Non-linear Dimensionality Reduction-based Intrusion Detection using Deep Autoencoder

Abstract

The intrusion detection has become core part of any network of computers due to increasing amount of digital content available. In parallel, the data breaches and malware attacks have also grown in large numbers which makes the role of intrusion detection more essential. Even though many existing techniques are successfully used for detecting intruders but new variants of malware and attacks are being released every day. To counterfeit these new types of attacks, intrusion detection must be designed with state of art techniques such as Deep learning. At present the Deep learning techniques have a strong role in Natural Language Processing, Computer Vision and Speech Processing. This paper is focused on reviewing the role of deep learning techniques for intrusion detection and proposing an efficient deep Auto Encoder (AE) based intrusion detection technique. The intrusion detection is implemented in two stages with a binary classifier and multiclass classification algorithm (dense neural network). The performance of the proposed approach is presented and compared with parallel methods used for intrusion detection. The reconstruction error of the AE model is compared with the PCA and the performance of both anomaly detection and the multiclass classification is analyzed using metrics such as accuracy and false alarm rate. The compressed representation of the AE model helps to lessen the false alarm rate of both anomaly detection and attack classification using SVM and dense NN model respectively.

Authors and Affiliations

S. Sreenivasa Chakravarthi, R. Jagadeesh Kannan

Keywords

Related Articles

MCIP Client Application for SCADA in Iiot Environment

Modern automation systems architectures which include several subsystems for which an adequate burden sharing is required. These subsystems must work together to fulfil the tasks imposed by the common function, given by...

A DISTRIBUTED KEY BASED SECURITY FRAMEWORK FOR PRIVATE CLOUDS

Cloud computing in its various forms continues to grow in popularity as organizations of all sizes seek to capitalize on the cloud’s scalability, externalization of infrastructure and administration and generally reduced...

A Minimum Redundancy Maximum Relevance-Based Approach for Multivariate Causality Analysis

Causal analysis, a form of root cause analysis, has been applied to explore causes rather than indications so that the methodology is applicable to identify direct influences of variables. This study focuses on observati...

Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity cl...

Intrusion Detection in Wireless Body Sensor Networks

The recent advances in electronic and robotics industry have enabled the manufacturing of sensors capable of measuring a set of application-oriented parameters and transmit them back to the base station for analysis purp...

Download PDF file
  • EP ID EP626602
  • DOI 10.14569/IJACSA.2019.0100822
  • Views 123
  • Downloads 0

How To Cite

S. Sreenivasa Chakravarthi, R. Jagadeesh Kannan (2019). Non-linear Dimensionality Reduction-based Intrusion Detection using Deep Autoencoder. International Journal of Advanced Computer Science & Applications, 10(8), 168-174. https://europub.co.uk/articles/-A-626602