Evolutionof Intrusion Detection Systems Based on Machine Learning Methods (original) (raw)
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Anomaly Based Intrusion Detection System Which Analyze the Dataset and Detect Intrusion
VAWKUM Transactions on Computer Sciences
As the research increased in computer science highlight the scientists mind for the growing research world towards security. Researchers have done a lot of research work in network Security. Cybersecurity has progressively become a zone of alarm for officials, Government agencies and industries, including big commercialized infrastructure, are under attack daily. First signature-based intrusion detection systems were developed, and it detects only novel attacks. To detect strange attacks statistical IDS came into being recognized as anomaly-based IDS. It is not as much efficient as it detects all. In this, study the author focus on the efficiency of IDS using NSL-KDD99 dataset and support vector machine (SVM) technique to identify attacks. NSL-KDD dataset is used for the evaluation of these type of systems.
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.
Anomaly-Based Intrusion Detection System
Computer and Network Security, 2019
Anomaly-based network intrusion detection plays a vital role in protecting networks against malicious activities. In recent years, data mining techniques have gained importance in addressing security issues in network. Intrusion detection systems (IDS) aim to identify intrusions with a low false alarm rate and a high detection rate. Although classification-based data mining techniques are popular, they are not effective to detect unknown attacks. Unsupervised learning methods have been given a closer look for network IDS, which are insignificant to detect dynamic intrusion activities. The recent contributions in literature focus on machine learning techniques to build anomaly-based intrusion detection systems, which extract the knowledge from training phase. Though existing intrusion detection techniques address the latest types of attacks like DoS, Probe, U2R, and R2L, reducing false alarm rate is a challenging issue. Most network IDS depend on the deployed environment. Hence, developing a system which is independent of the deployed environment with fast and appropriate feature selection method is a challenging issue. The exponential growth of zero-day attacks emphasizing the need of security mechanisms which can accurately detect previously unknown attacks is another challenging task. In this work, an attempt is made to develop generic meta-heuristic scale for both known and unknown attacks with a high detection rate and low false alarm rate by adopting efficient feature optimization techniques.
Machine Learning Technique for Intrusion Detection in the field of the Intrusion Detection System
The growing connectivity between computers, sensors, and servers necessitates effective intrusion detection for network security. This research focuses on using machine learning techniques for host-based IDS in e-commerce applications. We propose a machine learning approach to classify HTTP requests as normal or malicious traffic using publicly available datasets like CSIC 2010. Feature selection is emphasized, and we compare the performance of various algorithms including logistic regression, k-means, Gaussian Naive Bayes, Gaussian Mixture, random forest, MLP classifier, KNN, decision tree, AdaBoost, XGBoost, and LightGBM. XGBoost and LightGBM. We optimize the models through hyperparameter tuning and class imbalance techniques and employ visualization methods for result interpretation. This research highlights the potential of supervised and unsupervised machine learning in enhancing intrusion detection systems and improving network security.
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.
Anomaly-based Intrusion Detection using Machine Learning Algorithms-A Review Paper
2020
An intrusion is termed as an activity that attempts to compromise the confidentiality or availability of a resource. An intrusion detection system i.e. IDS is the most important field of network security, that monitors the state of software and hardware running in the network. In the past few years, Intrusion detection using machine learning technique has captured the attention of most of the researchers, and every researcher proposes a different algorithm for the distinct features used in the dataset. KDD-Cup99 intrusion detection dataset plays a vital role in the network intrusion detection system and NSL-KDD is an updated or revised version of KDDCup99. The dataset which is mostly used by the researchers working in the field of intrusion detection is KDD-Cup99. This paper presents an overview of various IDS and also the detailed analyses of various machine learning techniques and datasets used for improving IDS.
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.
Anomaly Based Intrusion Detection through Efficient Machine Learning Model
IJEER , 2023
Machine learning is commonly utilised to construct an intrusion detection system (IDS) that automatically detects and classifies network intrusions and host-level threats. Malicious assaults change and occur in high numbers, needing a scalable solution. Cyber security researchers may use public malware databases for research and related work. No research has examined machine learning algorithm performance on publicly accessible datasets. Data and physical level security and analysis for Data protection have become more important as data volumes grow. IDSs collect and analyse data to identify system or network intrusions for data prevention. The amount, diversity, and speed of network data make data analysis to identify assaults challenging. IDS uses machine learning methods for precise and efficient development of data security mechanism. This work presented intrusion detection model using machine learning, which utilised feature extraction, feature selection and feature modelling for intrusion detection classifier.
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%).
Intrusion Detection System Using Machine Learning Approach
International Journal Of Engineering And Computer Science, 2016
In this paper, we present an intrusion detection model based on genetic algorithm and neural network. The key idea is to take advantage of classification abilities of genetic algorithm and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of this System. We used the KDD99 benchmark dataset and obtained reasonable detection rate