Automotive product development Component suppliers

URN 96/937Available from British Library Document Supply Centre-DSC:GPE/0053 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Structural health monitoring of smart structures

10.1088/0964-1726/11/4/314Smart Materials and Structures114581-589SMST

Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network

Botnet represent a critical threat to computer networks because their behavior allows hackers to take control of many computers simultaneously. Botnets take over the device of their victim and performs malicious activities on its system. Although many solutions have been developed to address the detection of Botnet in real time, these solutions are still prone to several problems that may critically affect the efficiency and capability of identifying and preventing Botnet attacks. The current work proposes a technique to detect Botnet attacks using a feed-forward backpropagation artificial neural network. The proposed technique aims to detect Botnet zero-day attack in real time. This technique applies a backpropagation algorithm to the CTU-13 dataset to train and evaluate the Botnet detection classifier. It is implemented and tested in various neural network designs with different hidden layers. Results demonstrate that the proposed technique is promising in terms of accuracy and efficiency of Botnet detection

Botnet detection techniques: review, future trends, and issues

NoIn recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.University of Malaya, Malaysia (No. FP034-2012A