scholarly journals Improving Anomalous Rare Attack Detection Rate for Intrusion Detection System Using Support Vector Machine and Genetic Programming

2015 ◽  
Vol 44 (2) ◽  
pp. 279-290 ◽  
Author(s):  
Muhammad Syafiq Mohd Pozi ◽  
Md Nasir Sulaiman ◽  
Norwati Mustapha ◽  
Thinagaran Perumal
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Genetic Programming ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector

scholarly journals Building attack detection system base on machine learning

2021 ◽  
Vol 6 (2) ◽  
pp. 018-032
Author(s):  
Rasha Thamer Shawe ◽  
Kawther Thabt Saleh ◽  
Farah Neamah Abbas
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector ◽  
Data Set ◽  
Kdd Cup 99

These days, security threats detection, generally discussed to as intrusion, has befitted actual significant and serious problem in network, information and data security. Thus, an intrusion detection system (IDS) has befitted actual important element in computer or network security. Avoidance of such intrusions wholly bases on detection ability of Intrusion Detection System (IDS) which productions necessary job in network security such it identifies different kinds of attacks in network. Moreover, the data mining has been playing an important job in the different disciplines of technologies and sciences. For computer security, data mining are presented for serving intrusion detection System (IDS) to detect intruders accurately. One of the vital techniques of data mining is characteristic, so we suggest Intrusion Detection System utilizing data mining approach: SVM (Support Vector Machine). In suggest system, the classification will be through by employing SVM and realization concerning the suggested system efficiency will be accomplish by executing a number of experiments employing KDD Cup’99 dataset. SVM (Support Vector Machine) is one of the best distinguished classification techniques in the data mining region. KDD Cup’99 data set is utilized to execute several investigates in our suggested system. The experimental results illustration that we can decrease wide time is taken to construct SVM model by accomplishment suitable data set pre-processing. False Positive Rate (FPR) is decrease and Attack detection rate of SVM is increased .applied with classification algorithm gives the accuracy highest result. Implementation Environment Intrusion detection system is implemented using Mat lab 2015 programming language, and the examinations have been implemented in the environment of Windows-7 operating system mat lab R2015a, the processor: Core i7- Duo CPU 2670, 2.5 GHz, and (8GB) RAM.

scholarly journals Intrusion Detection System using Hybrid SVM-RF and SVM-DT in Wireless Sensor Networks

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1926-1931
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Hybrid Algorithm ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Support Vector ◽  
Second Stage ◽  
Network Intrusion

Intrusion detection system (IDS) is one of the essential security mechanisms against attacks in WSN. Network intrusion detection system (NIDS) generally uses the classification techniques in order to obtain the best possible accuracy and attack detection rate. In this paper, Intrusion Detection System is designed which uses two-stage hybrid classification method. In the first stage it uses Support Vector Machine (SVM) as anomaly detection, and in the second stage it uses Random Forest (RF)/Decision Tree (DT) as misuse. The abnormal activities are detected in the first stage. These abnormal activities are further analyzed and the known attacks are identified in the second stage and are classified as Denial of Service (DoS) attack, Probe attack, Remote to Local (R2L) attack and User to Root (U2R) attack. Simulation results reveal that the proposed hybrid algorithm obtains better accuracy and detection rate than the single classifier namely, SVM, RF and DT algorithm. The experimental results also shows that hybrid algorithm can detect anomaly activity in a reliable way. Proposed technique uses the standard NSL KDD dataset to evaluate/calculate the performance of the proposed approach. Here the results show that the proposed Hybrid SVM-RF/DT IDS technique performs better in terms of detection rate, accuracy and recall than the existing SVM, RF and DT approaches.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

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