Towards Efficient Machine Learning Method for IoT DDoS Attack Detection

• URL: http://arxiv.org/abs/2408.10267v1
• Date: Fri, 16 Aug 2024 09:41:54 GMT
• Title: Towards Efficient Machine Learning Method for IoT DDoS Attack Detection
• Authors: P Modi,
• Abstract summary: DDoS attacks conducted with IoT devices can cause a significant downtime of applications running on the Internet. We propose a hybrid feature selection algorithm that selects only the most useful features and passes those features into an XGBoost model. Our model attains an accuracy of 99.993% on the CIC IDS 2017 dataset and a recall of 97.64 % on the CIC IoT 2023 dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rise in the number of IoT devices and its users, security in IoT has become a big concern to ensure the protection from harmful security attacks. In the recent years, different variants of DDoS attacks have been on the rise in IoT devices. Failure to detect DDoS attacks at the right time can result in financial and reputational loss for victim organizations. These attacks conducted with IoT devices can cause a significant downtime of applications running on the Internet. Although researchers have developed and utilized specialized models using artificial intelligence techniques, these models do not provide the best accuracy as there is always a scope of improvement until 100% accuracy is attained. We propose a hybrid feature selection algorithm that selects only the most useful features and passes those features into an XGBoost model, the results of which are explained using feature importances. Our model attains an accuracy of 99.993% on the CIC IDS 2017 dataset and a recall of 97.64 % on the CIC IoT 2023 dataset. Overall, this research would help researchers and implementers in the field of detecting IoT DDoS attacks by providing a more accurate and comparable model.

Related papers

• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Enhancing IoT Security Against DDoS Attacks through Federated Learning [0.0]
  Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Traditional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning.
  arXiv  Detail & Related papers  (2024-03-16T16:45:28Z)
• 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)
• IoTScent: Enhancing Forensic Capabilities in Internet of Things Gateways [45.44831696628473]
  This paper presents IoTScent, an open-source forensic tool that enables IoT gateways and Home Automation platforms to perform IoT traffic capture and analysis. IoTScent is specifically designed to operate over IEEE5.4-based traffic, which is the basis for many IoT-specific protocols such as Zigbee, 6LoWPAN and Thread. This work provides a comprehensive description of the IoTScent tool, including a practical use case that demonstrates the use of the tool to perform device identification from Zigbee traffic.
  arXiv  Detail & Related papers  (2023-10-05T09:10:05Z)
• Publishing Efficient On-device Models Increases Adversarial Vulnerability [58.6975494957865]
  In this paper, we study the security considerations of publishing on-device variants of large-scale models. We first show that an adversary can exploit on-device models to make attacking the large models easier. We then show that the vulnerability increases as the similarity between a full-scale and its efficient model increase.
  arXiv  Detail & Related papers  (2022-12-28T05:05:58Z)
• 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)
• SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment [20.586904296213007]
  We propose an adversarial self-supervised architecture for detecting malware in Internet of Things networks, SETTI. In the SETTI architecture, we design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and ASelf-MDS. To validate the attack and defence algorithms, we conduct experiments on two recent IoT datasets: IoT23 and NBIoT.
  arXiv  Detail & Related papers  (2022-04-16T10:10:59Z)
• Revisiting IoT Device Identification [4.451756374933898]
  Internet-of-Things (IoT) devices are known to be the source of many security problems. We explore how to accurately identify IoT devices based on their network behavior.
  arXiv  Detail & Related papers  (2021-07-16T11:01:45Z)
• Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach [8.641714871787595]
  Machine learning (ML) has emerged as one potential solution due to the abundance of data generated and available for IoT devices and networks. This paper proposes an optimized ML-based framework to detect attacks on IoT devices in an effective and efficient manner. Experimental results show that the proposed optimized framework has a high detection accuracy, precision, recall, and F-score.
  arXiv  Detail & Related papers  (2020-12-16T16:39:55Z)
• 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.