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

An Intelligent Network Intrusion Detection System Based on Multi-Modal Support Vector Machines

2013 ◽  
Vol 7 (4) ◽  
pp. 37-52
Author(s):  
Srinivasa K G
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Intelligent Network ◽  
Statistical Machine Learning ◽  
High Detection Rate ◽  
Network Intrusion

Increase in the number of network based transactions for both personal and professional use has made network security gain a significant and indispensable status. The possible attacks that an Intrusion Detection System (IDS) has to tackle can be of an existing type or of an entirely new type. The challenge for researchers is to develop an intelligent IDS which can detect new attacks as efficiently as they detect known ones. Intrusion Detection Systems are rendered intelligent by employing machine learning techniques. In this paper we present a statistical machine learning approach to the IDS using the Support Vector Machine (SVM). Unike conventional SVMs this paper describes a milti model approach which makes use of an extra layer over the existing SVM. The network traffic is modeled into connections based on protocols at various network layers. These connection statistics are given as input to SVM which in turn plots each input vector. The new attacks are identified by plotting them with respect to the trained system. The experimental results demonstrate the lower execution time of the proposed system with high detection rate and low false positive number. The 1999 DARPA IDS dataset is used as the evaluation dataset for both training and testing. The proposed system, SVM NIDS is bench marked with SNORT (Roesch, M. 1999), an open source IDS.

A Review on Network Intrusion Detection System Using Machine Learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bello Nazifi Kagara ◽  
Maheyzah Md Siraj
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Current Trend ◽  
Holistic Approach ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Network Intrusion ◽  
Daunting Task

The quality or state of being secure is the crucial concern of our daily life usage of any network. However, with the rapid breakthrough in network technology, attacks are becoming more trailblazing than defenses. It is a daunting task to design an effective and reliable intrusion detection system (IDS), while maintaining minimal complexity. The concept of machine learning is considered an important method used in intrusion detection systems to detect irregular network traffic activities. The use of machine learning is the current trend in developing IDS in order to mitigate false positives (FP) and False Negatives (FN) in the anomalous IDS. This paper targets to present a holistic approach to intrusion detection system and the popular machine learning techniques applied on IDS systems, bearing In mind the need to help research scholars in this continuous burgeoning field of Intrusion detection (ID).

scholarly journals Network Intrusion Detection and Measuring the Data Set Performance by Machine Learning Technique (MLT)

2019 ◽  
Vol 8 (4) ◽  
pp. 11806-11809
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Computer Network ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Traffic Information ◽  
Data Set ◽  
Identification Rate ◽  
Network Intrusion ◽  
Learning Techniques

Intrusion Detection System (IDS) is the most mainstream approach to protect a computer network from different malicious activities to identify an intrusion. There have been a lot of attempts towards more exceptional performance specifically in IDSs which depends on Data Mining (DM) and Machine Learning Techniques (MLT). Though there is a destructive issue in that available assessment, DataSet (DS), called KDD DS, can't reflect current network circumstances and the most recent attack situations. As far as we could know, there is no possible assessment DS. We present a novel evaluation DS in this paper, called Kyoto, based on the 5 years of actual traffic information, which derived from different sorts of honey pots. This Kyoto DS is utilized for testing and assessing distinctive MLT has examined in this work. The attention was on unprocessed measurements True +ve (TrPo), False +ve (FaPo), True – ve (TrNa), and False – ve (FaNa) to assess execution and to improve the identification rate of IDS.

