An Efficient Cyber Assault Detection System using Feature Optimization for IoT-based Cyberspace

Intrusion Detection Systems (IDS) play a vital role in securing Internet of Things (IoT) networks against cyber-attacks. Previous work used a binary classification approach to detect only Denial-of-Service (DoS) attacks. This paper presents an improved multiclass IDS that identifies multiple attack types. The proposed approach uses simulated IoTID20-based traffic datasets to classify Normal, Denial-of-Service, Mirai botnet, Man-in-the-Middle (MITM), and Scan activity attacks. The proposed approach uses a combination of feature selection methods, including correlation-based filtering and a genetic algorithm, followed by machine learning classifiers such as Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Experimental results show a significant improvement in classification performance. The RF classifier with Genetic Algorithm (GA)-based feature selection achieved the highest accuracy (96.5%), followed closely by DT and SVM. Therefore, based on our theoretical and experimental comparisons, the proposed approach could be a practical step toward deploying more robust, realistic IDS models in IoT environments.

The probability of network attacks is increasing daily due to the continuous development of tools and techniques that bypass the firewall and other network security boundaries. This motivates the researcher towards the upgradation and advancement in adaptive Artificial Intelligence (AI) based intrusion detection systems (IDS). The traditional machine learning (ML) based IDS has its limitations due to noise and the unexplainable nature of the decision‐making that took place during the implementation of the ML algorithm for categorization of attacked or normal data packets that arrive in the network. To address these issues, this research proposes an ML algorithm with the understanding of decisions through Explainable artificial intelligence (XAI). The dataset used for experimentation is IoTID20, which extracts data in a real‐time Internet of Things (IoT) environment. This paper compares the accuracy results of different ML algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron –Neural Networks (MLP‐NN), Deep Neural Networks (DN), and Decision Tree (DT). During the classification of attacks, the Random Forest shows an accuracy of 93.80%, the XGBoost shows an accuracy of 97.30%, the Decision Tree shows an accuracy of 99.99%, the MLP Classifier—Neural Network shows an accuracy of 95.90%, and the Deep Neural Network (DNN) shows an accuracy of 94.60%. These algorithms are also analyzed with Precision, Recall, and F1‐Score. The proposed method of incorporating XAI increases the automation process with high accuracy and explainability of the decision to categorize anomalous and normal packets in an IoT environment. This remarkable achievement provides better intuitions in IDS with good protection in resistance to novel and unknown attacks.

The rapid growth of the Internet of Things (IoT) has introduced considerable security challenges, making Intrusion Detection Systems (IDS) essential for protecting connected devices from cyber threats. This paper presents an Artificial Intelligence (AI)-driven Intrusion Detection System (IDS) designed for IoT environments, utilizing machine learning and anomaly detection techniques. The system leverages the NSL-KDD dataset to distinguish between normal and anomalous network traffic. We implemented multiple feature selection techniques, including correlation-based selection, Recursive Feature Elimination (RFE), and LASSO, to optimize model performance. Various machine learning classifiers, including Random Forest (RF), Bagging, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), were evaluated using key performance metrics such as accuracy, recall, precision, and AUC-ROC. Experimental results indicate that Random Forest and Bagging outperformed other models, achieving the highest accuracy of 99.89%, with an AUC-ROC of 99.88%, outperforming other models in intrusion detection. Additionally, feature selection methods effectively reduced dataset dimensionality while maintaining high detection performance. Despite these promising results, challenges such as high training time, false positives, and real-time adaptability remain critical concerns for practical deployment in IoT systems.

The Internet of Things (IoT) has transformed various sectors by enabling connectivity and automation through smart devices. However, the expansion of IoT networks has introduced new security challenges, particularly concerning the detection and prevention of cyber threats. This study explores the application of machine learning (ML) models for enhancing network security in IoT-enabled smart systems. The primary objective was to evaluate the performance of different ML algorithms, including Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-Means, and Autoencoder, in detecting and mitigating security threats in IoT environments. The study also aimed to identify the most effective model in terms of accuracy, precision, recall, and F1-score. The research methodology involved training and testing these models on publicly available IoT security datasets, followed by performance evaluation based on the key metrics. Statistical analysis, including One-Way ANOVA and post-hoc testing, was used to assess the significance of the performance differences across the models. The results revealed that CNN outperformed all other models, achieving the highest accuracy (96.7%), precision (95.8%), recall (97.2%), and F1-score (96.5%). Random Forest also demonstrated strong performance with an accuracy of 95.6% and a balanced evaluation across all metrics. In contrast, traditional models like SVM and DT showed comparatively lower performance, while Autoencoder struggled with classification tasks, particularly in real-time threat detection. Statistical analysis confirmed the significant differences in model performance. In conclusion, the study demonstrates the effectiveness of deep learning and ensemble models, particularly CNN and Random Forest, in enhancing IoT network security. The research highlights the importance of selecting the appropriate ML model based on the specific requirements of the IoT environment and computational constraints, and suggests that hybrid models and model optimization strategies may offer promising solutions for resource-constrained IoT systems.

