Internet Detection Research Articles

Correction to “[Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems]”

Correction to “[Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems]”

An AI-Driven Model to Enhance Sustainability for the Detection of Cyber Threats in IoT Environments

In the face of constantly changing cyber threats, a variety of actions, tools, and regulations must be considered to safeguard information assets and guarantee the confidentiality, reliability, and availability of digital resources. The purpose of this research is to create an artificial intelligence (AI)-driven system to enhance sustainability for cyber threat detection in Internet of Things (IoT) environments. This study proposes a modern technique named Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost) for optimizing accuracy and sustainability in identifying attacks, thus contributing to heightening security in IoT environments. CICIDS2017, NSL-KDD, and UNSW-NB15 were used in this study. Min-max normalization is employed to pre-process the obtained raw information. The proposed model AF-WAdaBoost dynamically adjusts classifiers, enhancing accuracy and resilience against evolving threats. Python is used for model implementation. The effectiveness of the suggested AF-WAdaBoost model in identifying different kinds of cyber-threats in IoT systems is examined through evaluation metrics like accuracy (98.69%), F-measure (94.86%), and precision (95.72%). The experimental results unequivocally demonstrate that the recommended model performed better than other traditional approaches, showing essential enhancements in accuracy and strength, particularly in a dynamic environment. Integrating AI-driven detection balances offers sustainability in cybersecurity, ensuring the confidentiality, reliability, and availability of information assets, and also helps in optimizing the accuracy of systems.

Metaheuristic optimized complex-valued dilated recurrent neural network for attack detection in internet of vehicular communications

The Internet of Vehicles (IoV) is a specialized iteration of the Internet of Things (IoT) tailored to facilitate communication and connectivity among vehicles and their environment. It harnesses the power of advanced technologies such as cloud computing, wireless communication, and data analytics to seamlessly exchange real-time data among vehicles, road-side infrastructure, traffic management systems, and other entities. The primary objectives of this real-time data exchange include enhancing road safety, reducing traffic congestion, boosting traffic flow efficiency, and enriching the driving experience. Through the IoV, vehicles can share information about traffic conditions, weather forecasts, road hazards, and other relevant data, fostering smarter, safer, and more efficient transportation networks. Developing, implementing and maintaining sophisticated techniques for detecting attacks present significant challenges and costs, which might limit their deployment, especially in smaller settings or those with constrained resources. To overcome these drawbacks, this article outlines developing an innovative attack detection model for the IoV using advanced deep learning techniques. The model aims to enhance security in vehicular networks by efficiently identifying attacks. Initially, data is collected from online databases and subjected to an optimal feature extraction process. During this phase, the Enhanced Exploitation in Hybrid Leader-based Optimization (EEHLO) method is employed to select the optimal features. These features are utilized by a Complex-Valued Dilated Recurrent Neural Network (CV-DRNN) to detect attacks within vehicle networks accurately. The performance of this novel attack detection model is rigorously evaluated and compared with that of traditional models using a variety of metrics.

Masked Memory Network for Semi-Supervised Anomaly Detection in Internet of Things

Masked Memory Network for Semi-Supervised Anomaly Detection in Internet of Things

Abnormal behavior detection in industrial control systems based on CNN

With the widespread application of Internet of Things technology in industrial control systems, abnormal behavior detection has become a key task to ensure the safety and stable operation of the system. We propose a multi-branch convolutional fusion neural network method to improve the accuracy and efficiency of abnormal behavior detection in Internet of Things industrial control systems. This method achieves efficient detection of abnormal behavior in video data by combining ResNet152 for spatial feature extraction and GRU for temporal feature extraction. Unlike traditional methods, this method adopts a multi-branch structure, which can simultaneously capture multi-scale feature information, significantly enhancing the richness of feature expression and the accuracy of detection. Experimental results show that on the UCF-Crime dataset, the accuracy of this method reaches 85.76%, which is significantly better than that of traditional methods. In addition, on the larger UCF-101 dataset, the accuracy of this method reaches 92.21%, further verifying its excellent generalization performance. Compared with the C3D network, this method improves the accuracy by nearly 6% while maintaining a high processing speed. These results show that the proposed method has great potential in practical applications.

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.

