A Comprehensive Analysis of Routing Vulnerabilities and Defense Strategies in IoT Networks

• URL: http://arxiv.org/abs/2410.13214v1
• Date: Thu, 17 Oct 2024 04:38:53 GMT
• Title: A Comprehensive Analysis of Routing Vulnerabilities and Defense Strategies in IoT Networks
• Authors: Kim Jae-Dong,
• Abstract summary: The Internet of Things (IoT) has revolutionized various domains, offering significant benefits through enhanced interconnectivity and data exchange. However, the security challenges associated with IoT networks have become increasingly prominent owing to their inherent vulnerability. This paper provides an in-depth analysis of the network layer in IoT architectures, highlighting the potential risks posed by routing attacks.
• Score: 0.0
• License:
• Abstract: The rapid expansion of the Internet of Things (IoT) has revolutionized various domains, offering significant benefits through enhanced interconnectivity and data exchange. However, the security challenges associated with IoT networks have become increasingly prominent owing to their inherent vulnerability. This paper provides an in-depth analysis of the network layer in IoT architectures, highlighting the potential risks posed by routing attacks, such as blackholes, wormholes, sinkholes, Sybil, and selective forwarding attacks. This study explores the unique challenges posed by the constrained resources, heterogeneity, and dynamic topology of IoT networks, which complicate the implementation of robust security measures. Various countermeasures, including trust-based mechanisms, Intrusion Detection Systems (IDS), and routing protocols, are evaluated for their effectiveness in mitigating these threats. This study also emphasizes the importance of considering misbehavior observation, trust management, and lightweight defense strategies in the design of secure IoT networks. These findings contribute to the development of comprehensive defense mechanisms tailored to the specific challenges of IoT environments.

Related papers

• Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities [0.0]
  Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. This study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments. Secure aggregation of locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information.
  arXiv  Detail & Related papers  (2025-02-14T23:11:51Z)
• A Review of the Duality of Adversarial Learning in Network Intrusion: Attacks and Countermeasures [0.0]
  Adversarial attacks, particularly those targeting vulnerabilities in deep learning models, present a nuanced and substantial threat to cybersecurity. Our study delves into adversarial learning threats such as Data Poisoning, Test Time Evasion, and Reverse Engineering. Our research lays the groundwork for strengthening defense mechanisms to address the potential breaches in network security and privacy posed by adversarial attacks.
  arXiv  Detail & Related papers  (2024-12-18T14:21:46Z)
• Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security [1.2369895513397127]
  Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. To efficiently secure IoT devices, real-time detection of intrusion systems is critical. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security.
  arXiv  Detail & Related papers  (2024-10-01T19:24:34Z)
• A Systematic Mapping Study on SDN Controllers for Enhancing Security in IoT Networks [0.0]
  We review the current body of knowledge on enhancing the security of IoT networks using SDN controllers. We conclude that the SDN controller architecture commonly used for securing IoT networks is the centralized controller architecture.
  arXiv  Detail & Related papers  (2024-08-02T14:44:15Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems [30.16714420093091]
  Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. This article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods.
  arXiv  Detail & Related papers  (2023-12-05T04:42:04Z)
• FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows [31.70913467854211]
  FLEE-GNN is a novel Federated Learning System for Edge-Enhanced Graph Neural Network. It combines the robustness and adaptability of graph neural networks with the privacy-conscious and decentralized aspects of federated learning. Results show the advancements of this approach to quantifying the resilience of multicommodity food flow networks.
  arXiv  Detail & Related papers  (2023-10-20T03:06:41Z)
• Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats [15.451585677257235]
  Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance.
  arXiv  Detail & Related papers  (2022-04-07T13:25:49Z)
• Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning [30.536369182792516]
  Device-type identification combined with data from vulnerability databases can pinpoint vulnerable IoT devices in a network. We present and evaluate two deep learning approaches to the reliable IoT device-type identification.
  arXiv  Detail & Related papers  (2021-02-16T14:49:34Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.