Intrusion Detection In Internet Research Articles

A two stage lightweight approach for intrusion detection in Internet of Things

The prevalence of the Internet of Things (IoT) in people’s life has led to a significant rise in the amounts of breaches against different IoT devices. Given that IoT devices are typically installed in settings with constrained resources and limited processing power, and malicious traffic samples are difficult to collect, resulting in traditional deep learning algorithms that do not handle intrusion detection in IoT network well. In this work, we present a two-stage lightweight intrusion detection model based on self-supervised contrastive learning and self-knowledge distillation to reduce model’s over-reliance on labels, optimize the model’s capacity for generalization and significantly increase the IoT network’s intrusion detection speed while lowering the model’s complexity. The representations of the network traffic are extracted in the first step using self-supervised contrastive learning, and the representations learnt by the teacher model are transferred to the student model in the second stage using self-knowledge distillation. On the benchmark datasets KDD CUP99, NSL-KDD, UNSW-NB15, CIC IDS2017 and BoT-IoT, we carried out in-depth studies, and the experimental results demonstrate that our proposed model outperforms recent state-of-the-art models in addition to achieving superior intrusion detection capabilities. The code will be released at https://github.com/RicardoMLi/CL-SKD.

A few-shot learning based method for industrial internet intrusion detection

A few-shot learning based method for industrial internet intrusion detection

Development of novel intrusion detection in Internet of Things using improved dart game optimizer‐derived optimal cascaded ensemble learning

AbstractBackground of the StudyInternet of things (IoT) industry has accelerated its development with the support of advanced information technology and economic expansion. A complete industrial foundation includes software, chips, electronic components, IoT services, integrated systems, machinery, and telecom operators, which the gradual improvement in the IoT industry system has formulated. As the exponential growth of IoT devices increases, the attack surface available to cybercriminals enables them to carry out potentially more damaging operations. As a result, the security sector has witnessed a rise in cyberattacks. Hackers use several methods to copy and modify the information in the IoT environment. Machine learning techniques are used by the intrusion detection (ID) model to determine and categorize attacks in IoT networks.ObjectivesThus, this study explores the ID system with the heuristic‐assisted deep learning approaches for effectively detect the attacks in the IoT. At first, the IoT data are garnered in benchmark resources. Then, the gathered data is preprocessed to perform data cleaning. Next, the data is transformed and fed to the feature extraction stage. The feature extraction is performed with the help of one‐dimensional convolutional neural network (1D‐CNN), where the features are extracted from the target‐based pooling layer. Then, these attained deep features are fed to the ID phase, where the cascaded ensemble learning (CEL) approach is adopted for detecting the intrusions. Here, the hyperparameter tuning is done with a new suggested improved darts game optimizer (IDGO) algorithm. Here, the main objective of the developed algorithm helps to maximize accuracy in ID.FindingsThroughout the experimental findings, the developed model provides 86% of accuracy. Thus, the finding of the developed model shows less detecting time and higher detection efficiency.

Lightweight, Trust-Managing, and Privacy-Preserving Collaborative Intrusion Detection for Internet of Things

This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet of Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The proposed hierarchical architecture spans edge, fog, and cloud layers, ensuring efficient and scalable collaborative intrusion detection. Trustworthiness is established through the incorporation of distributed ledger technology (DLT), leveraging blockchain frameworks to enhance the reliability and transparency of communication among IoT devices. Furthermore, the research adopts federated learning (FL) techniques to address privacy concerns, allowing devices to collaboratively learn from decentralized data sources while preserving individual data privacy. Validation of the proposed approach is conducted using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. This research contributes to the advancement of secure and resilient IoT infrastructures, addressing the imperative need for lightweight, trust-managing, and privacy-preserving solutions in the face of evolving cybersecurity challenges. According to our experiments, the proposed model achieved an average accuracy of 97.65%, precision of 97.65%, recall of 100%, and F1-score of 98.81% when detecting various attacks on IoT systems with heterogeneous devices and networks. The system is a lightweight system when compared with traditional intrusion detection that uses centralized learning in terms of network latency and memory consumption. The proposed system shows trust and can keep private data in an IoT environment.

