By Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita
With the quick upward push within the ubiquity and class of net expertise and the accompanying progress within the variety of community assaults, community intrusion detection has develop into more and more very important. Anomaly-based community intrusion detection refers to discovering unheard of or nonconforming styles in community site visitors info in comparison to common habit. discovering those anomalies has vast purposes in parts resembling cyber safety, bank card and assurance fraud detection, and armed forces surveillance for enemy actions. community Anomaly Detection: A computing device studying standpoint offers computer studying concepts extensive that can assist you extra successfully notice and counter community intrusion.
In this publication, you’ll study about:
Network anomalies and vulnerabilities at a number of layers
The execs and cons of assorted computer studying options and algorithms
A taxonomy of assaults in accordance with their features and behavior
Feature choice algorithms
How to evaluate the accuracy, functionality, completeness, timeliness, balance, interoperability, reliability, and different dynamic facets of a community anomaly detection system
Practical instruments for launching assaults, taking pictures packet or movement site visitors, extracting positive factors, detecting assaults, and comparing detection performance
Important unresolved matters and examine demanding situations that have to be conquer to supply greater safety for networks
Examining quite a few assaults intimately, the authors examine the instruments that intruders use and exhibit find out how to use this data to guard networks. The publication additionally offers fabric for hands-on improvement, that you should code on a testbed to enforce detection equipment towards the advance of your individual intrusion detection process. It bargains an intensive creation to the cutting-edge in community anomaly detection utilizing computing device studying ways and structures.
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Additional info for Network Anomaly Detection: A Machine Learning Perspective
This book introduces the concepts and discusses algorithms for feature selection, and also compares their performance. 5. Evaluation of Intrusion Detection Systems (IDSs): This book covers various methods and techniques to assess the accu- Introduction 13 racy, performance, completeness, timeliness, stability, interoperability, reliability and other dynamic aspects of a network intrusion detection system. It looks at accuracy with five important measures: sensitivity and specificity, misclassification rate, confusion matrix, precision and recall, F-measure and receiver operating characteristic (ROC) curves.
Issues, challenges and prognosis for network intrusions: This book finally discusses important unresolved issues and research challenges. For example, it discusses how network intrusion patterns may change in the future. Another topic of discussion is the enhanced computational requirements necessary for a network defense mechanism to possess as changed motivations of attackers lead to more sophisticated attacks. 7 Organization The book is organized in three major parts with a total of nine chapters and three appendices.
Network Security Evaluation, Syngress Publishing Inc. , Schultz, E. , Lu, W. , Network Intrusion Detection and Prevention — Concepts and Techniques, Springer • Marchette, D. , Dittrich, D. , Internet Denial of Service–Attack and Defense Mechanisms, Prentice Hall • Rehman, R. , Intrusion Detection with SNORT, SAMS • Yu, Z. and Tsai, J. J. , Intrusion Detection–A Machine Learning Approach, Imperial College Press Several of these books cover network security in a very general way, discussing various aspects of it.