With the growth of the internet of things (IoT) smart objects, managing these objects becomes a very important challenge, to know the total number of interconnected objects on a heterogeneous network, and if they are functioning correctly; the use of IoT objects can have advantages in terms of comfort, efficiency, and cost. In this context, the identification of IoT objects is the first step to help owners manage them and ensure the security of their IoT environments such as smart homes, smart buildings, or smart cities. In this paper, to meet the need for IoT object identification, we have deployed an intelligent environment to collect all network traffic traces based on a diverse list of IoT in real-time conditions. In the exploratory phase of this traffic, we have developed learning models capable of identifying and classifying connected IoT objects in our environment. We have applied the six supervised machine learning algorithms: support vector machine, decision tree (DT), random forest (RF), k-nearest neighbors, naive Bayes, and stochastic gradient descent classifier. Finally, the experimental results indicate that the DT and RF models proved to be the most effective and demonstrate an accuracy of 97.72% on the analysis of network traffic data and more particularly information contained in network protocols. Most IoT objects are identified and classified with an accuracy of 99.21%.

Recently, the Internet of Things (IoT) has been used in various applications such as manufacturing, transportation, agriculture, and healthcare that can enhance efficiency and productivity via an intelligent management console remotely. With the increased use of Industrial IoT (IIoT) applications, the risk of brutal cyber-attacks also increased. This leads researchers worldwide to work on developing effective Intrusion Detection Systems (IDS) for IoT infrastructure against any malicious activities. Therefore, this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure. A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is introduced. The data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT applications. Correspondingly, this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT network. This is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient fashion. Therefore, several machine learning algorithms such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (NB), Classification and Regression Tree (CART), Random Forest (RF), and AdaBoost (AB) are used for the detection intent on thirteen different intrusion datasets. Several performance metrics like accuracy, precision, recall, and F1-score are used to estimate the proposed framework. The experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed datasets. In addition, the proposed work accomplishes high performance compared to other recent related works in terms of different security and detection evaluation parameters.

The widespread adoption of Internet of Things (IoT) devices has been accompanied by a remarkable rise in both the frequency and intensity of Distributed Denial of Service (DDoS) attacks, which aim to overwhelm and disrupt the availability of networked systems and connected infrastructures. In this paper, we present a novel approach to DDoS attack detection and mitigation that integrates state-of-the-art machine learning techniques with Blockchain-based Mobile Edge Computing (MEC) in IoT environments. Our solution leverages the decentralized and tamper-resistant nature of Blockchain technology to enable secure and efficient data collection and processing at the network edge. We evaluate multiple machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Transformer architectures, and LightGBM, using the CICDDoS2019 dataset. Our results demonstrate that Transformer models achieve a superior detection accuracy of 99.78%, while RF follows closely with 99.62%, and LightGBM offers optimal efficiency for real-time detection. This integrated approach significantly enhances detection accuracy and mitigation effectiveness compared to existing methods, providing a robust and adaptive mechanism for identifying and mitigating malicious traffic patterns in IoT environments.

The Internet of Things (IoT) is a technological revolution that enables human-to-human and machine-to-machine communication for virtual data exchange. The IoT allows us to identify, locate, and access the various things and objects around us using low-cost sensors. The Internet of Things offers many benefits but also raises many issues, especially in terms of privacy and security. Appropriate solutions must be found to these challenges, and privacy and security are top priorities in the IoT. This study identifies possible attacks on different types of networks as well as their countermeasures. This study provides valuable insights to vulnerability researchers and IoT network protection specialists because it teaches them how to avoid problems in real networks by simulating them and developing proactive solutions. IoT anomalies were detected by simulating message queuing telemetry transport (MQTT) over a virtual network. Utilizing DDoS attacks and some machine learning algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR), as well as an artificial neural network, multilayer perceptron (MLP), naive Bayes (NB) and decision tree (DT) are used to detect and mitigate the attack. The proposed approach uses a dataset of 4998 records and 34 features with 8 classes of network traffic. The classifier RF showed the best performance with 99.94% accuracy. An intrusion detection system using Snort was implemented. The results provided theoretical proof of applicability and feasibility.

