scholarly journals Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1771
Author(s):  
Muhammad Ashfaq Khan ◽  
Juntae Kim
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Detection System ◽  
Rapid Development ◽  
Research Problem ◽  
Vital Role ◽  
Attack Detection ◽  
Detection Methods ◽  
Malicious Attacks

Recently, due to the rapid development and remarkable result of deep learning (DL) and machine learning (ML) approaches in various domains for several long-standing artificial intelligence (AI) tasks, there has an extreme interest in applying toward network security too. Nowadays, in the information communication technology (ICT) era, the intrusion detection (ID) system has the great potential to be the frontier of security against cyberattacks and plays a vital role in achieving network infrastructure and resources. Conventional ID systems are not strong enough to detect advanced malicious threats. Heterogeneity is one of the important features of big data. Thus, designing an efficient ID system using a heterogeneous dataset is a massive research problem. There are several ID datasets openly existing for more research by the cybersecurity researcher community. However, no existing research has shown a detailed performance evaluation of several ML methods on various publicly available ID datasets. Due to the dynamic nature of malicious attacks with continuously changing attack detection methods, ID datasets are available publicly and are updated systematically. In this research, spark MLlib (machine learning library)-based robust classical ML classifiers for anomaly detection and state of the art DL, such as the convolutional-auto encoder (Conv-AE) for misuse attack, is used to develop an efficient and intelligent ID system to detect and classify unpredictable malicious attacks. To measure the effectiveness of our proposed ID system, we have used several important performance metrics, such as FAR, DR, and accuracy, while experiments are conducted on the publicly existing dataset, specifically the contemporary heterogeneous CSE-CIC-IDS2018 dataset.

scholarly journals A Survey on Intrusion Detection System using Machine Learning and Deep Learning

2019 ◽  
pp. 264-270
Author(s):  
Er. Hemavati ◽  
Aparna R
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Performance Metrics ◽  
Detection System ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Network Applications ◽  
Important Challenge ◽  
Iot Devices

As we know internet of Things (IoT) is one of the fastest growing paradigm which is composed of Internet and different physical devices with different domains or the smart applications like home automation, business automation applications, health and environmental monitoring applications. The dependency on IOT devices is increasing day by day with our daily activities, which leads to most important challenge for security. Since having a better monitoring system for better security is a need. From more than two decades the concept or the frame work called IDS (Intrusion detection system) is playing important role for detecting the attacks in the network. Since the network attacks are not fixed in nature, a new type of attacks are happening on the network applications. There are many traditional IDS techniques are available but they are complex to apply. Since machine learning is one of the important area which is achieving good results in many applications. In this paper we study about the different machine learning techniques used till now and the methodology for the attack detection and the validation strategy. We will also discuss about the performance metrics.

scholarly journals A Multifractal Analysis and Machine Learning Based Intrusion Detection System with an Application in a UAS/RADAR System

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 21
Author(s):  
Ruohao Zhang ◽  
Jean-Philippe Condomines ◽  
Emmanuel Lochin
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Multifractal Analysis ◽  
Detection System ◽  
Rapid Development ◽  
Mobile Network ◽  
Attack Detection ◽  
Unmanned Aerial Systems ◽  
Novel Algorithm

The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unmanned aerial systems (UAS), intrusion detection systems (IDS) based on multifractal (MF) spectral analysis have been used to provide accurate MF spectrum estimations of network traffic. Such an estimation is then used to detect and characterize flooding anomalies that can be observed in an unmanned aerial vehicle (UAV) network. However, the previous contributions have lacked the consideration of other types of network intrusions commonly observed in UAS networks, such as the man in the middle attack (MITM). In this work, this promising methodology has been accommodated to detect a spoofing attack within a UAS. This methodology highlights a robust approach in terms of false positive performance in detecting intrusions in a UAS location reporting system.

