address
space on the Internet, are related to indiscriminate scanning and attack activities. In recent …

Dark-TRACER: Early Detection Framework for Malware Activity Based on Anomalous Spatiotemporal Patterns

C Han, J Takeuchi, T Takahashi, D Inoue - IEEE Access, 2022 - ieeexplore.ieee.org
As cyberattacks become increasingly prevalent globally, there is a need to identify trends in
these cyberattacks and take suitable countermeasures quickly. The darknet, an unused IP …

Real-time detection of malware activities by analyzing darknet traffic using graphical lasso

C Han, J Shimamura, T Takahashi… - 2019 18th IEEE …, 2019 - ieeexplore.ieee.org
Recent malware evolutions have rendered cyberspace less secure, and we are currently
witnessing an increasing number of severe security incidents. To minimize the impact of …

Deep in the dark-deep learning-based malware traffic detection without expert knowledge

G Marín, P Casas… - 2019 IEEE Security and …, 2019 - ieeexplore.ieee.org
With the ever-growing occurrence of networking attacks, robust network security systems are
essential to prevent and mitigate their harming effects. In recent years, machine learning …

Semi-supervised classification of malware families under extreme class imbalance via hierarchical non-negative matrix factorization with automatic model selection

ME Eren, M Bhattarai, RJ Joyce, E Raff… - ACM Transactions on …, 2023 - dl.acm.org
Identification of the family to which a malware specimen belongs is essential in
understanding the behavior of the malware and developing mitigation strategies. Solutions …

FINISH: Efficient and Scalable NMF-Based Federated Learning for Detecting Malware Activities

YW Chang, HY Chen, C Han… - … on Emerging Topics …, 2023 - ieeexplore.ieee.org
5G networks with the vast number of devices pose security threats. Manual analysis of such
extensive security data is complex. Dark-NMF can detect malware activities by monitoring …

Event detection based on nonnegative matrix factorization: Ceasefire violation, environmental, and malware events

B Drake, T Huang, A Beavers, R Du, H Park - Advances in Human Factors …, 2018 - Springer
Event detection is a very important problem across many domains and is a broadly
applicable encompassing many disciplines within engineering systems. In this paper, we …

A malware collection and analysis framework based on darknet traffic

J Song, JW Choi, SS Choi - … , ICONIP 2012, Doha, Qatar, November 12-15 …, 2012 - Springer
Since a darknet is a set of unused IP addresses (ie, no real hosts are operated with them),
we are unable to observe the network traffic on it generally. In many cases, however …

Port-piece embedding for darknet traffic features and clustering of scan attacks

S Ishikawa, S Ozawa, T Ban - … 2020, Bangkok, Thailand, November 23–27 …, 2020 - Springer
With the proliferation of Internet of Things (IoT), the damage brought by cyber-attacks
abusing the resources of malware-infected IoT devices is becoming more serious. Darknet …