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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 traffic.By consuming time and resources,intrusive traffic hampers the efficient operation of network infrastructure.An 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 activity.Finding unusual internet traffic has become a severe security risk for intelligent devices.These systems are negatively impacted by several attacks,which are slowing computation.In 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 data.The NSL-KDD dataset shall be utilized for both training and testing.In this paper,we present a custom ANN model architecture using the Keras open-source software package.The specific arrangement of nodes and layers,along with the activation functions,enhances the model’s ability to capture intricate patterns in network data.The 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 ANN.It is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all evaluations.This result underlines the effectiveness of the suggested methodology by demonstrating the ANN’s capacity to accurately assess the effectiveness of the developed strategy in identifying and categorizing instances of ne

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

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Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

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Cybersecurity 2022年第2期5卷 119-140页

作者： Raisa Abedin Disha Sajjad Waheed Department of Information and Communication Technology Bangladesh University of ProfessionalsMirpur CantonmentDhaka1216Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh

To protect the network, resources, and sensitive data, the intrusion detection system (IDS) has become a fundamental component of organizations that prevents cybercriminal activities. Several approaches have been introduced and implemented to thwart malicious activities so far. Due to the effectiveness of machine learning (ML) methods, the proposed approach applied several ML models for the intrusion detection system. In order to evaluate the performance of models, UNSW-NB 15 and Network TON_IoT datasets were used for offline analysis. Both datasets are comparatively newer than the NSL-KDD dataset to represent modern-day attacks. However, the performance analysis was carried out by training and testing the Decision Tree (DT), Gradient Boosting Tree (GBT), Multilayer Perceptron (MLP), AdaBoost, Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the binary classification task. As the performance of IDS deteriorates with a high dimensional feature vector, an optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique. This technique employed Gini impurity as the splitting criterion of trees and adjusted the weights for two different classes of the imbalanced data to make the learning algorithm understand the class distribution. Based upon the importance score, 20 features were selected from UNSW-NB 15 and 10 features from the Network TON_IoT dataset. The experimental result revealed that DT performed well with the feature selection technique than other trained models of this experiment. Moreover, the proposed GIWRF-DT outperformed other existing methods surveyed in the literature in terms of the F1 score.

关键词： Cyber security Feature selection intrusion detection system Machine learning Network security

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Internet of Things intrusion detection system Based on Convolutional Neural Network

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计算机、材料和连续体（英文） 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 layer.Finally, 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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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 secure.Moreover,the rapid growth of IoT devices in homes increases the risk of cyber-attacks.intrusion detection systems(IDS)are commonly employed to prevent cyberattacks.These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures.Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been unsuccessful.In this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks.Specifically,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 dataset.To accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network models.We also propose two baseline models to demonstrate the performance of the proposed models.Generally,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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An Efficient Cyber Security and intrusion detection system Using CRSR with PXORP-ECC and LTH-CNN

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Computers, Materials & Continua 2023年第8期76卷 2061-2078页

作者： Nouf Saeed Alotaibi Department of Computer Science College of Science and Humanities Al DawadmiShaqra UniversityShaqra11961Saudi Arabia

intrusion detection system(IDS)is a network security mechanism that analyses all users’and applications’traffic and detectsmalicious activities in real-time.The existing IDSmethods suffer fromlower accuracy and lack the required level of security to prevent sophisticated attacks.This problem can result in the system being vulnerable to attacks,which can lead to the loss of sensitive data and potential system failure.Therefore,this paper proposes an intrusion detection system using Logistic Tanh-based Convolutional Neural Network Classification(LTH-CNN).Here,the Correlation Coefficient based Mayfly Optimization(CC-MA)algorithm is used to extract the input characteristics for the IDS from the input data.Then,the optimized features are utilized by the LTH-CNN,which returns the attacked and non-attacked data.After that,the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security phases.To prevent the system from cyber-attack,the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right(CRSR),where,in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography(PXORP-ECC)Algorithm to improve the data security.TheNetwork Security Laboratory-Knowledge Discovery inDatabases(NSLKDD)dataset and real-time sensor are used to train and evaluate the proposed LTH-CNN.The suggested model is evaluated based on accuracy,sensitivity,and specificity,which outperformed the existing IDS methods,according to the results of the experiments.

关键词： intrusion detection system logistic tanh-based convolutional neural network classification(LTH-CNN) correlation coefficient based mayfly optimization(CC-MA) cyber security

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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 rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To 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 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classiﬁcation approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classiﬁcation process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunﬂower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In 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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Enhanced Coyote Optimization with Deep Learning Based Cloud-intrusion detection system

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Computers, Materials & Continua 2023年第2期74卷 4319-4336页

作者： Abdullah M.Basahel Mohammad Yamin Sulafah M.Basahel E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia E-commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its users.But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders.intrusion detection system(IDS)refers to one of the commonly utilized system for detecting attacks on cloud.IDS proves to be an effective and promising technique,that identifies malicious activities and known threats by observing traffic data in computers,and warnings are given when such threatswere identified.The current mainstream IDS are assisted with machine learning(ML)but have issues of low detection rates and demanded wide feature engineering.This article devises an Enhanced Coyote Optimization with Deep Learning based intrusion detection system for Cloud Security(ECODL-IDSCS)model.The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic(ADASYN)technique.For detecting and classification of intrusions,long short term memory(LSTM)model is exploited.In addition,ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment.Once the presented ECODL-IDSCS model is tested on benchmark dataset,the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.