Explainable AI for epileptic seizure detection in Internet of Medical Things

In the field of precision healthcare, where accurate decision-making is paramount, this study underscores the indispensability of eXplainable Artificial Intelligence (XAI) in the context of epilepsy management within the Internet of Medical Things (IoMT). The methodology entails meticulous preprocessing, involving the application of a band-pass filter and epoch segmentation to optimize the quality of Electroencephalograph (EEG) data. The subsequent extraction of statistical features facilitates the differentiation between seizure and non-seizure patterns. The classification phase integrates Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest classifiers. Notably, SVM attains an accuracy of 97.26%, excelling in the precision, recall, specificity, and F1 score for identifying seizures and non-seizure instances. Conversely, KNN achieves an accuracy of 72.69%, accompanied by certain trade-offs. The Random Forest classifier stands out with a remarkable accuracy of 99.89%, coupled with an exceptional precision (99.73%), recall (100%), specificity (99.80%), and F1 score (99.86%), surpassing both SVM and KNN performances. XAI techniques, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP), enhance the system's transparency. This combination of machine learning and XAI not only improves the reliability and accuracy of the seizure detection system but also enhances trust and interpretability. Healthcare professionals can leverage the identified important features and their dependencies to gain deeper insights into the decision-making process, aiding in informed diagnosis and treatment decisions for patients with epilepsy.

Federated Bayesian optimization XGBoost model for cyberattack detection in internet of medical things

BackgroundHospitals and medical facilities are increasingly concerned about network security and patient data privacy as the Internet of Medical Things (IoMT) infrastructures continue to develop. Researchers have studied customized network security frameworks and cyberattack detection tools driven by Artificial Intelligence (AI) to counter different types of attacks, such as spoofing, data alteration, and botnet attacks. However, carrying out routine IoMT services and tasks during an under-attack scenario is challenging. Machine Learning has been extensively suggested for detecting cyberattacks in IoMT and IoT infrastructures. However, the conventional centralized approach in ML cannot effectively detect newly emerging attacks without compromising patient data privacy and network flow data confidentiality. AimThis study discusses a Federated Bayesian Optimization XGBoost framework that employs multimodal sensory signals from patient vital signs and network flow data to detect attack patterns and malicious network traffic in IoMT infrastructure while ensuring data privacy and detecting previously unknown attacks. MethodologyThe proposed model employs a Federated Bayesian Optimisation XGBoost approach, which allows us to search the parameter space quickly and find an optimal solution from each local server while aggregating the model parameters from each local server to the centralised server. The XGBoost algorithm generates a new tree by taking into account the previously estimated value for the tree's input data and then optimizing the prediction gain. This study used a dataset with 44 attributes and 16 318 instances. During the preprocessing phase, 10 features were dropped, and the remaining 34 features were used to evaluate the network flows and biometric data (patient vital signs). ResultsThe performance evaluation reveals that the proposed model predicts data alteration, malware, and spoofing attacks in patients' vital signs and network flow data with a prediction accuracy of 0.96. The results obtained from the experiment demonstrate that both the centralized and federated models are synchronized, with the latter occasionally being slightly reduced. ConclusionThe findings indicate that the suggested model can be incorporated into the IoMT domain to detect malicious patterns while maintaining data privacy and confidentiality efficiently.

Hybrid stacked autoencoder with dwarf mongoose optimization for Phishing attack detection in internet of things environment

With the fast development of the Internet of Things (IoTs), phishing attacks are transferring to this domain because of the number of IoT devices and private data they can handle. IoT phishing suggests the malicious practice of targeting IoT devices and their users to steal sensitive data, gain unauthorized access, or compromise the safety and functionality of these devices. Phishing attacks in the framework of IoT take several procedures, comprising SMS phishing, email phishing, or even physical attacks on the devices themselves. Deep learning (DL) approaches are executed to improve the prevention and detection of IoT phishing attacks. Consequently, this article introduces a Dwarf Mongoose Optimization with deep learning for Phishing Attack Detection (DMODL-PAD) method in an IoT platform. The objective of the DMODL-PAD method is to utilize feature selection (FS) with a hyperparameter tuning strategy for automated phishing attack detection in the IoT platform. The DMODL-PAD method involves the DMO model, unlike the FS method. Afterwards, a hybrid stacked autoencoder (HSAE) model can execute the phishing attack detection process. The DMODL-PAD method uses a jellyfish search optimizer (JSO) for the hyperparameter tuning process. The experimentation validation of the DMODL-PAD technique has been examined under a benchmark database. The extensive outcomes portrayed the superior detection results of the DMODL-PAD technique over other recent methods. These results ensured that the DMODL-PAD technique could effectually detect phishing attacks from the IoT environment.