Towards optimized machine-learning-driven intrusion detection for Internet of Things applications

Towards optimized machine-learning-driven intrusion detection for Internet of Things applications

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things

Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performance trade-offs and vulnerability to cyber-attacks. One approach to address these concerns is minimising computational overhead and adopting lightweight intrusion detection techniques. In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in IoT environments. The proposed OCFSDA model incorporates feature selection, data compression, pruning, and deparameterization. We deployed the model on a Raspberry Pi4 using the TFLite interpreter by leveraging optimisation and inferencing with semi-supervised learning. Using the MQTT-IoT-IDS2020 and CIC-IDS2017 datasets, our experimental results demonstrate a remarkable reduction in the computation cost in terms of time and memory use. Notably, the model achieved an overall average accuracies of 99% and 97%, along with comparable performance on other important metrics such as precision, recall, and F1-score. Moreover, the model accomplished the classification tasks within 0.30 and 0.12 s using only 2KB of memory.

Spotted hyena optimizer with ensemble deep learning based intrusion detection in internet of things environment

The Internet of Things (IoT) has many potential uses in the day-to-day operations of individuals, companies, and governments. It makes linking all devices to the internet a realistic possibility. Convincing IoT devices to work together to implement several real-world applications is a challenging feat. Security issues impact innovative platform applications due to the current security state in IoT-based operations. As a result, intrusion detection systems (IDSs) tailored to IoT platforms are essential for protecting against security breaches caused by the Internet of Things (IoT) that exploit its vulnerabilities. Issues with data loss, dangers, service interruption, and external hostile assaults are all part of the IoT security landscape. Designing and implementing appropriate security solutions for IoT environments is the main emphasis of this research. Within the Internet of Things (IoT) context, this research creates a Spotted Hyena Optimizer (SHO-EDLID) method for intrusion detection using ensemble deep learning. The main goal of the demonstrated SHO-EDLID method was to detect and categorize intrusions in an Internet of Things setting. It comprises many subprocesses, including feature selection, categorization, and pre-processing. The SHO-EDLID method uses a SHO-based feature selection strategy to identify the best feature subsets. It then used an ensemble of three DL models— a deep belief network (DBN), a stacked autoencoder (SAE), and a bidirectional recurrent neural network (BiRNN)— to detect and name cyberattacks. Finally, the DL models’ parameters are tuned using the Adabelief optimizer. A comprehensive simulation was run to illustrate that the offered model performed better. According to a thorough comparative analysis, the suggested method outperformed other recent approaches. Purpose of the Manuscript: To identify the best feature subsets, the SHO-EDLID method used the SHO-based feature selection method... Afterward, cyberattack identification and tracking were carried out using an ensemble of three DL models: DBN, SAE, and BiRNN. The final step in optimizing the DL models’ parameters is the Adabelief optimizer. The main comparative results: The proposed model present the Comparative analysis of SHO-EDLID algorithm with other existing systems and its outperform the performance in precision 97.50, accuracy 99.56, Recall 98.42, F-Measure.97.95.

A lightweight Intrusion Detection for Internet of Things‐based smart buildings

AbstractThe integration of Internet of Things (IoT) devices into commercial or industrial buildings to create smart environments, such as Smart Buildings (SBs), has enabled real‐time data collection and processing to effectively manage building operations. Due to poor security design and implementation in IoT devices, SB networks face an array of security challenges and threats (e.g., botnet malware) that leverage IoT devices to conduct Distributed Denial of Service (DDoS) attacks on the Internet infrastructure. Machine Learning (ML)‐based traffic classification systems aim to automatically detect such attacks by effectively differentiating attacks from benign traffic patterns in IoT networks. However, there is an inherent accuracy‐efficiency tradeoff in network traffic classification tasks. To balance this tradeoff, we develop an accurate yet lightweight device‐specific traffic classification model. This model classifies SB traffic flows into four types of coarse‐grained flows, based on the locations of traffic sources and the directions of traffic transmissions. Through these four types of coarse‐grained flows, the model can extract simple yet effective flow rate features to conduct learning and predictions. Our experiments find the model to achieve an overall accuracy of 96%, with only 32 features to be learned by the ML model.

