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intrusion detection system with Customized Machine Learning Techniques for NSL-KDD Dataset

Computers, Materials & Continua 2023年第12期77卷 4025-4054页

作者： Mohammed Zakariah Salman A.AlQahtani Abdulaziz M.Alawwad Abdullilah A.Alotaibi Department of Computer Science College of Computer and Information ScienceKing Saud UniversityRiyadh11495Saudi Arabia New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Engineering College of Computer and Information ScienceKing Saud UniversityRiyadh11495Saudi Arabia

Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based *** consuming time and resources,intrusive traffic hampers the efficient operation of network *** effective strategy for preventing,detecting,and mitigating intrusion incidents will increase productivity.A crucial element of secure network traffic is intrusion detection system(IDS).An IDS system may be host-based or network-based to monitor intrusive network *** unusual internet traffic has become a severe security risk for intelligent *** systems are negatively impacted by several attacks,which are slowing *** addition,networked communication anomalies and breaches must be detected using Machine Learning(ML).This paper uses the NSL-KDD data set to propose a novel IDS based on Artificial Neural Networks(ANNs).As a result,the ML model generalizes sufficiently to perform well on untried *** NSL-KDD dataset shall be utilized for both training and *** this paper,we present a custom ANN model architecture using the Keras open-source software *** specific arrangement of nodes and layers,along with the activation functions,enhances the model’s ability to capture intricate patterns in network *** performance of the ANN is carefully tested and evaluated,resulting in the identification of a maximum detection accuracy of 97.5%.We thoroughly compared our suggested model to industry-recognized benchmark methods,such as decision classifier combinations and ML classifiers like k-Nearest Neighbors(KNN),Deep Learning(DL),Support Vector Machine(SVM),Long Short-Term Memory(LSTM),Deep Neural Network(DNN),and *** is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all *** result underlines the effectiveness of the suggested methodology by demonstrating the ANN’s capacity to accurately assess the effectiveness of the developed strategy

关键词： Artificial neural networks intrusion detection system classification NSL-KDD dataset machine and deep-learning neural network

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intrusion detection system Based on an Intelligent Multilayer Model Using Machine Learning

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Journal of Artificial Intelligence and Technology 2024年第4期4卷 332-341页

作者： Ouafae El Aeraj Cherkaoui Leghris Laboratory of Mathematics Computer Science and ApplicationsFaculty of Sciences and TechniquesHassanⅡUniversity of CasablancaMohammedia 28806Morocco

With the rapid advent of information technology and social networking,the multiplication of connected devices further exposes users to the vulnerability of their personal *** growing interconnectedness increases the risk of online attacks,underlining the daily challenge of cybersecurity in the face of increasingly sophisticated *** in automatic software updates and the limited responsiveness of devices underline the need for an innovative approach to detecting intrusions and securing *** detection of intrusions within companies is essential to anticipate threats and respond rapidly to *** recommend the use of several tools and methods to counter malicious *** article introduces our innovative development of an automated model called Snort Support Vector Machine(SSVM)based on a hierarchical strategy organized in distinct *** model,automated by the joint use of Python and Shell,merges the efficiency of these languages to guarantee autonomous and resilient *** examining several intrusion detection and prevention systems,the first layer implements a selected system as the initial *** second layer uses machine learning to fill in the gaps in the initial ***,the third layer applies a global evaluation methodology,taking into account execution time,energy consumption,and physical resources in order to orchestrate the entire evaluation *** approach we propose appears to improve on other conventional intrusion detection systems by making the detection process more *** does this by reducing false positives and false negatives compared with existing models.

