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
- Multi-agent Reinforcement Learning-based Network Intrusion Detection System [3.4636217357968904]
Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks.
We propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection.
Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns.
arXiv Detail & Related papers (2024-07-08T09:18:59Z) - 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) - 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) - Exploring Robustness of Unsupervised Domain Adaptation in Semantic
Segmentation [74.05906222376608]
We propose adversarial self-supervision UDA (or ASSUDA) that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space.
This paper is rooted in two observations: (i) the robustness of UDA methods in semantic segmentation remains unexplored, which pose a security concern in this field; and (ii) although commonly used self-supervision (e.g., rotation and jigsaw) benefits image tasks such as classification and recognition, they fail to provide the critical supervision signals that could learn discriminative representation for segmentation tasks.
arXiv Detail & Related papers (2021-05-23T01:50:44Z) - 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) - Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness [97.67477497115163]
We use mode connectivity to study the adversarial robustness of deep neural networks.
Our experiments cover various types of adversarial attacks applied to different network architectures and datasets.
Our results suggest that mode connectivity offers a holistic tool and practical means for evaluating and improving adversarial robustness.
arXiv Detail & Related papers (2020-04-30T19:12:50Z) - 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.