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.

Machine Learning Based Vulnerability Detection in Internet of Things Operating System’s Codes

Machine Learning Based Vulnerability Detection in Internet of Things Operating System’s Codes

A Review of Anomalous Behavior Detection in Internet of Vehicles

The intelligent transportation system with the Internet of Vehicles as its core is gradually penetrating into the lives of urban residents, but it has also exposed security threats such as remote control of vehicles and leakage of personal information of car owners. Compared to the security issues at the level of vehicle end devices and vehicle networking service platforms, the article focuses on the security issues of abnormal behavior in vehicle networking. Based on this, the article reviews the relevant research on abnormal behavior detection mechanisms in the Internet of Vehicles environment in recent years. Firstly, the definition of abnormal behavior was analyzed, and the basic framework for detecting abnormal behavior was provided; Then, the classification of abnormal behavior detection mechanisms was discussed from three aspects: deep learning based abnormal behavior detection, spatiotemporal fusion based abnormal behavior detection, and visual based abnormal behavior detection; Finally, the unresolved technical issues and future research trends in the current abnormal behavior detection mechanism for the Internet of Vehicles were summarized.

Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems

AbstractThe rapid increase and adoption of Internet of Things (IoT) devices have introduced unprecedented conveniences into modern life. However, this growth has also ushered in a wave of cyberattacks targeting these often‐vulnerable systems. Smart cities, relying on interconnected sensors, are particularly susceptible to attacks due to the expanded entry points created by these devices. A security breach in such systems can compromise personal data and disrupt entire ecosystems. Traditional security measures are inadequate against the evolving sophistication of cyberattacks. The authors aim to address these challenges by leveraging honeypot data and machine learning to enhance IoT security. The research focuses on three objectives: identifying datasets from IoT‐targeted honeypots, evaluating machine learning algorithms for threat detection, and proposing comprehensive security solutions. Real‐world cyber‐attack datasets from diverse honeypots simulating IoT devices are analysed using various machine learning and neural network algorithms. Results demonstrate significant improvement in cyber‐attack detection and mitigation when integrating honeypot data into IoT security frameworks. The authors advance knowledge and provides practical insights for implementing robust security measures in diverse IoT applications, filling a crucial research gap.

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.

Hybrid Data Augmentation Based Machine Learning Approach for Botnet Attack Detection in IOT Networks

This paper presents a comprehensive approach to botnet detection in Internet of Things (IoT) networks through the development and evaluation of a Generative Adversarial Network (GAN) augmented machine learning model. The methodology encompasses a multi-step process, starting with data collection and pre-processing, including feature extraction, normalization, and handling missing values. To address the challenge of data imbalance, a novel application of GANs is proposed. For classification of network traffic into botnet and legitimate traffic is performed using xgboost. The performance of the proposed model is rigorously evaluated using the N-BaIoT dataset, demonstrating its effectiveness through high accuracy, precision, recall, and F1-score metrics. The results indicate significant improvements over existing models, showcasing the potential of the proposed methodology in enhancing IoT network security against botnet threats.

Empowering anomaly detection algorithm: a review

<span lang="EN-US">Detecting anomalies in a data stream relevant to domains like intrusion detection, fraud detection, security in sensor networks, or event detection in internet of things (IoT) environments is a growing field of research. For instance, the use of surveillance cameras installed everywhere that is usually governed by human experts. However, when many cameras are involved, more human expertise is needed, thus making it expensive. Hence, researchers worldwide are trying to invent the best-automated algorithm to detect abnormal behavior using real-time data. The designed algorithm for this purpose may contain gaps that could differentiate the qualities in specific domains. Therefore, this study presents a review of anomaly detection algorithms, introducing the gap that presents the advantages and disadvantages of these algorithms. Since many works of literature were reviewed in this review, it is expected to aid researchers in closing this gap in the future.</span>

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.

Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

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.