关键词： intrusion detection system support vector machines snort machine learning

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Novel Framework for an intrusion detection system Using Multiple Feature Selection Methods Based on Deep Learning

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Tsinghua Science and Technology 2024年第4期29卷 948-958页

作者： A.E.M.Eljialy Mohammed Yousuf Uddin Sultan Ahmad Department of Information systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost

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Intelligent intrusion detection system for Industrial Internet of Things Environment

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Computer systems Science & Engineering 2023年第2期44卷 1567-1582页

作者： R.Gopi R.Sheeba K.Anguraj T.Chelladurai Haya Mesfer Alshahrani Nadhem Nemri Tarek Lamoudan Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information systems College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Mathematics College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival *** classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and *** resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 *** CCSOA-OWKELM technique focuses on the design of feature selection with classiﬁcation approach to achieve minimum computation complex-ity and maximum detection *** CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with ***,the OWKELM technique is applied for the intrusion detection and classiﬁcation *** addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunﬂower optimization(SFO)*** utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better *** order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

关键词： intrusion detection system artiﬁcial intelligence machine learning industry 4.0 internet of things

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Augmenting IoT intrusion detection system Performance Using Deep Neural Network

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Computers, Materials & Continua 2023年第1期74卷 1351-1374页

作者： Nasir Sayed Muhammad Shoaib Waqas Ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science CECOS University of IT and Emerging SciencesPeshawar25000Pakistan Department of Electrical Engineering HITEC University TaxilaTaxila47080Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to ***,the rapid growth of IoT devices in homes increases the risk of *** detection systems(IDS)are commonly employed to prevent *** systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate *** have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been *** this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT ***,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 *** accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network *** also propose two baseline models to demonstrate the performance of the proposed ***,both models achieve high accuracy in detecting the majority of these nine attacks.

关键词： Internet of things intrusion detection system deep learning convolutional neural network supervised learning

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Internet of Things intrusion detection system Based on Convolutional Neural Network

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Computers, Materials & Continua 2023年第4期75卷 2119-2135页

作者： Jie Yin Yuxuan Shi Wen Deng Chang Yin Tiannan Wang Yuchen Song Tianyao Li Yicheng Li Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina

In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output ***, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time.

关键词： Internet of things intrusion detection system convolutional neural network federated learning

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Deep Transfer Learning Techniques in intrusion detection system-Internet of Vehicles: A State-of-the-Art Review

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Computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for intrusion detection systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

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An Intelligent SDN-IoT Enabled intrusion detection system for Healthcare systems Using a Hybrid Deep Learning and Machine Learning Approach

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China Communications 2024年第10期21卷 267-287页

作者： R Arthi S Krishnaveni Sherali Zeadally Computational Intelligence School of ComputingSRMISTKattankulathurChennaiIndia College of Communication and Information University of KentuckyUSA

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every ***,it is essential to make medical services connected to internet,available in every remote location during these ***,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical ***,services in the healthcare ecosystem need rapid,uninterrupted,and secure *** solution provided in this research addresses security concerns and services availability for patients with critical health in remote *** research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare *** propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare *** addition,this system can efficiently monitor connected devices and suspicious ***,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application *** experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

关键词： deep neural network healthcare intrusion detection system IoT machine learning software-defined networks

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A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity intrusion detection system

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Computers, Materials & Continua 2021年第2期66卷 1785-1798页

作者： Amir Haider Muhammad Adnan Khan Abdur Rehman Muhib Ur Rahman Hyung Seok Kim Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Electrical Engineering Polytechnique MontrealMontrealQC H3T 1J4Canada

In recent years,cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things(IoT)and the widespread development of computer infrastructure and *** is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to *** have worked on developing intrusion detection models that depend on machine learning(ML)methods to address these security *** intelligent intrusion detection device powered by data can exploit artificial intelligence(AI),and especially ML,***,we propose in this article an intrusion detection model based on a Real-Time Sequential Deep Extreme Learning Machine Cybersecurity intrusion detection system(RTS-DELM-CSIDS)security *** proposed model initially determines the rating of security aspects contributing to their significance and then develops a comprehensive intrusion detection framework focused on the essential ***,we investigated the feasibility of our proposed RTS-DELM-CSIDS framework by performing dataset evaluations and calculating accuracy parameters to *** experimental findings demonstrate that the RTS-DELM-CSIDS framework outperforms conventional ***,the proposed approach has not only research significance but also practical significance.

关键词： Security DELM intrusion detection system machine learning

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Network Security Enhanced with Deep Neural Network-Based intrusion detection system

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma S.Alrayes Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad Saad Alqurni Information systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

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