Outlier Dirichlet Mixture Mechanism: Adversarial Statistical Learning for Anomaly Detection in the Fog

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Outlier Dirichlet Mixture Mechanism: Adversarial Statistical Learning for Anomaly Detection in the Fog

By: 
Nour Moustafa; Kim-Kwang Raymond Choo; Ibrahim Radwan; Seyit Camtepe

Current anomaly detection systems (ADSs) apply statistical and machine learning algorithms to discover zero-day attacks, but such algorithms are vulnerable to advanced persistent threat actors. In this paper, we propose an adversarial statistical learning mechanism for anomaly detection, outlier Dirichlet mixture-based ADS (ODM-ADS), which has three new capabilities. First, it can self-adapt against data poisoning attacks that inject malicious instances in the training phase for disrupting the learning process. Second, it establishes a statistical legitimate profile and considers variations from the baseline of the profile as anomalies using a proposed outlier function. Third, to deal with dynamic and large-scale networks such as Internet of Things and cloud and fog computing, we suggest a framework for deploying the mechanism as Software as a Service in the fog nodes. The fog enables the proposed mechanism to concurrently process streaming data at the edge of the network. The ODM-ADS mechanism is evaluated using both NSL-KDD and UNSW-NB15 datasets, whose findings indicate that ODM-ADS outperforms seven other peer algorithms in terms of accuracy, detection rates, false positive rates, and computational time.

SPS on Twitter

  • Our Biomedical Imaging and Signal Processing Webinar Series continues on Tuesday, 5 July when Michael Unser present… https://t.co/7bYh8ZPHI0
  • Join us TODAY at 11:00 AM ET when the Brain Space Initiative Talk Series continues with Dr. Tianming Liu presenting… https://t.co/MEfnzk6dAE
  • Our 75th anniversary is approaching in 2023, and we're celebrating with a Special Issue of IEEE Signal Processing M… https://t.co/U6UNv8kLSO
  • The SPS Webinar Series continues on Monday, 20 June when Dr. Zhijin Qin presents "Semantic Communications: Principl… https://t.co/FhI7aP3GLi
  • CALL FOR PROPOSALS: Now seeking proposals for the 2024 IEEE International Workshop on Machine Learning for Signal P… https://t.co/Stt6OG2qo7

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar