scholarly journals Machine Learning based Intrusion Detection for Cyber-Security in IoT Networks

2021 ◽  
Vol 297 ◽  
pp. 01057
Author(s):  
Amine Khatib ◽  
Mohamed Hamlich ◽  
Denis Hamad
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Large Scale ◽  
Performance Comparison ◽  
Cyber Attacks ◽  
Network Intrusion ◽  
Critical Issues ◽  
Imbalanced Classes ◽  
Use Of Internet

IoT network is a promising technology, IoT implementation is growing rapidly but cybersecurity is still a loophole, detection of attacks in IOT infrastructures is a growing concern in the field of IoT. With the increased use of Internet of Things in different areas, cyber-attacks are also increasing proportionately and can cause failures in the system. IDS becomes the leading security solution. Anomaly based network intrusion detection (IDS) detection plays a major role in protecting networks against various malicious activities. Improving the security of loT networks has become one of the most critical issues. This is due to the large-scale development and deployment of loT devices and the insufficiency of Intrusion Detection Systems (IDS) to be deployed for the use of special purpose networks. In this article, the performance of several machine learning models has been compared to accurately predict attacks on IoT systems, the case of imbalanced classes was subsequently treated using the SMOTE technique. The Nystrom based kernel SVM is the first time used to detect attacks in the IoT network and the results are promising. The evaluation metrics used in the performance comparison are accuracy, precision, recall, f1 score, and auc-roc curve.

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

scholarly journals Improving the Performance of Machine Learning-Based Network Intrusion Detection Systems on the UNSW-NB15 Dataset

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Soulaiman Moualla ◽  
Khaldoun Khorzom ◽  
Assef Jafar
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
Supervised Machine Learning ◽  
Intrusion Detection Systems ◽  
Detection Rates ◽  
Detection Systems ◽  
Network Intrusion ◽  
Imbalanced Classes ◽  
Learning Machine

Networks are exposed to an increasing number of cyberattacks due to their vulnerabilities. So, cybersecurity strives to make networks as safe as possible, by introducing defense systems to detect any suspicious activities. However, firewalls and classical intrusion detection systems (IDSs) suffer from continuous updating of their defined databases to detect threats. The new directions of the IDSs aim to leverage the machine learning models to design more robust systems with higher detection rates and lower false alarm rates. This research presents a novel network IDS, which plays an important role in network security and faces the current cyberattacks on networks using the UNSW-NB15 dataset benchmark. Our proposed system is a dynamically scalable multiclass machine learning-based network IDS. It consists of several stages based on supervised machine learning. It starts with the Synthetic Minority Oversampling Technique (SMOTE) method to solve the imbalanced classes problem in the dataset and then selects the important features for each class existing in the dataset by the Gini Impurity criterion using the Extremely Randomized Trees Classifier (Extra Trees Classifier). After that, a pretrained extreme learning machine (ELM) model is responsible for detecting the attacks separately, “One-Versus-All” as a binary classifier for each of them. Finally, the ELM classifier outputs become the inputs to a fully connected layer in order to learn from all their combinations, followed by a logistic regression layer to make soft decisions for all classes. Results show that our proposed system performs better than related works in terms of accuracy, false alarm rate, Receiver Operating Characteristic (ROC), and Precision-Recall Curves (PRCs).

scholarly journals A GAN and Feature Selection-Based Oversampling Technique for Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaodong Liu ◽  
Tong Li ◽  
Runzi Zhang ◽  
Di Wu ◽  
Yongheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Large Scale ◽  
High Rate ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
False Alarms ◽  
Security Issues

In recent years, there have been numerous cyber security issues that have caused considerable damage to the society. The development of efficient and reliable Intrusion Detection Systems (IDSs) is an effective countermeasure against the growing cyber threats. In modern high-bandwidth, large-scale network environments, traditional IDSs suffer from a high rate of missed and false alarms. Researchers have introduced machine learning techniques into intrusion detection with good results. However, due to the scarcity of attack data, such methods’ training sets are usually unbalanced, affecting the analysis performance. In this paper, we survey and analyze the design principles and shortcomings of existing oversampling methods. Based on the findings, we take the perspective of imbalance and high dimensionality of datasets in the field of intrusion detection and propose an oversampling technique based on Generative Adversarial Networks (GAN) and feature selection. Specifically, we model the complex high-dimensional distribution of attacks based on Gradient Penalty Wasserstein GAN (WGAN-GP) to generate additional attack samples. We then select a subset of features representing the entire dataset based on analysis of variance, ultimately generating a rebalanced low-dimensional dataset for machine learning training. To evaluate the effectiveness of our proposal, we conducted experiments based on the NSL-KDD, UNSW-NB15, and CICIDS-2017 datasets. The experimental results show that our method can effectively improve the detection performance of machine learning models and outperform the baselines.

