Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection

• URL: http://arxiv.org/abs/2008.02327v1
• Date: Wed, 5 Aug 2020 19:29:35 GMT
• Title: Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection
• Authors: MohammadNoor Injadat, Fadi Salo, Ali Bou Nassif, Aleksander Essex, Abdallah Shami
• Abstract summary: In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
• Score: 66.05992706105224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.

Related papers

• Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection [0.0]
  We propose an Enhanced Convolutional Neural Network (EnCNN) for Network Intrusion Detection Systems (NIDS) We compare EnCNN with various machine learning algorithms, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and ensemble methods like Random Forest, AdaBoost, and Voting Ensemble. The results show that EnCNN significantly improves detection accuracy, with a notable 10% increase over state-of-art approaches.
  arXiv  Detail & Related papers  (2024-09-27T11:20:20Z)
• Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection [10.177917426690701]
  Hierarchical Federated Learning (HFL) faces the challenge of adversarial or unreliable vehicles in vehicular networks. Our study introduces a novel framework that integrates dynamic vehicle selection and robust anomaly detection mechanisms. Our proposed algorithm demonstrates remarkable resilience even under intense attack conditions.
  arXiv  Detail & Related papers  (2024-05-25T18:31:20Z)
• Performance evaluation of Machine learning algorithms for Intrusion Detection System [0.40964539027092917]
  This paper focuses on intrusion detection systems (IDSs) analysis using Machine Learning (ML) techniques. We analyze the KDD CUP-'99' intrusion detection dataset used for training and validating ML models.
  arXiv  Detail & Related papers  (2023-10-01T06:35:37Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection [2.304713283039168]
  Deep neural networks (DNN) have been successfully applied for intrusion detection problems. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyper parameters. We show that the proposed framework demonstrates significantly higher intrusion detection performance than the random search optimization-based approach.
  arXiv  Detail & Related papers  (2022-07-07T20:08:38Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Robustification of Online Graph Exploration Methods [59.50307752165016]
  We study a learning-augmented variant of the classical, notoriously hard online graph exploration problem. We propose an algorithm that naturally integrates predictions into the well-known Nearest Neighbor (NN) algorithm.
  arXiv  Detail & Related papers  (2021-12-10T10:02:31Z)
• ADASYN-Random Forest Based Intrusion Detection Model [0.0]
  Intrusion detection has been a key topic in the field of cyber security, and the common network threats nowadays have the characteristics of varieties and variation. Considering the serious imbalance of intrusion detection datasets, using ADASYN oversampling method to balance datasets was proposed. It has better performance, generalization ability and robustness compared with traditional machine learning models.
  arXiv  Detail & Related papers  (2021-05-10T12:22:36Z)
• AP-Loss for Accurate One-Stage Object Detection [49.13608882885456]
  One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously. The former suffers much from extreme foreground-background imbalance due to the large number of anchors. This paper proposes a novel framework to replace the classification task in one-stage detectors with a ranking task.
  arXiv  Detail & Related papers  (2020-08-17T13:22:01Z)

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.