Realtime robust malicious traffic detection via frequency domain analysis

C Fu, Q Li, M Shen, K Xu - Proceedings of the 2021 ACM SIGSAC …, 2021 - dl.acm.org
Machine learning (ML) based malicious traffic detection is an emerging security paradigm,
particularly for zero-day attack detection, which is complementary to existing rule based …

[PDF][PDF] Anomaly Detection in the Open World: Normality Shift Detection, Explanation, and Adaptation.

D Han, Z Wang, W Chen, K Wang, R Yu, S Wang… - NDSS, 2023 - ndss-symposium.org
Concept drift is one of the most frustrating challenges for learning-based security
applications built on the closeworld assumption of identical distribution between training and …

Deep learning for vulnerability and attack detection on web applications: A systematic literature review

RL Alaoui, EH Nfaoui - Future Internet, 2022 - mdpi.com
Web applications are the best Internet-based solution to provide online web services, but
they also bring serious security challenges. Thus, enhancing web applications security …

Cognitive memory-guided autoencoder for effective intrusion detection in internet of things

H Lu, T Wang, X Xu, T Wang - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
With the development of the Internet of Things (IoT) technology, intrusion detection has
become a key technology that provides solid protection for IoT devices from network …