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>

Network Intrusion Detection in Internet of Things (IoT)

2021 ◽  
Vol 8 (1) ◽  
pp. 49-65
Author(s):  
Winfred Yaokumah ◽  
Richard Nunoo Clottey ◽  
Justice Kwame Appati
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Future Direction ◽  
Public Datasets ◽  
Private Datasets

The open nature of the internet of things network makes it vulnerable to cyber-attacks. Intrusion detection systems aid in detecting and preventing such attacks. This paper offered a systematic review of studies on intrusion detection in IoT, focusing on metrics, methods, datasets, and attack types. This review used 33 network intrusion detection papers in 31 journals and 2 conference proceedings. The results revealed that the majority of the studies used generated or private datasets. Machine learning (ML)-based methods (85%) were used in the studies, while the rest used statistical methods. Eight categories of metrics were identified as prominent in evaluating IoT performance, and 94.9% of the ML-based methods employed average detection rate. Moreover, over 20 attacks on IoT networks were detected, with denial of service (DoS) and sinkhole being the majority. Based on the review, the future direction of research should focus on using public datasets, machine learning-based methods, and metrics such as resource consumption, energy consumption, and power consumption.

scholarly journals Machine Learning Cybersecurity Adoption in Small and Medium Enterprises in Developed Countries

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 150
Author(s):  
Nisha Rawindaran ◽  
Ambikesh Jayal ◽  
Edmond Prakash
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Small And Medium Enterprises ◽  
Developed Countries ◽  
Cyber Attacks ◽  
Primary Data ◽  
Network Intrusion ◽  
The Uk ◽  
Medium Enterprises ◽  
Future Path

In many developed countries, the usage of artificial intelligence (AI) and machine learning (ML) has become important in paving the future path in how data is managed and secured in the small and medium enterprises (SMEs) sector. SMEs in these developed countries have created their own cyber regimes around AI and ML. This knowledge is tested daily in how these countries’ SMEs run their businesses and identify threats and attacks, based on the support structure of the individual country. Based on recent changes to the UK General Data Protection Regulation (GDPR), Brexit, and ISO standards requirements, machine learning cybersecurity (MLCS) adoption in the UK SME market has become prevalent and a good example to lean on, amongst other developed nations. Whilst MLCS has been successfully applied in many applications, including network intrusion detection systems (NIDs) worldwide, there is still a gap in the rate of adoption of MLCS techniques for UK SMEs. Other developed countries such as Spain and Australia also fall into this category, and similarities and differences to MLCS adoptions are discussed. Applications of how MLCS is applied within these SME industries are also explored. The paper investigates, using quantitative and qualitative methods, the challenges to adopting MLCS in the SME ecosystem, and how operations are managed to promote business growth. Much like security guards and policing in the real world, the virtual world is now calling on MLCS techniques to be embedded like secret service covert operations to protect data being distributed by the millions into cyberspace. This paper will use existing global research from multiple disciplines to identify gaps and opportunities for UK SME small business cyber security. This paper will also highlight barriers and reasons for low adoption rates of MLCS in SMEs and compare success stories of larger companies implementing MLCS. The methodology uses structured quantitative and qualitative survey questionnaires, distributed across an extensive participation pool directed to the SMEs’ management and technical and non-technical professionals using stratify methods. Based on the analysis and findings, this study reveals that from the primary data obtained, SMEs have the appropriate cybersecurity packages in place but are not fully aware of their potential. Secondary data collection was run in parallel to better understand how these barriers and challenges emerged, and why the rate of adoption of MLCS was very low. The paper draws the conclusion that help through government policies and processes coupled together with collaboration could minimize cyber threats in combatting hackers and malicious actors in trying to stay ahead of the game. These aspirations can be reached by ensuring that those involved have been well trained and understand the importance of communication when applying appropriate safety processes and procedures. This paper also highlights important funding gaps that could help raise cyber security awareness in the form of grants, subsidies, and financial assistance through various public sector policies and training. Lastly, SMEs’ lack of understanding of risks and impacts of cybercrime could lead to conflicting messages between cross-company IT and cybersecurity rules. Trying to find the right balance between this risk and impact, versus productivity impact and costs, could lead to UK SMES getting over these hurdles in this cyberspace in the quest for promoting the usage of MLCS. UK and Wales governments can use the research conducted in this paper to inform and adapt their policies to help UK SMEs become more secure from cyber-attacks and compare them to other developed countries also on the same future path.

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 Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Yu ◽  
Jun Long ◽  
Zhiping Cai
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
High Performance ◽  
Large Scale ◽  
Network Intrusion Detection ◽  
Learning Approaches ◽  
Detection Techniques ◽  
Feature Representations ◽  
Detection Systems ◽  
Network Intrusion

Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs). It is quite potential and promising to apply our model in the large-scale and real-world network environments.

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