scholarly journals INTRUSION DETECTION IN COMPUTER NETWORKS USING LATENT SPACE REPRESENTATION AND MACHINE LEARNING

2020 ◽  
pp. 442-448
Author(s):  
Vladyslav Hamolia ◽  
Viktor Melnyk ◽  
Pavlo Zhezhnych ◽  
Anna Shilinh
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Feature Space ◽  
Space Representation ◽  
History Of Research ◽  
Latent Space ◽  
History Of

Anomaly detection (AD) identifies samples that are not related to the overall distribution in the feature space. This problem has a long history of research through diverse methods, including statistical and modern Deep Neural Networks (DNN) methods. Non-trivial tasks such as covering ambiguous user actions and the complexity of standard algorithms challenged researchers. This article discusses the results of introducing an intrusion detection system using a machine learning (ML) approach. We compared these results with the characteristics of the most common existing rule-based Snort system. Signature Based Intrusion Detection System (SBIDS) has critical limitations well observed in a large number of previous studies. The crucial disadvantage is the limited variety of the same attack type due to the predetermination of all the rules. DNN solves this problem with long short-term memory (LSTM). However, requiring the amount of data and resources for training, this solution is not suitable for a real-world system. This necessitated a compromise solution based on DNN and latent space techniques.

A MACHINE LEARNING BASED IOT FOR PROVIDING AN INTRUSION DETECTION SYSTEM FOR SECURITY

2021 ◽  
pp. 103741
Author(s):  
Dhanke Jyoti Atul ◽  
Dr. R. Kamalraj ◽  
Dr. G. Ramesh ◽  
K. Sakthidasan Sankaran ◽  
Sudhir Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System

An intelligent flow-based and signature-based IDS for SDNs using ensemble feature selection and a multi-layer machine learning-based classifier

2020 ◽  
pp. 1-20
Author(s):  
K. Muthamil Sudar ◽  
P. Deepalakshmi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Network Architecture ◽  
Detection System ◽  
Software Defined Networking ◽  
Control Plane ◽  
Control Logic ◽  
Data Plane

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

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