Anomaly-based Intrusion Detection using Machine Learning Algorithms-A Review Paper (original) (raw)
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Anomaly based Intrusion Detection System using Machine Learning
IJRASET, 2021
Attacks on the computer infrastructures are becoming an increasingly serious issue. The problem is ubiquitous and we need a reliable system to prevent it. An anomaly detection-based network intrusion detection system is vital to any security framework within a computer network. The existing Intrusion detection system have a high detection rate but they also have mendacious alert rates. With the use of Machine Learning, we can implement an efficient and reliable model for Intrusion detection and stop some of the hazardous attacks in the network. This paper focuses on detailed study on NSL-KDD dataset after extracting some of the relevant records and then several experiments have been performed and evaluated to assess various machine learning classifiers based on dataset. The implemented experiments demonstrated that the Random forest classifier has achieved the highest average accuracy and has outperformed the other models in various evaluations.
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.
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 System Using Machine Learning: An Overview
IRJET, 2022
Today's wireless networks are faced with rapid expansions in errors, flaws, and attacks that threaten to undermine their security. Since computer networks and applications are built on multiple platforms, network security is becoming increasingly important. Both complex and expensive operating programs may have security vulnerabilities. The term "intrusion" refers to attempts to break security, completeness, and availability. Network security vulnerabilities and abnormalities can be identified using an IDS. The development of intrusion detection technology has been a burgeoning field, despite being often regarded as premature and not as an ultimately comprehensive method of fighting intrusions. Security experts and network administrators have also made it a priority task. This means that more secure systems cannot replace it completely. Using data mining to detect intrusion, IDS is able to predict future intrusions based on detected intrusions. An extensive review of literature on the use of data mining methods for IDS is presented in this paper. First, we will review data mining approaches for detecting intrusions using realtime and benchmark datasets. This paper presents a comparison of methods of detecting intrusions in the network with their merits and demerits. In this paper, we propose approaches to improve network intrusion detection.
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.
A Review of Machine Learning-based Algorithms for Intrusion Detection System
INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT), 2023
Networks play important roles in modern life, and cyber security has become a dynamic research area. An intrusion detection system (IDS) which is an important cyber security method, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that exploit on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. In this paper, we conducted a comprehensive review on machine learning techniques used in building IDS.
A Review on Network Intrusion Detection System Using Machine Learning
2019
After digital revolution, large amount of data are produced from diverse networks from time to time. Hence security of this data is more important. So, there is a need to automate this security system. Intrusion detection systems are considered as the best solution to detect intrusions. Network intrusion detection systems (NIDS) are hired as a defense system to protect networks. Numerous techniques for the development of these defense systems are found in the literature. However, study on the enhancement of datasets used to train and test such security systems is also important. Improved datasets progress the detection capabilities for both offline and online intrusion detection models. Standard datasets like KDD 99, NSL-KDD cup 99 and DARPA 1999 are outdated and they don’t contain data of present attacks such as Denial of Service, therefore they are not suitable for evaluation. In this paper, in depth analysis of CIDDS-001 dataset is shown and the sightings are presented. In this p...
Anomaly intrusion detection using machine learning- IG-R based on NSL-KDD dataset
Bulletin of Electrical Engineering and Informatics, 2024
Cybersecurity is challenging for security guards because of the rising quantity, variety, and frequency of attacks and malicious activities in cyberspace. Intrusion attacks are among the most common types of cyberspace attacks. Therefore, an intrusion detection system (IDS) is in high demand to accurately detect and mitigate their impact. In this paper, an anomaly IDS using machine learning and information gain-rank (IG-R) is proposed to improve the detection accuracy of intrusions. The network security lab-knowledge discovery dataset (NSL-KDD) is used to train and test the proposed IDS. Initially, the information gain (IG) algorithm and Ranker are used to evaluate, rank and reduce the number of selected instances from 41 instances to only 6 instances. Furthermore, many classifiers have been tested and evaluated; such as adaptive boosting (AdaBoostM1), random forest, J48, and naïve Bayes to choose the best performance classifier to be used in the detection process. After applying the IG-R and testing the suggested classifiers, the results showed that the random forest classifier has the best performance over the tested classifiers with TPR, FPR, and accuracy of 99.7%, 0.3%, and 99.7%, respectively, and is recommended to be used in the detection process.
2020
Network security is very essential in today’s environment in data security, cloud security as well as all the resources security which is shared in network environment. Basically IDS is the such kind of program which takes unauthorized access of vulnerable resources. It has categorized into Network base IDS and Host base IDS. Intrusions and abuse are constantly threatening to comprehensive internet service use. Therefore, the system for intrusion detection is the most important component of the machine and its network security. Intrusion Detection System (IDS) is an algorithmfocused computer network surveillance system that detects the presence of malevolent interference in the network. The IDS system has been recognized for maintaining high standards of safety, meaning that information is exchanged with confidence and security amongst dissimilar organizations. Systems for intrusion detection divide user activity into two main categories: regular, and distrustful. This paper system ...
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/a-detailed-analysis-on-nsl-kdd-dataset-using-various-machine-learning-techniques-for-intrusion-detection https://www.ijert.org/research/a-detailed-analysis-on-nsl-kdd-dataset-using-various-machine-learning-techniques-for-intrusion-detection-IJERTV2IS120804.pdf During the last decade the analysis of intrusion detection has become very important, the researcher focuses on various dataset to improve system accuracy and to reduce false positive rate based on DAPRA 98 and later the updated version as KDD cup 99 dataset which shows some statistical issues, it degrades the evaluation of anomaly detection that affects the performance of the security analysis which leads to the replacement of KDD dataset to NSL-KDD dataset. This paper focus on detailed study on NSL-KDD dataset that contains only selected record. This selected dataset provide a good analysis on various machine learning techniques for intrusion detection.