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https://ieeexplore.ieee.org/document/5473653/
Abstract: Intrusion detection system is of most importance to network security. Support Vector Machine (SVM) is algorithm about how to solve machine learning problems under circumstance of small sample. The paper respectively applies SVM based on least square and least-square SVM improved by greedy algorithm to intrusion detection, and does simulation experiment on intrusions detection data.Cited by: 1
https://www.sciencedirect.com/science/article/pii/S0305054804000711
In this study, we explore the feasibility of applying an Artificial Neural Network (ANN) and Support Vector Machine (SVM) to predict attacks based on frequency-based encoding techniques. The goal of using ANN and SVM for attack detection is to develop a …Cited by: 357
https://www.researchgate.net/publication/224289101_Application_of_Support_Vector_Machine_and_Genetic_Algorithm_to_Network_Intrusion_Detection
The paper first gives an introduction to the basic concept of intrusion detection and the basic principle of the classifier based on support vector machine, then discusses algorithm of support ...
http://www.iosrjen.org/Papers/Conf.NWIOT-2018/Volume-1/6.%2025-27.pdf
Support Vector Machine Used in Network Intrusion Detection National Workshop on Internet of Things (IoT) on 28th -29th Sept 2018 26 Page Generic Algorithms (GAs), and Fuzzy Logic, as well as hybrid classifiers that combine multiple machine learning techniques to improve the performance of …
https://www.researchgate.net/publication/292467230_Network-based_intrusion_detection_with_support_vector_machines
This paper proposes a method of applying Support Vector Machines to network-based Intrusion Detection System (SVM IDS). Support vector machines(SVM) is …
https://pdfs.semanticscholar.org/1b02/2613fbea96bacf3071b482bcb1eded7c0757.pdf
Application of Weighted Support Vector Machines to Network Intrusion Detection Yinshan Jia 1,2, Chuanying Jia 2, Hongwei Qi 3 1School of Information Technology, Liaoning University of Petroleum and Chemical Technology, Fushun 113001, China 2Dalian Maritime University, Dalian 116026, China 3Fushun Ethylene Chemical Co. Ltd., Fushun 113004, China
https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/8880/Nyakundi_Eric_201505_Msc.pdf?sequence=5
classi er. The support vector machines achieve a high detection rate of 99.1% with low false positive rate of 4.5% in the intrusion classi cation task on the ISCX2012 dataset. This demonstrates that SVMs can be used successfully as the classi er of choice in the classi cation module of a network anomaly intrusion detection system. 1.2 Overview ...
https://pdfs.semanticscholar.org/f60f/c271f451373196189398bac7d91429345217.pdf
Intrusion Detection System, Support Vector Machine. Keywords Support Vector Machines, k-nearest neighbor algorithm, Information Gain Ratio, feature ranking and selection, intrusion detection system. 1. INTRODUCTION With the advent and increased reach of information technology over the last few years, there have been significant
https://link.springer.com/article/10.1007%2Fs00778-006-0002-5
Aug 31, 2006 · A new intrusion detection system using support vector machines and hierarchical clustering. ... and by overloading network hosts. Intrusion Detection attempts to detect computer attacks by examining various data records observed in processes on the network and it is split into two groups, anomaly detection systems and misuse detection systems ...Cited by: 432
http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
intrusion detection accuracy and low false positives but also in terms of their generalization ability in the presence of noise and running time. Index Terms—Intrusion detection, computer security, robust support vector machines, noisy data. I. INTRODUCTION THE rapid increase in …
https://ieeexplore.ieee.org/document/5473653/
Application of Improved Support Vector Machines in Intrusion Detection Abstract: Intrusion detection system is of most importance to network security. Support Vector Machine (SVM) is algorithm about how to solve machine learning problems under circumstance of small sample.