Privacy-preserving intrusion detection in Internet of medical things neural networks using a novel recurrent U-Net auto-encoder algorithm for biomedical applications

The rapid development of Internet of Things (IoT) technology has enabled the emergence of the Internet of Medical Things (IoMT), especially in body area network applications. To protect sensitive medical data, it is essential to ensure privacy preservation and detect intrusions in this context. This study proposes a novel intrusion detection system that protects the privacy of IoMT networks, specifically in the context of body area networks. For feature extraction, the system employs a recurrent U-Net autoencoder algorithm, which effectively captures temporal dependencies in IoMT data. In addition, privacy is protected through the combination of data anonymization techniques and data classification using Principal Component Analysis (PCA). Combining the recurrent U-Net autoencoder algorithm, privacy preservation mechanisms, and PCA-based data classification, the proposed system architecture comprises the U-Net autoencoder algorithm. The proposed method is superior to existing approaches in terms of accuracy, precision, recall, F-measure, and classification loss, as demonstrated by experimental evaluations. This research contributes to the field of privacy protection and intrusion detection in IoMT, specifically in body area network applications.

Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach

The Internet of Things (IoT) has proliferated, transitioning from modest home automation to encompass sectors like healthcare, agriculture, transportation, and manufacturing. This evolution is characterized by devices' ability to autonomously gather, disseminate, and analyze data, leading to improved real-time decision-making, predictive insights, and customized user experiences. The ubiquity of IoT, while promising, introduces significant data security concerns. The vast number of interlinked devices and diverse and often insufficient security features make them vulnerable to cyber threats, emphasizing the need for robust security mechanisms. Intrusion Detection Systems (IDS) have traditionally acted as vital guards against such threats; however, with the ever-increasing data in the IoT, traditional IDS models, such as Naive Bayes, face processing speed and accuracy challenges. This paper introduces a novel model, "FE+NB," which merges advanced Feature Extraction (FE) with the Naive Bayes (NB) classifier. Central to this model is the "Temporal-Structural Synthesis" technique tailored for IoT traffic data, focusing on data compression, temporal and structural analyses, and Feature Selection (FS) using mutual information. Consequently, the model enhances efficiency and accuracy in Intrusion Detection (ID) in complex IoT networks.

IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things

IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming

Converging Technologies for Health Prediction and Intrusion Detection in Internet of Healthcare Things With Matrix- Valued Neural Coordinated Federated Intelligence

Converging Technologies for Health Prediction and Intrusion Detection in Internet of Healthcare Things With Matrix- Valued Neural Coordinated Federated Intelligence

Tokenization Representation and Deep Learning based Intrusion Detection in Internet of Vehicles

Tokenization Representation and Deep Learning based Intrusion Detection in Internet of Vehicles

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

Automated Network Intrusion Detection for Internet of Things: Security Enhancements

Automated Network Intrusion Detection for Internet of Things: Security Enhancements

Blockchain based federated learning for intrusion detection for Internet of Things

Blockchain based federated learning for intrusion detection for Internet of Things

Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach.

The traditional Transformer model primarily employs a self-attention mechanism to capture global feature relationships, potentially overlooking local relationships within sequences and thus affecting the modeling capability of local features. For Support Vector Machine (SVM), it often requires the joint use of feature selection algorithms or model optimization methods to achieve maximum classification accuracy. Addressing the issues in both models, this paper introduces a novel network framework, CTSF, specifically designed for Industrial Internet intrusion detection. CTSF effectively addresses the limitations of traditional Transformers in extracting local features while compensating for the weaknesses of SVM. The framework comprises a pre-training component and a decision-making component. The pre-training section consists of both CNN and an enhanced Transformer, designed to capture both local and global features from input data while reducing data feature dimensions. The improved Transformer simultaneously decreases certain training parameters within CTSF, making it more suitable for the Industrial Internet environment. The classification section is composed of SVM, which receives initial classification data from the pre-training phase and determines the optimal decision boundary. The proposed framework is evaluated on an imbalanced subset of the X-IIOTID dataset, which represent Industrial Internet data. Experimental results demonstrate that with SVM using both "linear" and "rbf" kernel functions, CTSF achieves an overall accuracy of 0.98875 and effectively discriminates minor classes, showcasing the superiority of this framework.

IoT empowered smart cybersecurity framework for intrusion detection in internet of drones

The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD, specifically while applying B-LSTM and LSTM. The system attains precision values of 89.10% and 90.16%, accuracy rates up to 91.00–91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.