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.

scholarly journals Assessment of Machine Learning Algorithms for Network Intrusion Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1667-1671
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Network Intrusion Detection ◽  
Learning Models ◽  
Network Intrusion ◽  
Tree Classifier ◽  
Machine Learning Models

A Network Intrusion Detection System (NIDS) is a framework to identify network interruptions as well as abuse by checking network traffic movement and classifying it as either typical or strange. Numerous Intrusion Detection Systems have been implemented using simulated datasets like KDD’99 intrusion dataset but none of them uses a real time dataset. The proposed work performs and assesses tests to overview distinctive machine learning models reliant on KDD’99 intrusion dataset and an ongoing created dataset. The machine learning models achieved to compute required performance metrics so as to assess the chosen classifiers. The emphasis was on the accuracy metric so as to improve the recognition pace of the interruption identification framework. The actualized calculations showed that the decision tree classifier accomplished the most noteworthy estimation of accuracy while the logistic regression classifier has accomplished the least estimation of exactness for both of the datasets utilized.

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 Classification model for accuracy and intrusion detection using machine learning approach

2021 ◽  
Vol 7 ◽  
pp. e437
Author(s):  
Arushi Agarwal ◽  
Purushottam Sharma ◽  
Mohammed Alshehri ◽  
Ahmed A. Mohamed ◽  
Osama Alfarraj
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Detection System ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor

In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.

Application of Machine Learning Approaches in Intrusion Detection System

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Zena Abdulmunim Aziz ◽  
◽  
Adnan Mohsin Abdulazeez ◽  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Naive Bayes ◽  
Detection System ◽  
Rapid Development ◽  
Naïve Bayes ◽  
The Internet ◽  
Learning Approaches ◽  
Data Set ◽  
Supporting Layer

The rapid development of technology reveals several safety concerns for making life more straightforward. The advance of the Internet over the years has increased the number of attacks on the Internet. The IDS is one supporting layer for data protection. Intrusion Detection Systems (IDS) offer a healthy market climate and prevent misgivings in the network. Recently, IDS has been used to recognize and distinguish safety risks using Machine Learning (ML). This paper proposed a comparative analysis of the different ML algorithms used in IDS and aimed to identify intrusions with SVM, J48, and Naive Bayes. Intrusion is also classified. Work with the KDD-CUP data set, and their performance has been checked with the WEKA software. A comparison of techniques such as J48, SVM, and Naïve Bayes showed that the accuracy of j48 is the higher one which was (99.96%).

scholarly journals A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System

2020 ◽  
Vol 1 (2) ◽  
pp. 15-25
Author(s):  
Suresh Adithya Nallamuthu ◽  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Performance Analysis ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Attack Detection ◽  
Network Systems

The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-security CIC-IDS 2017 dataset is used for the evaluation of performance analysis. Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model outperforms all these classifying techniques in every performance metric, like accuracy, precision, recall, and detection rate. Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.

scholarly journals Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Ahmad Azhari ◽  
Arif Wirawan Muhammad ◽  
Cik Feresa Mohd Foozy
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Policy Development ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Ddos Attacks ◽  
Detection Techniques

Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity.  DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the  deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development.  According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%.

scholarly journals Performance Analysis of Proposed Hybrid Machine Learning Model for Efficient Intrusion Detection

2020 ◽  
Vol 9 (6) ◽  
pp. 904-911
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Hybrid Approach ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Detection Techniques ◽  
Networking Technologies

At present networking technologies has provided a better medium for people to communicate and exchange information on the internet. This is the reason in the last ten years the number of internet users has increased exponentially. The high-end use of network technology and the internet has also presented many security problems. Many intrusion detection techniques are proposed in combination with KDD99, NSL-KDD datasets. But there are some limitations of available datasets. Intrusion detection using machine learning algorithms makes the detection system more accurate and fast. So in this paper, a new hybrid approach of machine learning combining feature selection and classification algorithms is presented. The model is examined with the UNSW NB15 intrusion dataset. The proposed model has achieved better accuracy rate and attack detection also improved while the false attack rate is reduced. The model is also successful to accurately classify rare cyber attacks like worms, backdoor, and shellcode.

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