A Systematic Mapping Study on SDN Controllers for Enhancing Security in IoT Networks

• URL: http://arxiv.org/abs/2408.01303v1
• Date: Fri, 2 Aug 2024 14:44:15 GMT
• Title: A Systematic Mapping Study on SDN Controllers for Enhancing Security in IoT Networks
• Authors: Charles Oredola, Adnan Ashraf,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: The increase in Internet of Things (IoT) devices gives rise to an increase in deceptive manipulations by malicious actors. These actors should be prevented from targeting the IoT networks. Cybersecurity threats have evolved and become dynamically sophisticated, such that they could exploit any vulnerability found in IoT networks. However, with the introduction of the Software Defined Network (SDN) in the IoT networks as the central monitoring unit, IoT networks are less vulnerable and less prone to threats. %Although, the SDN itself is vulnerable to several threats. Objective: To present a comprehensive and unbiased overview of the state-of-the-art on IoT networks security enhancement using SDN controllers. Method: We review the current body of knowledge on enhancing the security of IoT networks using SDN with a Systematic Mapping Study (SMS) following the established guidelines. Results: The SMS result comprises 33 primary studies analyzed against four major research questions. The SMS highlights current research trends and identifies gaps in the SDN-IoT network security. Conclusion: We conclude that the SDN controller architecture commonly used for securing IoT networks is the centralized controller architecture. However, this architecture is not without its limitations. Additionally, the predominant technique utilized for risk mitigation is machine learning.

Related papers

• Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway [45.70482328441101]
  This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic by analyzing network behavior at the edge. We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic. This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
  arXiv  Detail & Related papers  (2025-04-22T09:40:05Z)
• Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review [0.0]
  The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices. Traditional Intrusion Detection Systems (IDS) struggle to adapt to IoT networks' dynamic and evolving nature and threat patterns. This systematic review examines the application of Deep Reinforcement Learning (DRL) to enhance IDS in IoT settings.
  arXiv  Detail & Related papers  (2025-04-20T00:55:58Z)
• Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things [11.185300073739098]
  Traditional Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature of IoT networks. This paper explores how Machine Learning (ML) and Deep Learning (DL) techniques can significantly enhance IDS performance in IoT environments.
  arXiv  Detail & Related papers  (2025-04-09T18:52:15Z)
• 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)
• Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning [64.20847540439318]
  Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks. Some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network. This paper explores the solution of combining GNNs with DRL to build a resilient network.
  arXiv  Detail & Related papers  (2025-01-19T15:22:17Z)
• A Comprehensive Analysis of Routing Vulnerabilities and Defense Strategies in IoT Networks [0.0]
  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.
  arXiv  Detail & Related papers  (2024-10-17T04:38:53Z)
• Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices [38.16309790239142]
  Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. The limited computational resources available on Internet of Things (IoT) devices make it challenging to deploy conventional computing-based IDSs. We propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset.
  arXiv  Detail & Related papers  (2024-06-04T20:36:21Z)
• 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)
• Blockchain and Deep Learning-Based IDS for Securing SDN-Enabled Industrial IoT Environments [11.04540520633849]
  We propose an integrated method for better detecting and preventing security threats associated with software-defined networking (SDN)-based IIoT architectures. The two components consist of a convolutional neural network-based Intrusion Detection System (IDS) implemented as an SDN application and a injection-based system (BS) to empower application layer and network layer security. The proposed IDS exhibits superior classification accuracy in both binary and multiclass categories.
  arXiv  Detail & Related papers  (2023-12-31T11:49:42Z)
• Classification of cyber attacks on IoT and ubiquitous computing devices [49.1574468325115]
  This paper provides a classification of IoT malware. Major targets and used exploits for attacks are identified and referred to the specific malware. The majority of current IoT attacks continue to be of comparably low effort and level of sophistication and could be mitigated by existing technical measures.
  arXiv  Detail & Related papers  (2023-12-01T16:10:43Z)
• IoT Device Identification Based on Network Communication Analysis Using Deep Learning [43.0717346071013]
  The risk of attacks on an organization's network has increased due to the growing use of less secure IoT devices. To tackle this threat and protect their networks, organizations generally implement security policies in which only white listed IoT devices are allowed on the network. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network.
  arXiv  Detail & Related papers  (2023-03-02T13:44:58Z)
• IoT Botnet Detection Using an Economic Deep Learning Model [0.0]
  This paper proposes an economic deep learning-based model for detecting IoT botnet attacks along with different types of attacks. The proposed model achieved higher accuracy than the state-of-the-art detection models using a smaller implementation budget and accelerating the training and detecting processes.
  arXiv  Detail & Related papers  (2023-02-03T21:41:17Z)
• Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network [0.0]
  Internet of Things (IoT) has altered living by controlling devices/things over the Internet. To bring down the IoT network, attackers can utilise these devices to conduct a variety of network attacks. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabelled dataset.
  arXiv  Detail & Related papers  (2022-07-16T11:12:32Z)
• Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning [73.85267769520715]
  Internet of Things (IoT) with its growing number of deployed devices and applications raises significant challenges for network maintenance procedures. We formulate a problem of autonomous maintenance in IoT networks as a Partially Observable Markov Decision Process. We utilize Deep Reinforcement Learning algorithms (DRL) to train agents that decide if a maintenance procedure is in order or not and, in the former case, the proper type of maintenance needed.
  arXiv  Detail & Related papers  (2020-12-31T11:19:51Z)
• Lightweight IoT Malware Detection Solution Using CNN Classification [2.288885651912488]
  The security aspect of IoT devices is an infant field, which is why it is our focus in this paper. We developed a system that can recognize malicious behavior of a specific IoT node on the network. Through convolutional neural network and monitoring, we were able to provide malware detection for IoT using a central node that can be installed within the network.
  arXiv  Detail & Related papers  (2020-10-13T10:56:33Z)
• IoT Device Identification Using Deep Learning [43.0717346071013]
  The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers. The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization's network also increases the risk of attacks. In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network.
  arXiv  Detail & Related papers  (2020-02-25T12:24:49Z)

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.