Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks (original) (raw)
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MAIDEn: A Machine Learning Approach for Intrusion Detection using Ensemble Technique
International Journal of Computer Applications
An Intrusion detection system is a machine or software that monitors the traffic in a network and on detection of a malicious packet, informs the user or a specific acting unit which can take further action and avoid the malicious packet from entering the network. This paper discusses a way to implement an intelligent IDS which classifies the normal traffic in a network with abnormal or attacked ones. This paper explains the method used to generate such a system and the various classifiers used in the generation process. The proposed system of Intrusion Detection, classifies data with three different classifiers and an Ensemble technique which selects the majority of the three classifiers to assign the packet in the network as anomaly or normal. The dataset used to train the classifiers is the NSL-KDD dataset. The IDS proposed serves many applications in the field of Military Systems, Banks and Social Networking websites where data is very sensitive. The paper also explains related work done in this field and briefly explains every classifier, the network attacks and the dataset.
A Machine Learning Approach for Intrusion Detection using Ensemble Technique-A Survey
An Intrusion detection system is a machine or software that monitors the traffic in a network and on detection of a malicious packet, informs the user or a specific acting unit which can take further action and avoid the malicious packet from entering the network. In network intrusion, there may be multiple computing nodes attacked by intruders. The evidences of intrusions have to gather from all such attacked nodes. An intruder may move between multiple nodes in the network to conceal the origin of attack, or misuse some compromised hosts to launch the attack on other nodes. To detect such intrusion activities spread over the whole network, we present a new intrusion detection system (IDS) that classifies data with three different classifiers and an Ensemble technique that selects the majority of the three classifiers to assign the packet in the network as anomaly or normal. In this paper, we discuss a different ways to implement intelligent IDS, which classifies the normal traffic...
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.
Intrusion Detection: Supervised Machine Learning
Journal of Computing Science …, 2011
Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS).The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.
Intrusion Detection and Attack Classification using an Ensemble Approach
2020
The challenges to ensure safe and trusted communication of information between various organizations have increased multifold in recent past. Intrusion Detection Systems such as firewall, message encryption and other approaches are being employed with partial success, however the risks and chances of malicious intrusions are still posing a threat. We are proposing to make use of recent advancements in the field of machine learning to develop an intrusion detection system. In our work, the machine learning classifiers namely, random forest, decision table, multi-layer perceptron and naive bayes were used in an ensemble model showing a significant improvement in the overall accuracy. The proposed approach was implemented using a bench-marking dataset from KDDCup.
Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset
Future Internet
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that ...
2016
The emergence of new technologies in this dynamic information era has caused a tremendous increase in the rate at which data is being generated through interactive applications thereby increasing the movement of information and data on communication networks as individuals, organizations and business interact on a daily basis. Big Data is flooding our networks and storage devices stimulating a cause for concern in terms of processing, storage, access and security of large blocks of data in most networks. The facilitation of online research services is always under the risk of intruders and malicious activity. Most techniques used in today's Intrusion Detection Systems are not able to deal with the dynamic and complex nature of cyberattacks on computer networks. Over the years, Intrusion Detection Systems .Various methods have been developed by many researchers to detect intrusions aimed at networks as well as standalone devices which are based on machine learning algorithms, neu...
Intrusion Detection using Machine Learning Techniques: An Experimental Comparison
2021 International Congress of Advanced Technology and Engineering (ICOTEN)
Due to an exponential increase in the number of cyber-attacks, the need for improved Intrusion Detection Systems (IDS) is apparent than ever. In this regard, Machine Learning (ML) techniques are playing a pivotal role in the early classification of the attacks in case of intrusion detection within the system. However, due to the large number of algorithms available, the selection of the right method is a challenging task. To resolve this issue, this paper analyses some of the current state of the art intrusion detection methods and discusses their pros and cons. Further, a review of different ML methods is carried out with four methods showing to be the most suitable one for classifying attacks. Several algorithms are selected and investigated to evaluate the performance of IDS. These IDS classifies binary and multiclass attacks in terms of detecting whether or not the traffic has been considered as benign or an attack. The experimental results demonstrate that binary classification has greater consistency in their accuracy results which ranged from 0.9938 to 0.9977, while multiclass ranges from 0.9294 to 0.9983. However, it has been also observed that multiclass provides the best results with the algorithm k-Nearest neighbor giving an accuracy score of 0.9983 while the binary classification highest score is 0.9977 from Random Forest. The experimental results demonstrate that multiclass classification produces better performance in terms of intrusion detection by specifically differentiating between the attacks and allowing a more targeted response to an attack.
ARRAY, 2023
Intrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity of traditional intrusion detection systems (IDS) to identify unknown sophisticated threats is constrained by their reliance on signature-based detection. Approaches based on machine learning have shown promising results in identifying unknown malicious attacks. No learning algorithm-based model, however, is able to accurately and consistently detect all different kinds of attacks. Besides that, the existing models are tested for a specific dataset. In this research, a novel ensemble-based machine-learning technique for intrusion detection is presented. Numerous public datasets and multiple ensemble strategies, including Random Forest, Gradient Boosting, Adaboost, Gradient XGBoost, Bagging, and Simple Stacking, will be employed to evaluate the performance of the proposed approach. The most relevant features for the detection of intrusion are selected using correlation analysis, mutual information, and principal component analysis. Our research using different ensemble methods demonstrates that the proposed approach using the Random Forest technique outperforms existing approaches in terms of accuracy and FPR, typically exceeding 99% with better evaluation metrics like Precision, Recall, F1-score, Balanced Accuracy, Cohen's Kappa, etc. This strategy may be a useful tool for strengthening the safety of computer systems and networks against emerging cyber threats.
A Survey of Network Intrusion Detection Using Machine Learning Techniques
Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics, 2021
Nowadays, a huge amount of information flows daily on public and private computer networks. Since sensitive information has a high probability of being transmitted, there is an important need to protect networks from intrusions. Hence, adopting an intrusion detection system is imperative. As the frequency of sophisticated attacks has been increasing tremendously over the past years, machine learning approaches were introduced to identify intrusion patterns and prevent sophisticated attacks. This survey provides an up-to-date review of leading-edge techniques used by intrusion detection systems that rely on machine learning techniques. Moreover, it introduces important key machine learning concepts such as ensemble learning and feature selection that are applied to protect networks from unauthorized access and make networks and computers safer. The article then reviews signature, anomaly, and hybrid intrusion detection systems that apply machine learning techniques. It is observed that hybrid network intrusion detection system may be the most effective. Then, the article examines the characteristics of popular benchmark datasets for evaluating intrusion detection systems such as NSL-KDD, Kyoto 2006 +, and KDD Cup-'99 and performance metrics to appraise intrusion detection results. Finally, the article discusses research opportunities in the field of intrusion detection.