IoT Attacks Research Articles

A voting-based trustworthy distributed IoT attack detection model

A voting-based trustworthy distributed IoT attack detection model

An Efficient Intrusion Detection System using a Multiscale Deep Bi-Directional GRU Network to Detect Blackhole Attacks in IoT based WSNs

An Efficient Intrusion Detection System using a Multiscale Deep Bi-Directional GRU Network to Detect Blackhole Attacks in IoT based WSNs

Ejection of Real-Time Malicious Intrusion and Attacks in IoT Empowered Infrastructure

The project recognizes the pervasive threat of computer viruses, malware, and hostile attacks on computer networks, highlighting the critical role of intrusion detection as a proactive defense technology. The project introduces a novel approach based on deep learning to identify and mitigate cybersecurity vulnerabilities and breaches in IoT-driven cyber-physical systems, aiming for enhanced security measures. The project's objective is to elevate intrusion detection beyond the limitations of traditional systems by addressing issues like accuracy, detection effectiveness, and reducing false positives. This emphasizes the advancement and innovation in cybersecurity. To achieve the project's goals, the method employs a generative adversarial network, a cutting-edge deep learning technique. Additionally, it distinguishes itself by contrasting unsupervised and deep learning-based discriminative approaches, showcasing a comprehensive and effective approach to cybersecurity. In our project, we successfully implemented an ensemble method to boost predictive accuracy by integrating multiple individual models. Particularly noteworthy is the inclusion of a hybrid architecture, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), denoted as CNN+LSTM. This hybrid model achieved an impressive accuracy of 99% when applied to the KDD-Cup dataset, underscoring the efficacy of our ensemble technique for intrusion detection in IoT- based cybersecurity infrastructures.

ENHANCING THE RESILIENCE OF IOT NETWORKS: STRATEGIES AND MEASURES FOR MITIGATING DDOS ATTACKS

The Internet of Things (IoTs) are emerging and become a vital need in our daily routine. The privacy protection and insecurity of these IoT-based devices face many challenges. Distributed denial of service (DDoS) attacks in IoT networks become a significant growing challenge that is addressed in this research. The resilience and strategy for IoT devices due to distributed denial of service (DDoS) attacks assess current security measures by proposing modern procedures to upgrade the strength of IoT frameworks. This article proposes a mechanism that mitigiates the effects of DDoS attacks in IoTs, that cause significant destruction to existing systems. Utilizing secondary data from Kaggle, the machine is trained and tested. Our proposed approach incorporates descriptive statistics, correlations, t-tests, chi-square tests, and regression analyses to supply a systematic understanding of IoT security by critically analyzing the existing variants of numerous DDoS attacks, Security issues in IoTs, and creation of them in Botnets or zombies. Our findings show that the proposed security techniques are viable and detection rates correlate with security viability. The proposed model asses various network threat and cybersecurity arrangements for mitigating DDoS attacks in IoT’s and outperforms the previously implemented Web Application Firewall (WAF), Bot Mitigation, Resource Prioritisation, and Content Delivery Networks (CDNs)based DDoS mitigation techniques by 80.5%, 88%, 86% in terms of effectiveness, T-test, chi test, and correlation.

Towards a Deep Learning Approach for IoT Attack Detection Based on a New Generative Adversarial Network Architecture and Gated Recurrent Unit

Towards a Deep Learning Approach for IoT Attack Detection Based on a New Generative Adversarial Network Architecture and Gated Recurrent Unit

An Efficient Self Attention-Based 1D-CNN-LSTM Network for IoT Attack Detection and Identification Using Network Traffic

In the last ten years, the IoT has played a crucial role in society’s digital transformation. However, because of the wide range of devices it encompasses, it is also facing increased security vulnerabilities. This research presents a novel mechanism called the Self-attention-based 1D-CNN-LSTM, which uses Convolutional Neural Networks (CNNs) combined with a Long Short-Term Memory (LSTM) model enhanced with a Self-Attention mechanism for detecting IoT attacks. The proposed mechanism achieves high accuracy and efficiently differentiates malicious and benign network traffic. By employing Shapley Additive Explanations (SHAP), we identified important predictive features from the preprocessed data, which were retrieved using CICFlowmeter. This has strengthened the dependability of the model. In addition, we enhanced the model by training it on a smaller collection of features, resulting in shorter training time while preserving accuracy. We have also generated nine augmented IoT tabular datasets named CIC-BCCC-NRC_TabularIoTAttack-2024 from accessible IoT datasets to evaluate the model’s robustness and showcase its efficacy in IoT security.

PIoT‐fortifying IoT device identity and data access: A security framework empowered by blockchain

AbstractThe IoT, which refers to the process in which billions of devices are connected to the Internet, has emerged as an essential component of today's society. Yet, the safety of data exchange and the identification of devices in IoT networks have emerged as significant concerns in recent years. Also, there will be a significant drop in the effectiveness of the conventional consensus process. This study presents a novel consensus technique, Proof of IoT (PIoT), to address the security and authentication‐related issues in the IoT Infrastructure. It uses blockchain‐based smart contracts to securely transfer the data and uniquely identify the device on the IoT‐based network. PIoT uses a reputation assessment technique that involves complex cryptographic NuCypher‐based security computations to authenticate and update the data across IoT devices. This contract assures that only authorized parties can access the information recorded in the blockchain. A reward‐based filtering technique is developed for PIoT to identify malicious nodes with harmful actions, preventing them from contributing to consensus. Compared to specific conventional methods, PIoT performs better in simulations. Compared with LPBFT, DAC4SH Consensus, the Point of Interaction in blockchain‐based PIoT improved by 74.7% and 96.8%, respectively. When compared to DAC4SH, PIoT shows an overall 15% improvement in the security of the blockchain under the IoT. In addition to providing a secure method of communication between IoT devices, the given study also provides security against various IoT attacks.

