scholarly journals A Comprehensive Analysis and Solution of Cyber Attacks using Machine Learning Techniques

2020 ◽  
Vol 8 (12s3) ◽  
pp. 70-74
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Computer Network ◽  
Modern Society ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Human Skills

Cyber security is a major problem of modern society so that Vulnerabilities of computer Network is become easy with the help of technologies and human skills. Now day’s difference type of attacks occurred for example DOS attack, Probing, R2U, R2L virus, port scans, buffer overflow, CGI Attack and flooding etc. We need a platform where a system can be developed for recognition and prevention of these attacks. In This paper, most of the latest methods are summarised to implement IDS for cyber security. Intrusion Detection Systems is a most suitable solution for cyber attacks. Machine learning based Intrusion Detection Systems have high accuracy, in rapidly changing environment. This paper discusses which type of ML techniques has low accuracy, so it explore some research area for researcher.

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 Machine Learning confronted with the operational constraints of detection systems

2019 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Sridarala Ramu ◽  
Daniel Osaku
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Academic Research ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Techniques ◽  
High Level ◽  
Operational Constraints

Intrusion detection systems, traditionally based on signatures, have not escaped the recent appeal of machine learning techniques. While the results presented in academic research articles are often excellent, security experts still have many reservations about the use of Machine Learning in intrusion detection systems. They generally fear an inadequacy of these techniques to operational constraints, in particular because of a high level of expertise required, or a large number of false positives. In this article, we show that Machine Learning can be compatible with the operational constraints of detection systems. We explain how to build a detection model and present good practices to validate it before it goes into production. The methodology is illustrated by a case study on the detection of malicious PDF files and we offer a free tool, SecuML, to implement it.

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 Machine Learning Techniques for Network Intrusion Detection System (NIDS): A Survey

2021 ◽  
Vol 9 (12) ◽  
pp. 1477-1483
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Vital Role ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Network Intrusion

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

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