https://www.semanticscholar.org/paper/Application-of-SVM-and-ANN-for-intrusion-detection-Chen-Hsu/b06092bae394b8877e0f0dfdc1c12a451e5c1377
The popularization of shared networks and Internet usage demands increases attention on information system security, particularly on intrusion detection. Two data mining methodologies-Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) and two encoding methods-simple frequency-based scheme and tfi?idf scheme are used to detect potential …
http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
Index Terms—Intrusion detection, computer security, robust support vector machines, noisy data. I. INTRODUCTION THE rapid increase in connectivity and accessibility of computer systems has resulted in frequent opportunities for intrusions and attacks. Anomaly detection and misuse detection are two general approaches to computer intrusion ...
https://www.ijrcar.com/Volume_3_Issue_12/v3i1208.pdf
Keywords: Intrusion detection system, support vector machine. 1. Introduction Security is becoming a critical issue as the Internet applications are growing. The current security technologies are focusing on encryption, ID, firewall and access control. But all these technologies cannot assure flawless security.
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004340340
Support Vector Machine (SVM) is a powerful tool to solve classification problems. Many works have been done in intrusion detection based on SVM, and the detection accuracy is relatively high. But how to get a higher accuracy is a new question. In this paper, we apply SVM and Genetic Algorithm (GA) to intrusion detection to solve this problem.
https://thescipub.com/PDF/jcssp.2011.1560.1564.pdf
Although support vector machines have become the key techniques for anomaly intrusion detection due to their good generalization nature and the ability to overcome the curse of dimensionality (Lundin and Jonsson, 2002; Sodiya et al ., 2004), the main issue of SVM technique applied to intrusion detection is its low efficiency.
https://file.scirp.org/pdf/JILSA_2014021411471330.pdf
Support Vector Machineand Random Forest Modeling for Intrusion Detection System (IDS)46 OPEN ACCESS JILSA As network attacks have increased in number and se - verity over the past few years, Intrusion Detection Sys-tems (IDSs) have become a necessary addition to the se- curity infrastructure of most organizations 3]. Deploying
https://web.cs.ucdavis.edu/~vemuri/papers/rvsm.pdf
MIT’s Lincoln Labs to study intrusion detection systems, the performance of robust support vector machines (RVSMs) was compared with that of conventional support vector machines and nearest neighbor classifiers in separating normal usage profiles from intrusive profiles of computer programs. The results
https://www.powershow.com/view/152fc3-Mjk3O/Application_of_SVM_and_ANN_for_intrusion_detection_Computers_powerpoint_ppt_presentation?varnishcache=1
PPT – Application of SVM and ANN for intrusion detection Computers PowerPoint presentation free to view - id: 152fc3-Mjk3O. The Adobe Flash plugin is needed to view this content. Get the plugin now
https://www.sciencedirect.com/science/article/pii/S0167739X13001416
In this paper, we introduce a new machine-learning-based data classification algorithm that is applied to network intrusion detection. The basic task is to classify network activities (in the network log as connection records) as normal or abnormal while minimizing misclassification.
https://www.mdpi.com/2073-8994/11/3/380/pdf
In this paper, a key feature recognition algorithm of network intrusion signal based on neural network and support vector machine is proposed. Generalized learning rules are used to train linear principal component neural network to extract feature components, and then support vector machine is used to do so.
http://infonomics-society.org/wp-content/uploads/jitst/published-papers/volume-2-2013/A-Comparative-Study-of-Hidden-Markov-Model-and-Support-Vector-Machine-in-Anomaly-Intrusion-Detection1.pdf
Hidden Markov Model (HMM) and Support Vector Machine (SVM) for anomaly intrusion detection. These techniques discriminate between normal and abnormal behaviour of network traffic. The specific focus of this study is to investigate and identify distinguishable TCP services that comprise of both
https://ieeexplore.ieee.org/document/8438865
Aug 17, 2018 · Enhanced Network Anomaly Detection Based on Deep Neural Networks ... Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant ...
https://link.springer.com/chapter/10.1007%2F978-981-10-3187-8_13
Sep 27, 2017 · This paper proposes a novel approach called KDSVM, which utilized the k-mean techniques and advantage of feature learning with deep neural network (DNN) model and strong classifier of support vector machines (SVM) , to detection intrusion networks. KDSVM is composed of two stages.
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