An intrusion detection system for health-care system using machine and deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sagar Pande ◽  
Aditya Khamparia ◽  
Deepak Gupta
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Deep Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Learning Techniques

Purpose One of the important key components of health care–based system is a reliable intrusion detection system. Traditional techniques are not adequate to handle complex data. Also, the diversified intrusion techniques cannot meet current network requirements. Not only the data is getting increased but also the attacks are increasing very rapidly. Deep learning and machine learning techniques are very trending in the area of research in the area of network security. A lot of work has been done in this area by still evolutionary algorithms along with machine learning is very rarely explored. The purpose of this study is to provide novel deep learning framework for the detection of attacks. Design/methodology/approach In this paper, novel deep learning is the framework is proposed for the detection of attacks. Also, a comparison of machine learning and deep learning algorithms is provided. Findings The obtained results are more than 99% for both the data sets. Research limitations/implications The diversified intrusion techniques cannot meet current network requirements. Practical implications The data is getting increased but also the attacks are increasing very rapidly. Social implications Deep learning and machine learning techniques are very trending in the area of research in the area of network security. Originality/value Novel deep learning is the framework is proposed for the detection of attacks.

scholarly journals A Framework for Generating Evasion Attacks for Machine Learning based Network Intrusion Detection Systems

2021 ◽  
Author(s):  
Raymond Mogg ◽  
Simon Enoch ◽  
Dong Seong Kim
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Vital Role ◽  
Cyber Attacks ◽  
Algorithm Performance ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Learning Technique

<p>Intrusion Detection System (IDS) plays a vital role in detecting anomalies and cyber-attacks in networked systems. However, sophisticated attackers can manipulate the IDS’ attacks samples to evade possible detection. In this paper, we present a network-based IDS and investigate the viability of generating interpretable evasion attacks against the IDS through the application of a machine learning technique and an evolutionary algorithm. We employ a genetic algorithm to generate optimal attack features for certain attack categories, which are evaluated against a decision tree-based IDS in terms of their fitness measurements. To demonstrate the feasibility of our approach, we perform experiments based on the NSL-KDD dataset and analyze the algorithm performance. </p> <p> </p>

scholarly journals A Framework for Generating Evasion Attacks for Machine Learning based Network Intrusion Detection Systems

2021 ◽  
Author(s):  
Raymond Mogg ◽  
Simon Enoch ◽  
Dong Seong Kim
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Vital Role ◽  
Cyber Attacks ◽  
Algorithm Performance ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Learning Technique

<p>Intrusion Detection System (IDS) plays a vital role in detecting anomalies and cyber-attacks in networked systems. However, sophisticated attackers can manipulate the IDS’ attacks samples to evade possible detection. In this paper, we present a network-based IDS and investigate the viability of generating interpretable evasion attacks against the IDS through the application of a machine learning technique and an evolutionary algorithm. We employ a genetic algorithm to generate optimal attack features for certain attack categories, which are evaluated against a decision tree-based IDS in terms of their fitness measurements. To demonstrate the feasibility of our approach, we perform experiments based on the NSL-KDD dataset and analyze the algorithm performance. </p> <p> </p>

scholarly journals Anomaly based Intrusion Detection System using Machine Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 255-260
Author(s):  
Akshat Runwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
High Detection Rate ◽  
Network Intrusion ◽  
Average Accuracy

Abstract: 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. Keywords: Intrusion Detection System, Anomaly Detection, Machine Learning, Random Forest, Network Security

Machine Learning Based Intrusion Detection System

2021 ◽  
pp. 140-149
Author(s):  
Ashish Pandey ◽  
Neelendra Badal
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Passive Network

Security is one of the fundamental issues for both computer systems and computer networks. Intrusion detection system (IDS) is a crucial tool in the field of network security. There are a lot of scopes for research in this pervasive field. Intrusion detection systems are designed to uncover both known and unknown attacks. There are many methods used in intrusion detection system to guard computers and networks from attacks. These attacks can be active or passive, network based or host based, or any combination of it. Current research uses machine learning techniques to make intrusion detection systems more effective against any kind of attack. This survey examines designing methodology of intrusion detection system and its classification types. It also reviews the trend of machine learning techniques used from past decade. Related studies comprise performance of various classifiers on KDDCUP99 and NSL-KDD dataset.

Sign in / Sign up

Export Citation Format

Share Document