An Explainable Deep Learning Framework for Resilient Intrusion Detection in IoT-Enabled Transportation Networks

Oseni, Ayodeji; Moustafa, Nour; Creech, Gideon; Sohrabi, Nasrin; Strelzoff, Andrew; Tari, Zahir; Linkov, Igor

doi:10.1109/tits.2022.3188671

Cited by 48 publications

(16 citation statements)

References 36 publications

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“…Furthermore, another research’s [ 95 ] objective included improving IDSs in IoT networks using DL. This project addressed the difficulty of interpreting the judgments made by AI algorithms used in detecting intrusions in IoT networks.…”

Section: Deep Learning—iot Network Anomaly Detectionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Rafique,

Abdallah,

Musa

et al. 2024

Sensors

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With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of anomalies. When AI and the IoT are combined, anomaly detection becomes more effective, enhancing the reliability, efficacy, and integrity of IoT systems. AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection, and back-door exploits. Intelligent Intrusion Detection Systems (IDSs) are imperative in IoT devices, which help detect anomalies or intrusions in a network, as the IoT is increasingly employed in several industries but possesses a large attack surface which presents more entry points for attackers. This study reviews the literature on anomaly detection in IoT infrastructure using machine learning and deep learning. This paper discusses the challenges in detecting intrusions and anomalies in IoT systems, highlighting the increasing number of attacks. It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. From this survey, it is concluded that further development of current systems is needed by using varied datasets, real-time testing, and making the systems scalable.

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Section: Deep Learning—iot Network Anomaly Detectionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Rafique,

Abdallah,

Musa

et al. 2024

Sensors

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“…The raw network traffic needs to be gathered in the first stage. Utilize the BoTNeTIoT-L01 (19,36,37,38,39) publicly accessible dataset and UNSW-NB15 (20,40,41,42,43) as the sources of raw traffic in this study. Attacks are denoted by 0 in the dataset class label and normal samples by 1.…”

Section: Input Data Collection and Feature Extractionmentioning

confidence: 99%

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things

Lavanya,

Anushiya

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Federated Learning (FL) has established as a potentially effective practice for cyberattack identification in the last decade, particularly for Internet-of-Things (IoT) structures. FL can increase learning effectiveness, lower transmission overheads, and enhance intrusion detection system (IDS) privacy by spreading the learning process amongst IoT gateways. The absence of labeled data and the distinction of data features for training pose significant obstacles to the deployment of FL in IoT networks. In this research, suggest an Autoencoder based Deep Federated Transfer Learning (ADFTL) to conquer these obstacles. Specifically, Create an ADFTL model utilizing two AutoEncoders (AEs) as the basis. Initially the supervised mode is employed to train the first AE (AE1) on the source datasets while the unsupervised mode is employed to train the second AE (AE2) on the target datasets without label information. The bottleneck layer, or latent representation, of AE2 is forced via the transfer learning method in an effort to resemble the latent representation of AE1. Subsequently, assaults in the input in the target domain are identified employing the latent representation of AE2. Particularly, Weighted k-Subspace Network (WkSNC) clustering is proposed for clustering the dataset and Boosted Sine Cos method (BSCM) is used for feature selection. The requirement that the network datasets utilized in current studies have identical properties is significant since it restricts the effectiveness, adaptability, and scalability of IDS. Nonetheless, the suggested structure can tackle these issues by sharing the "knowledge" of learning among distinct deep learning (DL) simulations, even in cases when their datasets possess dissimilar features. Comprehensive tests on current BoT-IoT datasets demonstrate that the suggested structure can outperform the most advanced DL-based methods by more than 6%

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“…Achieving sufficient interpretability and explainability is essential for garnering public and regulatory trust and accepting DL-based transportation models. Progress in developing more interpretable DL architectures tailored explicitly for the transportation domain is necessary [233]. Hybrid models that combine DL with symbolic reasoning may offer greater transparency and oversight.…”

Section: B Explainable Dl-enabled Itsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

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Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

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An Explainable Deep Learning Framework for Resilient Intrusion Detection in IoT-Enabled Transportation Networks

Cited by 48 publications

References 36 publications

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

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