Network Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Na\"ive Bayes Algorithm

• URL: http://arxiv.org/abs/2105.13698v1
• Date: Fri, 28 May 2021 09:44:14 GMT
• Title: Network Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Na\"ive Bayes Algorithm
• Authors: Fan Huang
• Abstract summary: The application of machine learning methods based on supervised classification technology would help liberate the network security staff from the heavy and boring tasks. A finetuned model would accurately recognize user behavior, which could provide persistent monitoring with a relative high accuracy and good adaptability.
• Score: 1.6181085766811525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network activities recognition has always been a significant component of intrusion detection. However, with the increasing network traffic flow and complexity of network behavior, it is becoming more and more difficult to identify the specific behavior quickly and accurately by user network monitoring software. It also requires the system security staff to pay close attention to the latest intrusion monitoring technology and methods. All of these greatly increase the difficulty and complexity of intrusion detection tasks. The application of machine learning methods based on supervised classification technology would help to liberate the network security staff from the heavy and boring tasks. A finetuned model would accurately recognize user behavior, which could provide persistent monitoring with a relative high accuracy and good adaptability. Finally, the results of network activities recognition by J48 and Na\"ive Bayes algorithms are introduced and evaluated.

Related papers

• Deep Learning Algorithms Used in Intrusion Detection Systems -- A Review [0.0]
  This review paper studies recent advancements in the application of deep learning techniques, including CNN, Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), autoencoders (AE), Multi-Layer Perceptrons (MLP), Self-Normalizing Networks (SNN) and hybrid models, within network intrusion detection systems.
  arXiv  Detail & Related papers  (2024-02-26T20:57:35Z)
• A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection [4.718295605140562]
  We propose a three-stage deep learning anomaly detection based network intrusion attack detection framework. The framework comprises an integration of unsupervised (K-means clustering), semi-supervised (GANomaly) and supervised learning (CNN) algorithms. We then evaluated and showed the performance of our implemented framework on three benchmark datasets.
  arXiv  Detail & Related papers  (2022-12-02T04:40:54Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks [0.0]
  Intrusion detection is one of the challenging issues in the area of network security. In this paper, a comparative analysis of conventional machine learning classification algorithms has been performed. It can be observed that Multi-Layer Perception (MLP) has dependencies between input and output and relies more on network configuration for intrusion detection.
  arXiv  Detail & Related papers  (2021-11-02T05:58:07Z)
• Few-shot Network Anomaly Detection via Cross-network Meta-learning [45.8111239825361]
  We propose a new family of graph neural networks -- Graph Deviation Networks (GDN) GDN can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network. We equip the proposed GDN with a new cross-network meta-learning algorithm to realize few-shot network anomaly detection.
  arXiv  Detail & Related papers  (2021-02-22T16:42:37Z)
• Darknet Traffic Big-Data Analysis and Network Management to Real-Time Automating the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework [0.0]
  We propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process. It is an effective and accurate computational intelligent tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real-time.
  arXiv  Detail & Related papers  (2021-02-16T19:03:25Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  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.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning [40.989393438716476]
  Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs are often enough to change network-based decisions, which was recently shown to cause an autonomous vehicle to swerve into another lane. This work leverages research on certified adversarial robustness to develop an online certifiably robust for deep reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2020-04-11T21:36:13Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)
• Pelican: A Deep Residual Network for Network Intrusion Detection [7.562843347215287]
  We propose a deep neural network, Pelican, that is built upon specially-designed residual blocks. Pelican can achieve a high attack detection performance while keeping a much low false alarm rate.
  arXiv  Detail & Related papers  (2020-01-19T05:07:48Z)

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.