Rule-based integration of multiple measure-models for effective intrusion detection

2004 ◽  
Author(s):  
Sang-Jun Han ◽  
Sung-Bae Cho
Keyword(s):  
Intrusion Detection ◽  
Rule Based ◽  
Multiple Measure

Intrusion Detection Using Machine Learning

2010 ◽  
pp. 70-107
Author(s):  
Mohammed M. Mazid ◽  
A. B.M. Shawkat Ali ◽  
Kevin S. Tickle
Keyword(s):  
Intrusion Detection ◽  
Medical Diagnosis ◽  
Computer Network ◽  
Model Identification ◽  
Intrusion Detection Systems ◽  
Network Technology ◽  
Rule Based ◽  
Detection Systems ◽  
Research Areas ◽  
Future Direction

Intrusion detection has received enormous attention from the beginning of computer network technology. It is the task of detecting attacks against a network and its resources. To detect and counteract any unauthorized activity, it is desirable for network and system administrators to monitor the activities in their network. Over the last few years a number of intrusion detection systems have been developed and are in use for commercial and academic institutes. But still there have some challenges to be solved. This chapter will provide the review, demonstration and future direction on intrusion detection. The authors’ emphasis on Intrusion Detection is various kinds of rule based techniques. The research aims are also to summarize the effectiveness and limitation of intrusion detection technologies in the medical diagnosis, control and model identification in engineering, decision making in marketing and finance, web and text mining, and some other research areas.

Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection

2020 ◽  
Vol 29 (6) ◽  
pp. 267-283
Author(s):  
Femi Emmanuel Ayo ◽  
Sakinat Oluwabukonla Folorunso ◽  
Adebayo A. Abayomi-Alli ◽  
Adebola Olayinka Adekunle ◽  
Joseph Bamidele Awotunde
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Learning Model ◽  
Network Intrusion Detection ◽  
Rule Based ◽  
Network Intrusion ◽  
Deep Learning Model

scholarly journals Neural networks in intrusion detection systems

2012 ◽  
Vol 50 (No. 1) ◽  
pp. 35-40 ◽  
Author(s):  
A. Veselý ◽  
D. Brechlerová
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
System Structure ◽  
Intrusion Detection Systems ◽  
Network Technology ◽  
Promising Technique ◽  
Abnormal Behaviour ◽  
Rule Based ◽  
Detection Systems ◽  
Neural Network Technology

Security of an information system is its very important property, especially today, when computers are interconnected via internet. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. For this purpose, Intrusion Detection Systems (IDS) were designed. There are two basic models of IDS: misuse IDS and anomaly IDS. Misuse systems detect intrusions by looking for activity that corresponds to the known signatures of intrusions or vulnerabilities. Anomaly systems detect intrusions by searching for an abnormal system activity. Most IDS commercial tools are misuse systems with rule-based expert system structure. However, these techniques are less successful when attack characteristics vary from built-in signatures. Artificial neural networks offer the potential to resolve these problems. As far as anomaly systems are concerned, it is very difficult to build them, because it is difficult to define the normal and abnormal behaviour of a system. Also for building anomaly system, neural networks can be used, because they can learn to discriminate the normal and abnormal behaviour of a system from examples. Therefore, they offer a promising technique for building anomaly systems. This paper presents an overview of the applicability of neural networks in building intrusion systems and discusses advantages and drawbacks of neural network technology.

Sign in / Sign up

Export Citation Format

Share Document