How To Mitigate And Defend Against DDoS Attacks In IoT Devices

• URL: http://arxiv.org/abs/2507.11772v1
• Date: Tue, 15 Jul 2025 22:21:19 GMT
• Title: How To Mitigate And Defend Against DDoS Attacks In IoT Devices
• Authors: Ifiyemi Leigha, Basak Comlekcioglu, Maria Pilar Bezanilla,
• Abstract summary: This paper analyzes the nature and impact of DDoS attacks such as those launched by the Mirai botnet.<n>It proposes layered mitigation strategies tailored to IoT environments.<n>The paper aims to help engineers and researchers understand and implement practical countermeasures to protect IoT infrastructures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed Denial of Service (DDoS) attacks have become increasingly prevalent and dangerous in the context of Internet of Things (IoT) networks, primarily due to the low-security configurations of many connected devices. This paper analyzes the nature and impact of DDoS attacks such as those launched by the Mirai botnet, and proposes layered mitigation strategies tailored to IoT environments. Key solutions explored include IPv6 Unique Local Addresses (ULA), edge computing, software-defined networking (SDN), honeypot deception, and machine learning-based intrusion detection systems. The paper aims to help engineers and researchers understand and implement practical countermeasures to protect IoT infrastructures.

Related papers

• Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks [0.34530027457862006]
  This paper investigates the application of machine learning techniques to enhance security in Edge-Computing-Assisted IoT environments.<n>It presents a comparative analysis of Random Forest, XGBoost, and LightGBM to address the dynamic and complex nature of botnet threats.<n>The results highlight the potential of machine learning to fortify IoT networks against emerging cybersecurity challenges.
  arXiv  Detail & Related papers  (2025-08-03T01:52:35Z)
• 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.<n>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.<n>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)
• Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies [49.57278643040602]
  Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure.<n>This paper offers a comprehensive survey of emerging DDoS attacks and detection strategies over the past decade.
  arXiv  Detail & Related papers  (2025-02-27T11:22:25Z)
• 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)
• 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)
• 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)
• 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)
• Forensics and security issues in the Internet of Things [6.422895251217666]
  This paper reviews forensic and security issues associated with IoT in different fields.<n>Most IoT devices are vulnerable to attacks due to a lack of standardized security measures.<n>To fulfill the security-conscious needs of consumers, IoT can be used to develop a smart home system.
  arXiv  Detail & Related papers  (2023-09-06T04:41:48Z)
• 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)
• Smart Home, security concerns of IoT [91.3755431537592]
  The IoT (Internet of Things) has become widely popular in the domestic environments. People are renewing their homes into smart homes; however, the privacy concerns of owning many Internet connected devices with always-on environmental sensors remain insufficiently addressed. Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices.
  arXiv  Detail & Related papers  (2020-07-06T10:36:11Z)
• Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks [30.68108039722565]
  Internet of Things (IoT) devices are susceptible to being compromised and being part of a new type of stealthy Distributed Denial of Service (DDoS) attack, called Mongolian DDoS. This study proposes a novel anomaly-based Intrusion Detection System (IDS) that is capable of timely detecting and mitigating this emerging type of DDoS attacks.
  arXiv  Detail & Related papers  (2020-06-15T00:54:49Z)

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.