关键词： intrusion detection system cloud security coyote optimization algorithm class imbalance data deep learning

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An IoT-Based intrusion detection system Approach for TCP SYN Attacks

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Computers, Materials & Continua 2022年第5期71卷 3839-3851页

作者： Abdelwahed Berguiga Ahlem Harchay Department of Computer Science College of Science and Arts in GurayatJouf UniversitySakakahSaudi Arabia

The success of Internet of Things(IoT)deployment has emerged important smart applications.These applications are running independently on different platforms,almost everywhere in the *** of Medical Things(IoMT),also referred as the healthcare Internet of Things,is the most widely deployed application against COVID-19 and offering extensive healthcare services that are connected to the healthcare information technologies systems.Indeed,with the impact of the COVID-19 pandemic,a large number of interconnected devices designed to create smart networks.These networks monitor patients from remote locations as well as tracking medication orders.However,IoT may be jeopardized by attacks such as TCP SYN flooding and sinkhole attacks.In this paper,we address the issue of detecting Denial of Service attacks performed by TCP SYN flooding attacker nodes.For this purpose,we develop a new algorithm for intrusion detection system(IDS)to detect malicious activities in the Internet of Medical Things.The proposed scheme minimizes as possible the number of attacks to ensure data security,and preserve confidentiality of gathered data.In order to check the viability of our approach,we evaluate analytically and via simulations the performance of our proposed solution under different probability of attacks.

关键词： IoT intrusion detection system denial-of-service TCP SYN flooding attacks

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Blockchain: Secured Solution for Signature Transfer in Distributed intrusion detection system

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Computer systems Science & Engineering 2022年第1期40卷 37-51页

作者： Shraddha R.Khonde Venugopal Ulagamuthalvi Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Engineering M.E.S.College of EngineeringS.P.Pune UniversityPune411001India

Exchange of data in networks necessitates provision of security and confidentiality.Most networks compromised by intruders are those where the exchange of data is at high risk.The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network.Malicious activities are monitored and detected by the intrusion detection system(IDS)that operates with nodes connected to a distributed network.The IDS operates in two phases,where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest,Convolutional neural network,and XGBoost along with genetic algorithm to improve the performance of IDS.The novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second phase.This phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher level.The blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0,to create a prototype for secure signature transfer.It exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperl-edger Fabric.The performance of the proposed blockchain system is evaluated on UNSW NB15 dataset.Blockchain performance has been evaluated in terms of execution time,average latency,throughput and transaction processing time.Experimental evidence of the proposed IDS system demonstrates improved performance with accuracy,detection rate and false alarm rate(FAR)as key parameters used.Accuracy and detection rate increase by 2%and 3%respectively whereas FAR reduces by 1.7%.

关键词： Blockchain intrusion detection system machine learning algorithms secured communication ensemble approach

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Performance Analysis of intrusion detection system in the IoT Environment Using Feature Selection Technique

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Intelligent Automation & Soft Computing 2023年第6期36卷 3709-3724页

作者： Moody Alhanaya Khalil Hamdi Ateyeh Al-Shqeerat Department of Computer Science College of ComputerQassim UniversitySaudi Arabia

The increasing number of security holes in the Internet of Things(IoT)networks creates a question about the reliability of existing network intrusion detection systems.This problem has led to the developing of a research area focused on improving network-based intrusion detection system(NIDS)technologies.According to the analysis of different businesses,most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques.However,these techniques are not suitable for every type of network.In light of this,whether the optimal algorithm and feature reduction techniques can be generalized across various datasets for IoT networks remains.The paper aims to analyze the methods used in this research and whether they can be generalized to other datasets.Six ML models were used in this study,namely,logistic regression(LR),decision trees(DT),Naive Bayes(NB),random forest(RF),K-nearest neighbors(KNN),and linear SVM.The primary detection algorithms used in this study,Principal Component(PCA)and Gini Impurity-Based Weighted Forest(GIWRF)evaluated against three global ToN-IoT datasets,UNSW-NB15,and Bot-IoT datasets.The optimal number of dimensions for each dataset was not studied by applying the PCA algorithm.It is stated in the paper that the selection of datasets affects the performance of the FE techniques and detection algorithms used.Increasing the efficiency of this research area requires a comprehensive standard feature set that can be used to improve quality over time.

关键词： Machine learning internet of things intrusion detection system feature selection technique

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