Data security is regarded to be one of the crucial challenges in this fast-growing internet world. Data generated through internet is exposed to various types of vulnerabilities and exploits. Security mechanisms such as Intrusion Detection System (IDS) are designed to detect various types of vulnerabilities and attacks. Various Machine Learning (ML) and Deep Learning (DL) techniques are used for building IDS. In this paper, we aim to build Deep Neural Network (DNN)-based IDS for attack detection and classification. DNN technique has certain challenges such as complex network structure, co-adaptation of feature vectors, over-fitting, to name a few. We aim to address these challenges by using AntiRectifier layer and variants of dropout namely, Standard dropout, Gaussian dropout, and Gaussian Noise. In this paper, we have evaluated DNN-based IDS using NSL-KDD, UNSW_NB-15 and CIC-IDS-2017 dataset. The experimental results show that DNN-based IDS with AntiRectifier layer outperforms compared to ML techniques such as Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (k-NN) and variants of dropout namely, Standard dropout, Gaussian dropout, and Gaussian noise in terms of accuracy, precision, recall, f-score, and false positive rate.

Machine Learning Algorithms for Intrusion Detection in IoT Prediction and Performance Analysis

The integration of the Internet of Things (IoT) with machine learning is revolutionizing healthcare by introducing real-time and predictive health monitoring solutions. This research presents a comprehensive health monitoring system that acquires physiological data using IoT sensors accurately using robust machine learning algorithms. The system processes physiological parameters, including pulse rate and body temperature, through effective normalization and outlier removal techniques in real time, ensuring data reliability. Five machine learning classifiers, including Support Vector Machine (SVM), Naïve Bayes, Random Forest, K-Nearest Neighbors (KNN), and Decision Tree, were implemented and evaluated on a dataset of 4287 patient samples. Naïve Bayes achieved the highest accuracy of 97%, with superior performance metrics across precision (71%), recall (75%), F1-score (73%), and False Positive Rate (0.91%). The system demonstrates low latency (≈25ms) for data transmission and effective scalability, making it suitable for diverse healthcare settings, particularly in resource-limited areas. Key findings validate the effectiveness of the system in enhancing early diagnosis and personalized care. This research contributes to healthcare technology advancement by providing a scalable, efficient, and reliable framework for predictive health monitoring. Keywords: Health Monitoring System, Internet of Things (IoT), Machine Learning, Real-Time Data Analysis, Predictive Healthcare.

The proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.

This study explores the development of robust Intrusion Detection Systems (IDS) to enhance cybersecurity in Wireless Sensor Networks (WSNs) within the evolving Internet of Things (IoT) ecosystem. It leverages a publicly available dataset derived from UNSW-NB15, retrieved from a GitHub repository, capturing diverse network traffic attributes (dttl, swin, dwin, tcprtt, synack, ackdat), protocol-specific indicators (proto tcp, proto udp), and service-specific attributes (service dns). These features enable precise analysis of TCP/IP headers and traffic patterns, supporting multi-class classification into four categories: Analysis, Denial of Service (DoS), Exploits, and Normal. Advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), were applied with systematic preprocessing (including KNN-based imputation, normalization, and one-hot encoding), feature selection using Random Forest importance, and 5-fold cross-validation. The best performance was achieved by Random Forest (accuracy, precision, recall, and F1-score of 99.9877%), followed by KNN (99.9754%) and SVM (99.9630%). The study demonstrates that combining well-structured models with relevant protocol-level features and robust evaluation strategies can significantly enhance intrusion detection capabilities in IoT-based environments. It reinforces the value of using modern public datasets and interpretable algorithms for building scalable and reliable IDS solutions.

The swift advancement of the Internet of Things (IoT) has significantly transformed contemporary communication frameworks by facilitating effortless data transmission among a diverse network of interconnected smart devices. Nevertheless, the increased connectivity and resource-constrained nature of IoT nodes have made them prime targets for cyber-attacks, necessitating the development of intelligent and privacy-aware Intrusion Detection Systems (IDS). Traditional IDS approaches often fall short in addressing the dual challenges of real-time threat detection and preserving user data confidentiality. This research represents a Privacy-Preserving Model for Efficient Designing of IDS in IoT Environments by integrating a deep learning-driven detection framework with advanced optimization techniques. The core detection engine is built upon a Gated Recurrent Unit (GRU) network, chosen for its lightweight structure and strong temporal pattern recognition capabilities. To enhance model performance, hyperparameters are tuned using the novel Ladybug Beetle Optimization (LBO) algorithm, which mimics the intelligent foraging behaviour of ladybugs to achieve global optima efficiently. To ensure data privacy and reduce communication overhead, the model is integrated with federated learning, allowing distributed training across IoT devices without centralized data aggregation. Additionally, lightweight encryption techniques are employed to secure data transmission during training and inference phases. The proposed system was evaluated using standard benchmark datasets, achieving a detection accuracy of 98.9%, and the results demonstrate significant improvements in recall, precision and computational efficiency when examined to traditional approaches. This work contributes a scalable, intelligent, and privacy-respecting intrusion detection architecture suitable for real-world IoT scenarios, comprising smart homes, healthcare systems, and industrial automation.

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.