Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

• URL: http://arxiv.org/abs/2210.02841v1
• Date: Sat, 1 Oct 2022 20:44:14 GMT
• Title: Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback
• Authors: Gopikrishna Rathinavel, Nikhil Muralidhar, Timothy O'Shea and Naren Ramakrishnan
• Abstract summary: CAAD employs contrastive learning in an adversarial setup to learn effective representations of normal and anomalous behavior in wireless networks. We conduct rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques and verify that CAAD yields a mean performance improvement of 92.84%.
• Score: 14.188603782159372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. To this end, we propose a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs contrastive learning in an adversarial setup to learn effective representations of normal and anomalous behavior in wireless networks. We conduct rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques and verify that CAAD yields a mean performance improvement of 92.84%. Additionally, we also augment CAAD enabling it to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). We view CAAD-EF as a novel, holistic and widely applicable solution to anomaly detection.

Related papers

• Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
  We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features. RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:56:22Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• RAPID: Robust APT Detection and Investigation Using Context-Aware Deep Learning [26.083244046813512]
  We introduce a novel deep learning-based method for robust APT detection and investigation. By utilizing self-supervised sequence learning and iteratively learned embeddings, our approach effectively adapts to dynamic system behavior. Our evaluation demonstrates RAPID's effectiveness and computational efficiency in real-world scenarios.
  arXiv  Detail & Related papers  (2024-06-08T05:39:24Z)
• Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks [3.489779105594534]
  backdoors can be exploited by malicious actors on deep neural networks (DNNs) and cloud services for data processing. Our approach leverages advanced tensor decomposition algorithms to meticulously analyze the weights of pre-trained DNNs and distinguish between backdoored and clean models. This advancement enhances the security of deep learning and AI in networked systems, providing essential cybersecurity against evolving threats in emerging technologies.
  arXiv  Detail & Related papers  (2024-03-13T03:10:11Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• An Explainable Ensemble-based Intrusion Detection System for Software-Defined Vehicle Ad-hoc Networks [0.0]
  In this study, we explore the detection of cyber threats in vehicle networks through ensemble-based machine learning. We propose a model that uses Random Forest and CatBoost as our main investigators, with Logistic Regression used to then reason on their outputs to make a final decision. We observe that our approach improves classification accuracy, and results in fewer misclassifications compared to previous works.
  arXiv  Detail & Related papers  (2023-12-08T10:39:18Z)
• A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN [5.381374943525773]
  Defect detection is crucial for ensuring the trustworthiness of railway systems. Current approaches rely on single deep-learning models, like CNNs. We propose a reusable AI-enabled defect detection approach.
  arXiv  Detail & Related papers  (2023-11-24T19:45:55Z)
• AUTO: Adaptive Outlier Optimization for Online Test-Time OOD Detection [81.49353397201887]
  Out-of-distribution (OOD) detection is crucial to deploying machine learning models in open-world applications. We introduce a novel paradigm called test-time OOD detection, which utilizes unlabeled online data directly at test time to improve OOD detection performance. We propose adaptive outlier optimization (AUTO), which consists of an in-out-aware filter, an ID memory bank, and a semantically-consistent objective.
  arXiv  Detail & Related papers  (2023-03-22T02:28:54Z)
• Incremental Embedding Learning via Zero-Shot Translation [65.94349068508863]
  Current state-of-the-art incremental learning methods tackle catastrophic forgetting problem in traditional classification networks. We propose a novel class-incremental method for embedding network, named as zero-shot translation class-incremental method (ZSTCI) In addition, ZSTCI can easily be combined with existing regularization-based incremental learning methods to further improve performance of embedding networks.
  arXiv  Detail & Related papers  (2020-12-31T08:21:37Z)
• An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies [2.2628381865476115]
  We present an Isolation Forest Learning-Based Outlier Detection Model for effectively classifying cyber anomalies. Experimental results show that the classification accuracy of cyber anomalies has been improved after removing outliers.
  arXiv  Detail & Related papers  (2020-12-09T05:09:52Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.