scholarly journals Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 224
Author(s):  
Deepaa Selva ◽  
Balakrishnan Nagaraj ◽  
Danil Pelusi ◽  
Rajendran Arunkumar ◽  
Ajay Nair
Keyword(s):  
Genetic Algorithms ◽  
Intrusion Detection ◽  
Intelligent Network ◽  
Internet Safety ◽  
Intrusion Prevention ◽  
Smart Systems ◽  
Mobile Networking ◽  
Intelligent Software ◽  
Network Intrusion ◽  
Smart Technologies

Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set. These techniques were useful to identify and prevent network intrusion to provide Internet safety and improve service value and accuracy, performance and efficiency. Furthermore, new algorithms of intelligent rules-based attributes collection algorithm for efficient function and rules-based improved vector support computer, were proposed in this article, along with a survey into the current smart techniques for intrusion detection systems.

An Intelligent Network Intrusion Detection System Based on Multi-Modal Support Vector Machines

2013 ◽  
Vol 7 (4) ◽  
pp. 37-52
Author(s):  
Srinivasa K G
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Intelligent Network ◽  
Statistical Machine Learning ◽  
High Detection Rate ◽  
Network Intrusion

Increase in the number of network based transactions for both personal and professional use has made network security gain a significant and indispensable status. The possible attacks that an Intrusion Detection System (IDS) has to tackle can be of an existing type or of an entirely new type. The challenge for researchers is to develop an intelligent IDS which can detect new attacks as efficiently as they detect known ones. Intrusion Detection Systems are rendered intelligent by employing machine learning techniques. In this paper we present a statistical machine learning approach to the IDS using the Support Vector Machine (SVM). Unike conventional SVMs this paper describes a milti model approach which makes use of an extra layer over the existing SVM. The network traffic is modeled into connections based on protocols at various network layers. These connection statistics are given as input to SVM which in turn plots each input vector. The new attacks are identified by plotting them with respect to the trained system. The experimental results demonstrate the lower execution time of the proposed system with high detection rate and low false positive number. The 1999 DARPA IDS dataset is used as the evaluation dataset for both training and testing. The proposed system, SVM NIDS is bench marked with SNORT (Roesch, M. 1999), an open source IDS.

scholarly journals Classifier Rank Identification using Multi-Criteria Decision Making Method for Intrusion Detection Dataset

2019 ◽  
Vol 9 (1) ◽  
pp. 1732-1738 ◽  
Keyword(s):  
Decision Making ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Detection System ◽  
False Positive Rate ◽  
Absolute Error ◽  
Multi Criteria Decision Making ◽  
Intrusion Prevention ◽  
Network Intrusion ◽  
Intrusion Prevention System

Network intrusion detection system (NIDS) tracks network traffic for suspicious activity and policy violations. It generates alerts whenever such activity found. The objective is to detect and report anomalies. Further intrusion prevention system can take action such as blocking traffic from suspected IP addresses. Classification of network traffic as is a tedious task. Existing classifiers are suffered by generating many/false alerts. It is paramount important to select best classification approach among set of available approaches. KDD 99 is the benchmark dataset utilized to test the classification capabilities of classifiers. However, many classifiers generate similar results by measuring performance on various criteria. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a traditional multi-criteria decision making (MCDM) approach which is widely used to rank classifiers from number of options that are assessed on various criteria. In this work, KDD 99 dataset is applied as input to bayes net, naive bayes, NB updateable, random forest, oneR, zeroR, adaboostM1, decision stump, J48 and decision table classifiers. The performance of each classifier is measured using 10 different criteria’s such as accuracy, misclassification, RA error, RMS error, false positive rate, f- measure, precision, RRS error, mean absolute error and recall. In order to test the effectiveness of proposed approach weka utility is utilized for classification and classifier performance result are supplied to the TOPSIS. An application is designed to implement TOPSIS method using python. It is observed that J48 secured at the top position with performance score 0.5829.

AN INTELLIGENT NETWORK INTRUSION DETECTION USING DATA MINING TECHNIQUES

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Mohd Afizi Mohd Shukran ◽  
Kamaruzaman Maskat
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Human Detection ◽  
Network Intrusion Detection ◽  
Intelligent Network ◽  
Data Mining Technique ◽  
Network Attacks ◽  
Network Intrusion ◽  
On Line ◽  
Using Data

Network Intrusion Detection is to detect malicious attacks to the networks for different uses from military to enterprise. Currently available approaches either rely on the known network attacks or have high proportion of normal network traffics that were erroneously reported as anomalous traffics. The aim of this paper is to develop an efficient algorithm for intrusion detection without prior knowledge of network attacks. Uniquely, our approach will integrate a newly developed data mining technique for data feature classification with techniques commonly used for human detection. The key idea is to achieve on-line and automated learning of new attacks for precise and real-time intrusion detection.

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