2022· International Journal of Artificial Intelligence & Digital Transformation· Vol 5, pp. 01-15· 0 citations
TL;DR
A self-healing Cloud–IoT architecture enhanced by adversarial machine learning (AML) to autonomously detect, mitigate, and recover from malicious disruptions is proposed, proving the effectiveness of AML-driven self-healing mechanisms for next-generation distributed systems.
Abstract
The rapid expansion of Cloud–IoT ecosystems has introduced unprecedented scalability and flexibility but also created complex attack surfaces vulnerable to evolving cyber threats. Traditional intrusion detection and fault-tolerance techniques struggle to address adversarial attacks that exploit machine-learning models and disrupt IoT service continuity. This paper proposes a self-healing Cloud–IoT architecture enhanced by adversarial machine learning (AML) to autonomously detect, mitigate, and recover from malicious disruptions. The framework integrates adversarial-resilient anomaly detection, dynamic attack classification, and automated healing modules that leverage reinforcement learning (RL) and predictive models to restore system functionality with minimal human intervention. Experimental evaluations demonstrate improved robustness, reduced downtime, and higher detection accuracy under various adversarial scenarios, proving the effectiveness of AML-driven self-healing mechanisms for next-generation distributed systems. This work highlights essential design considerations and presents future directions for secure, autonomous, and resilient Cloud–IoT infrastructures.
The rapid expansion of the internet of things (IoT) has enabled large‐scale connectivity across healthcare, smart homes, industrial automation, and intelligent infrastructure. However, this growth has also increased the exposure of IoT environments to complex and evolving cyber threats. Traditional intrusion detection systems, particularly signature‐based approaches, are often ineffective against previously unseen attacks and struggle to adapt to the heterogeneous and dynamic nature of IoT traffic. To address these challenges, this study proposes a hybrid intrusion detection framework that combines generative adversarial learning with graph attention‐based modeling. The proposed model leverages adversarial data generation to improve the representation of minority attack classes and employs graph attention mechanisms to capture structural dependencies among communicating entities. The framework was evaluated using the UNSW‐NB15 dataset and compared with baseline deep learning models, including generative adversarial networks, graph convolutional networks, and graph attention networks. The proposed method achieved an accuracy of 81.23%, precision of 83.89%, recall of 78.01%, and F1‐score of 80.84% on the held‐out test set, while also reducing false‐positive and false‐negative rates relative to the comparison models. The results demonstrate the effectiveness of combining adversarial data augmentation with graph attention‐based representation learning under the controlled, offline evaluation conditions used in this study. Although the framework may be relevant to IoT and industrial cybersecurity applications, its scalability, real‐time performance, edge‐device feasibility, and effectiveness in operational environments require further experimental validation.
M. H. Alanazi, A. Mir, Asma A. Alhashmi et al.· Engineering Reports· 0 citations
This rapid growth of IoT has changed the landscape of today’s digital world by allowing devices
to communicate effectively, especially in different fields like healthcare, smart cities, industrial
control, and defense. Despite its advantages, IoT introduces significant security challenges due
to device heterogeneity, constrained computational resources, and weak security architectures,
making it highly vulnerable to cyber threats. Traditional vulnerability management approaches,
including rule-based intrusion detection systems and signature-based scanning, have proven
inadequate in addressing the dynamic and large-scale nature of IoT environments, as they are
largely reactive and incapable of detecting novel attack patterns. This study proposes an AI
driven vulnerability management framework that integrates anomaly detection techniques using
machine learning to enhance proactive threat identification and mitigation in IoT ecosystems.
The framework leverages publicly available datasets such as Bot-IoT, CIC-IoT, and UNSW
NB15 to train and evaluate models capable of distinguishing between normal and malicious
network behaviors. Various machine learning algorithms, including supervised and
unsupervised techniques, were implemented and assessed using performance metrics such as
accuracy, precision, recall, F1-score and false positive rate. The results demonstrate that AI
based models significantly outperform traditional methods in detecting previously unseen
threats, achieving high detection accuracy and reduced false positives. The proposed framework
integrates anomaly detection into a structured vulnerability management lifecycle encompassing
identification, prioritization and remediation of vulnerabilities. Generally, the study provides a
scalable and adaptive solution for improving IoT security, reducing system vulnerabilities, and
enhancing resilience against evolving cyber threats, with potential for future real-world
deployment across critical sectors.
Daniel Nafisatu Mshelbila· International Journal of Com...· 0 citations
This study proposes a hybrid machine learning-based intrusion detection and prevention framework for securing IoT networks that integrates Isolation Forest, Autoencoder, Extreme Gradient Boosting, and Bidirectional Long Short-Term Memory models within a stacked ensemble architecture to improve attack detection while reducing false-positive predictions.
Ruthwik Palem, Likhith Reddy Peketi, Vanathi M et al.· Cureus Journal of Computer S...· 0 citations
This SLR analyzes 50 research articles to provide a holistic view of AML threats in IIoT systems and identifies seven distinct attack types: gradient-based perturbations, GAN-generated samples, poisoning attacks, reinforcement learning-based (RL) strategies, saliency-based feature manipulation, false data injection, and hybrid approaches.
Abeer Alsaidlani, Muhammad Rashid, M. Aljabri· Italian National Conference...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices across critical domains including healthcare, smart cities, industrial control systems, and intelligent transportation has fundamentally transformed the cybersecurity threat landscape. The inherent characteristics of IoT environments, namely resource-constrained devices, heterogeneous architectures, and large-scale deployment, render traditional Intrusion Detection Systems (IDS) inadequate for the sophisticated and evolving attack vectors targeting these networks. Deep learning (DL) has emerged as a compelling paradigm for next-generation IoT IDS, offering automated feature extraction, temporal pattern recognition, and adaptive threat detection capabilities that address the limitations of conventional approaches.
This paper provides a thorough and systematic review of the existing DL methods for IoT intrusion detection. The paper explore the IoT architectural paradigms, outline a four layered taxonomy for types of IoT attacks across its three primary layers Perception, Network and Application as well as Adversarial Machine Learning attacks, and systematically review seven classes of DL architectures Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), Autoencoders, Generative Adversarial Networks (GAN), models based on Transformer architecture and Federated Learning frameworks. In a comparative review of forty peer-reviewed studies, we demonstrate that hybrid DL models provide excellent detection performance (99-100% classification accuracy on benchmark datasets) as well as practical viability for deployment with privacy-preserving Federated Learning for large-scale data. The study additionally highlights five enduring challenges class imbalance, adversarial vulnerability, zero-day detection limitations, computational constraints and the absence of standardized benchmarking protocols that together account for the gap between performance benchmarks and real-world deployment efficacy. It outlines future research avenues targeting on five key axes with a particular focus in the integration of Explainable AI (XAI), lightweight edge-deployable architectures, and adversarial robustness mechanisms. This survey identifies a structured reference to advance the state of IoT intrusion detection from research to operationally viable and deployable systems.
Mohammed Gharkan, Mustafa I. Hussien Al-Janabi, Obaid Salim· Al-Noor Journal of Engineeri...· 0 citations
A constraint-aware adversarially robust Internet of Things (IoT) traffic classification system with protocol validity, device behavior consistency, and manifold-aware training and evaluation is presented, demonstrating improved robustness, realism, and deployability compared to existing approaches.
