Machine Learning techniques for Behavioral Feature Selection in Network Intrusion Detection Systems

2021 ◽  
Author(s):  
Vicente Martinez ◽  
Rodrigo Salas ◽  
Oliver Tessini ◽  
Romina Torres
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Network Intrusion Detection Systems

scholarly journals On evaluation of Network Intrusion Detection Systems: Statistical analysis of CIDDS-001 dataset using Machine Learning Techniques

2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Network Intrusion Detection Systems ◽  
Secure Networks

In the era of digital revolution, a huge amount of data is being generated from different networks on a daily basis. Security of this data is of utmost importance. Intrusion Detection Systems are found to be one the best solutions towards detecting intrusions. Network Intrusion Detection Systems are employed as a defence system to secure networks. Various techniques for the effective development of these defence systems have been proposed in the literature. However, the research on the development of datasets used for training and testing purpose of such defence systems is equally concerned. Better datasets improve the online and offline intrusion detection capability of detection model. Benchmark datasets like KDD 99 and NSL-KDD cup 99 obsolete and do not contain network traces of modern attacks like Denial of Service, hence are unsuitable for the evaluation purpose. In this work, a detailed analysis of CIDDS-001 dataset has been done and presented. We have used different well-known machine learning techniques for analysing the complexity of the dataset. Eminent evaluation metrics including Detection Rate, Accuracy, False Positive Rate, Kappa statistics, Root mean squared error have been used to show the performance of employed machine learning techniques.

scholarly journals On evaluation of Network Intrusion Detection Systems: Statistical analysis of CIDDS-001 dataset using Machine Learning Techniques

2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Network Intrusion Detection Systems ◽  
Secure Networks

In the era of digital revolution, a huge amount of data is being generated from different networks on a daily basis. Security of this data is of utmost importance. Intrusion Detection Systems are found to be one the best solutions towards detecting intrusions. Network Intrusion Detection Systems are employed as a defence system to secure networks. Various techniques for the effective development of these defence systems have been proposed in the literature. However, the research on the development of datasets used for training and testing purpose of such defence systems is equally concerned. Better datasets improve the online and offline intrusion detection capability of detection model. Benchmark datasets like KDD 99 and NSL-KDD cup 99 obsolete and do not contain network traces of modern attacks like Denial of Service, hence are unsuitable for the evaluation purpose. In this work, a detailed analysis of CIDDS-001 dataset has been done and presented. We have used different well-known machine learning techniques for analysing the complexity of the dataset. Eminent evaluation metrics including Detection Rate, Accuracy, False Positive Rate, Kappa statistics, Root mean squared error have been used to show the performance of employed machine learning techniques.

scholarly journals Ensemble-Based Online Machine Learning Algorithms for Network Intrusion Detection Systems Using Streaming Data

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 315
Author(s):  
Nathan Martindale ◽  
Muhammad Ismail ◽  
Douglas A. Talbert
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Intrusion Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems

As new cyberattacks are launched against systems and networks on a daily basis, the ability for network intrusion detection systems to operate efficiently in the big data era has become critically important, particularly as more low-power Internet-of-Things (IoT) devices enter the market. This has motivated research in applying machine learning algorithms that can operate on streams of data, trained online or “live” on only a small amount of data kept in memory at a time, as opposed to the more classical approaches that are trained solely offline on all of the data at once. In this context, one important concept from machine learning for improving detection performance is the idea of “ensembles”, where a collection of machine learning algorithms are combined to compensate for their individual limitations and produce an overall superior algorithm. Unfortunately, existing research lacks proper performance comparison between homogeneous and heterogeneous online ensembles. Hence, this paper investigates several homogeneous and heterogeneous ensembles, proposes three novel online heterogeneous ensembles for intrusion detection, and compares their performance accuracy, run-time complexity, and response to concept drifts. Out of the proposed novel online ensembles, the heterogeneous ensemble consisting of an adaptive random forest of Hoeffding Trees combined with a Hoeffding Adaptive Tree performed the best, by dealing with concept drift in the most effective way. While this scheme is less accurate than a larger size adaptive random forest, it offered a marginally better run-time, which is beneficial for online training.

scholarly journals Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches

2020 ◽  
Vol 10 (5) ◽  
pp. 1775 ◽  
Author(s):  
Roberto Magán-Carrión ◽  
Daniel Urda ◽  
Ignacio Díaz-Cano ◽  
Bernabé Dorronsoro
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Attack Detection ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Learning Approaches ◽  
Network Attack ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems

Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. To counteract against malicious behaviours and preserve essential security services, Network Intrusion Detection Systems (NIDSs) are the most widely used defence line in communications networks. Nevertheless, there is no standard methodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioning crucial steps regarding NIDSs validation that make their comparison hard or even impossible. This work firstly includes a comprehensive study of recent NIDSs based on machine learning approaches, concluding that almost all of them do not accomplish with what authors of this paper consider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structured methodology is proposed and assessed on the UGR’16 dataset to test its suitability for addressing network attack detection problems. The guideline and steps recommended will definitively help the research community to fairly assess NIDSs, although the definitive framework is not a trivial task and, therefore, some extra effort should still be made to improve its understandability and usability further.

scholarly journals Feature Selection Method based on Chaotic Salp Swarm Algorithm and Extreme Learning Machine for Network Intrusion Detection Systems

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 626-640
Author(s):  
Rana Nazhan Hadi ◽  
Dr. Rasha Orban Mahmoud ◽  
Dr. Adly S. Tag Eldien
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Learning Machine

Network Intrusion Detection Systems (IDSs) have been widely used to monitor and manage network connections and prevent unauthorized connections. Machine learning models have been utilized to classify the connections into normal connections or attack connections based on the users' behavior. One of the most common issues facing the IDSs is the detection system's low classification accuracy and high dimensionality in the feature selection process. However, the feature selection methods are usually used to decrease the datasets' redundancy and enhance the classification performance. In this paper, a Chaotic Salp Swarm Algorithm (CSSA) was integrated with the Extreme Learning Machine (ELM) classifier to select the most relevant subset of features and decrease the dimensionality of a dataset. Each Salp in the population was represented in a binary form, where 1 represented a selected feature, while 0 represented a removed feature. The proposed feature selection algorithm was evaluated based on NSL-KDD dataset, which consists of 41 features. The results were compared with others and have shown that the proposed algorithm succeeded in achieving classification accuracy up to 97.814% and minimized the number of selected features.

Sign in / Sign up

Export Citation Format

Share Document