Towards Detection of DDoS Attacks in IoT with Optimal Features Selection

Towards Detection of DDoS Attacks in IoT with Optimal Features Selection

AI-based IoT attack filtering methods in the context of connected vehicles

With the rapid development of connected vehicle technology, the volume of data generated in the context of connected vehicles is exhibiting an exponential growth trend compared to the traditional automotive industry. This data is closely related to the security of connected vehicles. However, if threats such as malicious intrusions, information tampering, and data theft cannot be effectively resisted, they will pose significant risks and hazards to users, the entire automotive industry, and society. Therefore, researching and implementing security measures for connected vehicles becomes crucial to ensure the sustainable development and widespread application of connected vehicle technology. In this regard, this paper proposes an optimized IoT attack filtering method based on random forest and neural network algorithms in deep learning to predict and filter malicious attacks. The CICIOT2023 dataset is used for training and evaluation, and results are analyzed based on accuracy, recall, precision, and F1 scores. A comparison between models constructed using random forest and neural networks reveals that while both perform similarly in terms of accuracy and precision, the model built with neural networks significantly outperforms random forest in terms of recall and F1 score. Therefore, for IoT attack filtering methods targeted at connected vehicle scenarios, the algorithm using neural networks for attack identification and filtering is recommended, as it can better protect the confidentiality and security of data in connected vehicle scenarios, ensuring user driving safety and safeguarding societal transportation security.

Estimation of the Multifractal Spectrum Characteristics of Fractal Dimension of Network Traffic and Computer Attacks in IoT

Estimation of the Multifractal Spectrum Characteristics of Fractal Dimension of Network Traffic and Computer Attacks in IoT

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.

FLOW: A Robust Federated Learning Framework to Defend Against Model Poisoning Attacks in IoT

FLOW: A Robust Federated Learning Framework to Defend Against Model Poisoning Attacks in IoT

Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Timely detection of DDoS attacks in IoT with dimensionality reduction

Timely detection of DDoS attacks in IoT with dimensionality reduction

FedXPro: Bayesian Inference for Mitigating Poisoning Attacks in IoT Federated Learning

FedXPro: Bayesian Inference for Mitigating Poisoning Attacks in IoT Federated Learning

Cybersecurity Warning System Using Diluted Convolutional Neural Network Framework for IOT Attack Prevention

Cybersecurity Warning System Using Diluted Convolutional Neural Network Framework for IOT Attack Prevention

Time-Series Analysis on AIDE IoT Attack Data Unraveling Trends and Patterns for Enhanced Security

With the growing use of IoT devices, cyberattacks have increased against these devices, using various exploitable vulnerabilities. Cyberattacks are potentially ruinous events for business owners and the cost of a cyber-attack is not only financial, but companies also must spend time on recovering from the attacks. Through applied research we set out the latest findings and trends to provide new insights into the security of IoTs, focusing on the emerging nature of IoT attacks, frequency, and usual vulnerabilities, thus providing significance to cybersecurity and IoT defense, which will be beneficial to researchers, manufacturers, individuals, organizations, and governments. Although the literature on IoTs is quite rich to this day, however, there is currently no study that provides an in-depth analysis of patterns and frequencies of IoT attacks to discover insights into the most vulnerable times and systems. Compared to other related research on the security of IoTs, this applied research encompasses much more technical angles to the security of IoT. It begins with the comprehensive data acquisition from the Global Cyber Alliance’s (GCA) Automated IoT Defense Ecosystem (AIDE) and preprocessing to ensure data integrity and relevance. Followed by a thorough feature selection and engineering process. Python scripts and libraries were used for efficient data analysis. Unraveling Trends and Patterns for Enhanced Security are sensitive areas that have remained untouched by previous research works. Time-Series Analysis on AIDE IoT Attack Data used in this study provided critical insights into attack patterns by fields such as timestamps, attack duration, and login credentials, implying a huge scientific and technological uncover of an intricate and evolving landscape of IoT security threats and attacks.

RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-Day Attacks in IoT

RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-Day Attacks in IoT

Log Poisoning Attacks in IoT: Methodologies, Evasion, Detection, Mitigation, and Criticality Analysis

Log Poisoning Attacks in IoT: Methodologies, Evasion, Detection, Mitigation, and Criticality Analysis

An analytical approach to network with IoT security using NodeMCU access point-prevention technique

This paper focuses on a very pertinent issue in the field of cybersecurity, particularly concerning the increased use of public Wi-Fi and the proliferation of IoT devices. The scenario, where an attacker captures user details through a malicious access point set up through a NodeMCU, is a practical illustration of a Man-in-the-Middle (MitM) attack. These attacks are especially concerning in the context of smart homes and smart cities, where security breaches can lead to severe privacy invasions and disruptions of essential services. The NodeMCU, being a low-cost, open-source IoT platform, is accessible for creating such malicious access points. It can be programmed to mimic legitimate Wi-Fi hotspots to intercept the data of unsuspecting users. This type of attack can enable unauthorized access to sensitive information such as login credentials, personal data, and even control over connected IoT devices. This approach is user-friendly and can significantly reduce the risk of users unknowingly connecting to malicious websites. The contribution of this paper includes a demonstration and analysis of a common network and IoT attack and a novel method for detecting fraudulent URLs, particularly those used in phishing attacks. By addressing these aspects, the paper makes a significant contribution to the field of cybersecurity and helps foster a more secure and resilient network and IoT ecosystem.