HFFPNN classifier: a hybrid approach for intrusion detection based OPSO and hybridization of feed forward neural network
- PDF / 1,702,607 Bytes
- 22 Pages / 439.37 x 666.142 pts Page_size
- 69 Downloads / 163 Views
HFFPNN classifier: a hybrid approach for intrusion detection based OPSO and hybridization of feed forward neural network (FFNN) and probabilistic neural network (PNN) T. Sree Kala 1 & A. Christy 2 Received: 3 January 2020 / Revised: 6 August 2020 / Accepted: 2 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Quick increase in web and system advancements has prompted significant increase in number of attacks and intrusions. Identification and prevention of these attacks has turned into an important part of security. Intrusion detection framework is one of the vital approaches to accomplish high security in computer systems and used to oppose attacks. Intrusion detection frameworks have reviled of dimensionality which tends to build time complexity and reduce resource use. Therefore, it is desirable that critical components of information must be examined by interruption detection framework to decrease dimensionality. These reduced features are then fed to a HFFPNN for training and testing on NSL-KDD dataset. HFFPNN is the hybridization of feed forward neural network (FFNN) and probabilistic neural network (PNN). Preprocessing of NSL-KDD dataset has been done to convert string attributes into numeric attributes before training. Comparisons with recent and relevant approaches are also tabled. Experimental results show the prominence of HFFPNN technique over the existing techniques in terms of intrusion detection classification. Therefore, the scope of this study has been expanded to encompass hybrid classifiers. Keywords Feature reduction . Feature classification . Intrusion detection . HFFPNN . Feedforward neural network (FFNN) . Probabilistic neural network (PNN) . Oppositional particle swarm optimization (OPSO)
* T. Sree Kala [email protected] A. Christy [email protected]
1
Assistant Professor, Department of Computer Science, VISTAS, Chennai, India
2
Department of Computer Science, Sathyabama University, Chennai, India
Multimedia Tools and Applications
1 Introduction Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable intrusion detection systems. An intrusion can be defined as “any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource” [11]. An Intrusion Detection System (IDS) provides an additional layer of security to network’s perimeter defence, which is usually, implemented using a firewall. The goal of IDS is to collect information from a variety of systems and network sources, and then analyze the information for signs of intrusion and misuse. IDSs are implemented in hardware, software, or a combination of both [24]. An IDS provides monitoring and analysis of user and system activity, can audit system configuration and vulnerabilities, assess the integrity of critical system and data files, provide statistical analysis of activity patterns based on the matching with known attacks, analyze abnormal activity, and operate system audit
Data Loading...
