Goal Of Intrusion Detection System Research Articles

A balanced communication-avoiding support vector machine decision tree method for smart intrusion detection systems

The Internet of Things field has created many challenges for network architectures. Ensuring cyberspace security is the primary goal of intrusion detection systems (IDSs). Due to the increases in the number and types of attacks, researchers have sought to improve intrusion detection systems by efficiently protecting the data and devices connected in cyberspace. IDS performance is essentially tied to the amount of data, data dimensionality, and security features. This paper proposes a novel IDS model to improve computational complexity by providing accurate detection in less processing time than other related works. The Gini index method is used to compute the impurity of the security features and refine the selection process. A balanced communication-avoiding support vector machine decision tree method is performed to enhance intrusion detection accuracy. The evaluation is conducted using the UNSW-NB 15 dataset, which is a real dataset and is available publicly. The proposed model achieves high attack detection performance, with an accuracy of approximately 98.5%.

Network intrusion detection system: machine learning approach

The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.

Construct an efficient distributed denial of service attack detection system based on data mining techniques

<span>Distributed denial-of-service (DDoS) attack is bluster to network security that purpose at exhausted the networks with malicious traffic. Although several techniques have been designed for DDoS attack detection, intrusion detection system (IDS) It has a great role in protecting the network system and has the ability to collect and analyze data from various network sources to discover any unauthorized access. The goal of IDS is to detect malicious traffic and defend the system against any fraudulent activity or illegal traffic. Therefore, IDS monitors outgoing and incoming network traffic. This paper contains a based intrusion detection system for DDoS attack, and has the ability to detect the attack intelligently, dynamically and periodically by evaluating the set of attackers of the current node with its neighbors. We use dataset named CICDDoS2019 that contains on binary classes benign and DDoS. Performance has evaluated by applying data mining algorithms as well as applying the best features to discover potential attack classes.</span>

Intrusion Detection using Deep Learning Long Short-term Memory with Wrapper Feature Selection Method

Recently, many companies move to use cloud com-puting systems to enhance their performance and productivity. Using these cloud computing systems allows the execution of applications, data, and infrastructures on cloud platforms (i.e., online), which increase the number of attacks on such systems. As a resulting, building robust Intrusion detection systems (IDS) is needed. The main goal of IDS is to detect normal and abnormal network traffic. In this paper, we propose a hybrid approach between an Enhanced Binary Genetic Algorithms (EBGA) as a wrapper feature selection (FS) algorithm and Long Short-Term Memory (LSTM). A novel injection method to prevent premature convergence of the GA is proposed in this paper. An intelligent k-means algorithm is employed to examine the solution distribution in the search space. Once 80% of the solutions belong to one cluster, an injection method (i.e., add new solutions) is used to redistribute the solutions over the search space. EBGA will reduce the search space as a preprocessing step, while LSTM works as a binary classification method. UNSW-NB15, a real-world public dataset, is used in this work to evaluate the proposed system. The obtained results show the ability of feature selection method to enhance the overall performance of LSTM.

Survey of Automotive Controller Area Network Intrusion Detection Systems

Editor’s note: Control Area Network (CAN) is one of the most popular targets for malicious attacks and exploitations in modern automotive systems. The goal of intrusion detection systems (IDS) is to identify and mitigate security attacks; consequently, they are of paramount importance to automotive security. This article surveys the state of the art in IDS, with special emphasis on techniques for detecting attacks on CAN modules. —Sandip Ray, University of Florida

A Comparative Analysis of Intrusion Detection Techniques: Machine Learning Approach

Intrusion detection plays vital role in network security. Information systems which are based on computer are crucial part of any organization. In network security, detecting an intrusion is major task. Thus, the goal of intrusion detection system is to detect attack in a network domain. To check confidentiality, integrity and availability several algorithms have been implemented. These algorithms are implemented on static dataset like KDD-Cup 99, NSL-KDD, UNSW-NB 15, Kyoto 2006+ etc. But there is a challenge to impart malicious activity on real time data using machine learning algorithm. This paper provides comparative analysis of different machine learning techniques which is use to classify the data and eventually compare the performance of the techniques with respect to accuracy. Experimental results show that RF outperforms over other algorithms.

Effect of Mutation and Crossover Probabilities on Genetic Algorithm and Signature Based Intrusion Detection System

Conventional methods of intrusion prevention like firewalls, cryptography techniques or access management schemes, have not provided complete protection to computer systems and networks from refined malwares and attacks. Intrusion Detection Systems (IDS) are giving the right solution to the current issues and became an important part of any security management system to detect these threats and will not generate widespread harm. The basic goal of IDS is to detect attacks and their nature that may harm the computer system. Several different approaches for intrusion detection have been reported in the literature. The signature based concept using genetic algorithm as features selection and, J48 as classifier to detect attack is proposed in this paper. The system was evaluated on KDD Cup 99, NSL-KDD and Kyoto 2006+ datasets.

A Study on Intrusion Detection Using Centroid-Based Classification

The ultimate goal of intrusion detection system (IDS) development is to accomplish the best possible accuracy for detection attacks. Various hybrid machine learning techniques were developed for IDS. The centroid-based classification method is a particular hybrid learning approach that highly efficient in the training and classification stages. This paper studies 60 associated papers in the period between 2010 and 2016 concentrating on developing IDS using hybrid classifiers, which 11 papers used centroid-based classification. Similar studies are compared by the algorithm used in hybrid machine learning, the dataset used, the establishment of the representative feature, the stages of pre-processing data, and evaluation methods considered. The accomplishments and limitations in developing IDSs using hybrid machine learning and centroid-based classification were presented and discussed. Several future research opportunities were provided that may encourage interested researchers to work in this area.

An Effective ODAIDS-HPS Approach for Preventing, Detecting and Responding to DDoS Attacks

The main objective of the network security is to prevent DDoS (Distributed Denial of Service) attacks in inter-connected systems. Generally, DDoS attacks are attempted by hackers and explicitly block an authorized user from accessing their account and deny them the services they are entitled to. Hackers seek recourse to hacking using malware (i.e., Botnets) for increased access to and control of a large number of computers. Once the malicious system commences its nefarious activities, the attacks are carried out using a well-coordinated operation. After that, an expensive attack is done on more than one targeted machine. The main goal of intrusion detection system and research community working to prevent such attacks is designing a perfect security technique against the discovered and undiscovered DDoS attacks. However, the design of such a technique needs an awareness of the security problem and also the designed technique’s method

Pattern Based Network Security Using Semi-supervised Learning

Network security is becoming increasingly important in today’s internet-worked systems. With development of internet, its use on public networks, number and severity of security threats has increased significantly. Intrusion Detection System can provide a layer of security to these systems. Intrusion Detection can be defined as the act of detecting actions that attempt to compromise confidentiality, integrity or availability of a resource”. More specifically, goal of intrusion detection system is to identify entities who attempt to subvert in-place security controls. At present, two fundamental problems, quantity and quality of outputs i.e. false alarms or alerts of IDS, have not been solved well. The pattern of attack changes frequently. Thus IDS should upgrade accordingly. The changes in patterns are mainly manifestations of attack. Pattern based IDS provides very low false alarms as compare to heuristic/anomaly based IDS. In real world it is very difficult to have large labeled data for training. Supervised approach can't be used in this case. So in this work we propose a semi-supervised approach for pattern based IDS. Our approach uses supervised algorithm as a black box and then filters unlabelled data with predicted label for training system. The experimentation is performed on KDD CUP99 dataset and NSL KDD data which is revised KDD CUP 99 data.