scholarly journals MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6578
Author(s):  
Ivan Vaccari ◽  
Giovanni Chiola ◽  
Maurizio Aiello ◽  
Maurizio Mongelli ◽  
Enrico Cambiaso
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Iot Devices ◽  
Definition Of

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

scholarly journals Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4372 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Detection System ◽  
Rapid Development ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Detection Systems ◽  
Iot Devices ◽  
Artificial Neural Network Ann ◽  
Feature Selection Approach

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

Detecting DDoS Attacks in IoT Environment

2021 ◽  
Vol 15 (2) ◽  
pp. 145-180
Author(s):  
Yasmine Labiod ◽  
Abdelaziz Amara Korba ◽  
Nacira Ghoualmi-Zine
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
False Positive Rate ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Ddos Attacks ◽  
High Detection Rate ◽  
Detection Techniques ◽  
Detection Model

With the great potential of internet of things (IoT) infrastructure in different domains, cyber-attacks are also rising commensurately. Distributed denials of service (DDoS) attacks are one of the cyber security threats. This paper will focus on DDoS attacks by adding the design of an intrusion detection system (IDS) tailored to IoT systems. Moreover, machine learning techniques will be investigated to distinguish the data representing flows of network traffic, which include both normal and DDoS traffic. In addition, these techniques will be used to help make a refined detection model for identifying different types of DDoS attacks. Furthermore, the performance of machine learning-based proposed solution is validated using N-BaIoT dataset and compared through different evaluation metrics. The experimental results show that the proposed IDS not only detects DDoS attacks types but also has a high detection rate and low false positive rate, which argues the usefulness of the proposed approach in comparison with several existing DDoS attacks detection techniques.

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.

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.

PREDICTION OF NETWORK ATTACKS USING MACHINE LEARNING TECHNIQUES

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Dr. G. Umarani Srikanth ◽  
Priyadharsini S.
Keyword(s):  
Machine Learning ◽  
Computer Network ◽  
Detection System ◽  
Cyber Attacks ◽  
Working Environment ◽  
Machine Learning Techniques ◽  
Sensitive Data ◽  
Security Breaches ◽  
The People ◽  
Learning Techniques

The networked systems become more and more pervasive and businesses still acquire a lot of sensitive data online, so that the quantity and class of cyber-attacks and network security breaches has risen dramatically. There are also instances that so many volumes of data are hacked even without the knowledge of the people concerned. So far setting an Intrusion Detection System (IDS), it is obvious to set the true working environment to model the possibilities of attacks. Therefore, it is imperative to design a software that will be able to identify network intrusions, in order to protect a computer network from the unknown users. For overcoming this challenge, it is essential to predict whether the connection is targeted or not from KDDCup99 dataset utilizing machine learning techniques. The objective of this work is to investigate machine learning based algorithms for enhancing packet connection transfers forecasting using ensemble learning voting classifier techniques. It is proposed to deploy AI-based technique to precisely anticipate the DOS, R2L, U2R, Probe and large assaults. Results showed that the viability of the proposed AI calculation strategy can be contrasted and the best exactness with accuracy, Recall and F1 Score.

scholarly journals A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

2020 ◽  
Vol 10 (3) ◽  
pp. 2701
Author(s):  
Safaa Laqtib ◽  
Khalid El Yassini ◽  
Moulay Lahcen Hasnaoui
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Detection Systems ◽  
Learning Techniques

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.

Anomaly Activities Detection System in Critical Water SCADA Infrastructure Using Machine Learning Techniques

2019 ◽  
Vol 28 (1) ◽  
pp. 343-384 ◽  
Author(s):  
Gamal Eldin I. Selim ◽  
EZZ El-Din Hemdan ◽  
Ahmed M. Shehata ◽  
Nawal A. El-Fishawy
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

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