An efficient intrusion detection system based on support vector machines and gradually feature removal method

2012 ◽  
Vol 39 (1) ◽  
pp. 424-430 ◽  
Author(s):  
Yinhui Li ◽  
Jingbo Xia ◽  
Silan Zhang ◽  
Jiakai Yan ◽  
Xiaochuan Ai ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Support Vector ◽  
Removal Method ◽  
Vector Machines

A novel intrusion detection system based on hierarchical clustering and support vector machines

2011 ◽  
Vol 38 (1) ◽  
pp. 306-313 ◽  
Author(s):  
Shi-Jinn Horng ◽  
Ming-Yang Su ◽  
Yuan-Hsin Chen ◽  
Tzong-Wann Kao ◽  
Rong-Jian Chen ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Intrusion Detection ◽  
Hierarchical Clustering ◽  
Intrusion Detection System ◽  
Detection System ◽  
Support Vector ◽  
Vector Machines

scholarly journals Evaluating Performance of Intrusion Detection System using Support Vector Machines: Review

2015 ◽  
Vol 9 (9) ◽  
pp. 225-234 ◽  
Author(s):  
Leila Mohammadpour ◽  
Mehdi Hussain ◽  
Alihossein Aryanfar ◽  
Vahid Maleki Raee ◽  
Fahad Sattar
Keyword(s):  
Support Vector Machines ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Support Vector ◽  
Vector Machines

Feature Selection for Intrusion Detection with Neural Networks and Support Vector Machines

2003 ◽  
Vol 1822 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Srinivas Mukkamala ◽  
Andrew H. Sung
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Support Vector Machines ◽  
Intrusion Detection ◽  
Detection System ◽  
Support Vector ◽  
General Interest ◽  
Data Set ◽  
Learning Machines ◽  
Vector Machines

Computational intelligence (CI) methods are increasingly being used for problem solving, and CI-type learning machines are being used for intrusion detection. Intrusion detection is a problem of general interest to transportation infrastructure protection, since one of its necessary tasks is to protect the computers responsible for the infrastructure’s operational control, and an effective intrusion detection system (IDS) is essential for ensuring network security. Two classes of learning machines for IDSs are studied: artificial neural networks (ANNs) and support vector machines (SVMs). SVMs are shown to be superior to ANNs in three critical respects of IDSs: SVMs train and run an order of magnitude faster; they scale much better; and they give higher classification accuracy. A related issue is ranking the importance of input features, which is itself a problem of great interest. Since elimination of the insignificant (or useless) inputs leads to a simplified problem and possibly faster and more accurate detection, feature selection is very important in intrusion detection. Two methods for feature ranking are presented: the first one is independent of the modeling tool, while the second method is specific to SVMs. The two methods were applied to identify the important features in the 1999 Defense Advanced Research Projects Agency intrusion data set. It was shown that the two methods produce results that are largely consistent. Experimental results indicated that SVM-based IDSs with a reduced number of features can deliver enhanced or comparable performance. An SVM-based IDS for class-specific detection is proposed.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

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