Analysis of Machine Learning Techniques for Intrusion Detection System: A Review (original) (raw)
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Application of Machine Learning Approaches in Intrusion Detection System: A Survey
Network security is one of the major concerns of the modern era. With the rapid development and massive usage of internet over the past decade, the vulnerabilities of network security have become an important issue. Intrusion detection system is used to identify unauthorized access and unusual attacks over the secured networks. Over the past years, many studies have been conducted on the intrusion detection system. However, in order to understand the current status of implementation of machine learning techniques for solving the intrusion detection problems this survey paper enlisted the 49 related studies in the time frame between 2009 and 2014 focusing on the architecture of the single, hybrid and ensemble classifier design. This survey paper also includes a statistical comparison of classifier algorithms, datasets being used and some other experimental setups as well as consideration of feature selection step.
Analysis of Machine Learning Techniques Based Intrusion Detection Systems
Attacks on Computer Networks are one of the major threats on using Internet these days. Intrusion Detection Systems (IDS) are one of the security tools available to detect possible intrusions in a Network or in a Host. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate as well as low false positive rate. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by authors at different times.
A Review on Intrusion Detection Using Machine Learning Techniques
International Journal of Engineering Research in Computer Science and Engineering, 2022
An essential tool for monitoring and identifying intrusion threats is the intrusion detection system (IDS). As a result, intrusion detection systems monitor network traffic heading through computer systems to detect for malicious activity and recognized dangers, and send alerts. With a focus on datasets, ML methods, and metrics, this study tries to analyse recent IDS research using a Machine Learning (ML) approach. To make sure the model is suitable for IDS application, dataset selection is crucial. The efficiency of the ML method can also be impacted by the dataset structure. As a result, the choice of ML algorithm depends on the dataset's structure. Metric will then offer a quantitative assessment of ML algorithms for a given dataset. In addition True Positive Rate (TPR), False Positive Rate (FPR) and accuracy, are the three metrics for IDS performance evaluation that are most frequently utilized. This is understandable given that these metrics offer crucial cues that are crucial to IDS performance. A clear path and direction for future study has been provided by the discussion and comparison of the results from various works.
Application of Machine Learning Approaches in Intrusion Detection System
Journal of Soft Computing and Data Mining, 2021
The rapid development of technology reveals several safety concerns for making life more straightforward. The advance of the Internet over the years has increased the number of attacks on the Internet. The IDS is one supporting layer for data protection. Intrusion Detection Systems (IDS) offer a healthy market climate and prevent misgivings in the network. Recently, IDS has been used to recognize and distinguish safety risks using Machine Learning (ML). This paper proposed a comparative analysis of the different ML algorithms used in IDS and aimed to identify intrusions with SVM, J48, and Naive Bayes. Intrusion is also classified. Work with the KDD-CUP data set, and their performance has been checked with the WEKA software. A comparison of techniques such as J48, SVM, and Naïve Bayes showed that the accuracy of j48 is the higher one which was (99.96%).
Machine Learning Techniques for Network Intrusion Detection System (NIDS): A Survey
International Journal of Emerging Trends in Engineering Research, 2021
In computer network, security of the network is a major issue and intrusion is the most common threats to security. Cyber attacks detection is becoming more enlightened challenge in detecting these threats accurately. In network security, intrusion detection system (IDS) has played a vital role to detect intrusion. In recent years, numerous methods have been proposed for intrusion detection to detect these security threats. This survey paper study examines recent work in the topic of network security, machine learning based techniques as well as a discussion of the many datasets that are commonly used to evaluate IDS. It also explains how researchers employ Machine Learning Based Techniques to detect intrusions.
Intrusion detection by machine learning: A review
The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.
With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework " s security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments; latest on the scene are Machine Learning approaches. Machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.
Enhancing the features of Intrusion Detection System by using machine learning approaches
International Journal of Scientific and …, 2012
The IDS always analyze network traffic to detect and analyze the attacks. The attack detection methods used by these systems are of two types: anomaly detection and misuse detection methods. Intrusion detection (ID) is a type of security management system for computers and networks. An ID system gathers and analyzes information from various areas within a computer or a network to identify possible security breaches, which include both intrusions and misuse. An Intrusion detection system is designed to classify the system activities into normal and abnormal. ID systems are being developed in response to the increasing number of attacks on major sites and networks. Intrusion detection is the act of detecting unwanted traffic on a network or a device. Several types of IDS technologies exist due to the variance of network configurations. In this paper, we provide you information about the methods that uses a combination of different machine learning approaches to detect a system attacks.
Machine Learning for Intrusion Detection Systems
2019
In recent decade most of technologies are evolved and there security handling also improved. In which, IDS is the software which is used to detect unauthorized intruders in the network. Even though the highly secure devices and there security feature are developed day-by-day. The malicious hackers update their techniques to crack the security by identifying the vulnerability in the network. Lots of intrusion detection algorithms are used in networking devices, most of the IDS attacks are introduced in common networking devices such as router, switches, networking tapes etc. Researchers found various algorithms for detection of intruders in the network. At last, we arrives Machine Learning algorithms for detection of intruders in the network. Machine Learning approaches are rapidly emerging in various extents nowadays, But most of the algorithms results in the sarcastic manner due to its redundancies. In this paper, we surveyed huge number of existing systems regarding IDS and its im...
Intrusion Detection using Machine Learning Techniques
2021
An Intrusion is an uncredited access to a computer in your organization or a personal computer. As the world is becoming more internet-oriented and data leaks occur more than ever in our tech-savvy world, we need to know about these attacks so that they can be prevented hence coming into action Intrusion Detection System. IDS are systems that alert about the attack by analyzing the traffic on the network for signs of unauthorized activity. To identify the attack and alert about that possible attack, this system needs to be trained on some previous attacks data, for this study, the improved version of the KDD99 dataset, NSL-KDD dataset have been used for training the Machine Learning Model. In this analysis of Machine Learning algorithms, the algorithms under consideration are Logistic Regression, Support Vector Machine, Decision Tree, Random Forest. For comparison of the performance of the algorithms metrics like Accuracy Score, Confusion Matrix, and Classification Report were consi...