Internet of Things attack detection using hybrid Deep Learning Model

Sahu, Amiya Kumar; Sharma, Suraj; Tanveer, Mohammad; Raja, Rohit

doi:10.1016/j.comcom.2021.05.024

Cited by 157 publications

(37 citation statements)

References 20 publications

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“…Furthermore, they used the NSL-KDD [144] dataset and the UNSW-NB15 [145] dataset for model training, which had a shorter detection time and higher classifier accuracy. In [146], a CNN was used to extract an accurate feature representation of data and then classify them using the LSTM model for cyber-attack detection, which can outperform other DL methods for detecting intrusions. DL-based solutions depend on the available datasets to achieve the required detection accuracy.…”

Section: Malware and Anomaly Detectionmentioning

confidence: 99%

Federated Learning in Edge Computing: A Systematic Survey

Abreha

Hayajneh

Serhani

2022

Sensors

155

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Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.

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Section: Malware and Anomaly Detectionmentioning

confidence: 99%

Federated Learning in Edge Computing: A Systematic Survey

Abreha

Hayajneh

Serhani

2022

Sensors

155

View full text Add to dashboard Cite

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“…The authors were able to attain a 92.99% accuracy rate [40]. IoT attack detection mechanisms based on CNN and LSTM to detect DDoS attacks in IoT were proposed by Sahu et al The authors evaluated the developed model using the dataset from the Stratosphere lab published in 2020 and they achieved an accuracy of 96% for the simulated attack detection [41]. In addition, Roy et al proposed a bi-directional LSTM deep learning technique for IDS in IoT.…”

Section: Related Workmentioning

confidence: 99%

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Alimi

Ouahada

Abu‐Mahfouz

et al. 2022

JSAN

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The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart objects (or devices) have exposed the IoT network to various security challenges and vulnerabilities which include manipulative data injection and cyberattacks such as a denial of service (DoS) attack. Any form of intrusive data injection or attacks on the IoT networks can create devastating consequences on the individual connected device or the entire network. Hence, there is a crucial need to employ modern security measures that can protect the network from various forms of attacks and other security challenges. Intrusion detection systems (IDS) and intrusion prevention systems have been identified globally as viable security solutions. Several traditional machine learning methods have been deployed as IoT IDS. However, the methods have been heavily criticized for poor performances in handling voluminous datasets, as they rely on domain expertise for feature extraction among other reasons. Thus, there is a need to devise better IDS models that can handle the IoT voluminous datasets efficiently, cater to feature extraction, and perform reasonably well in terms of overall performance. In this paper, an IDS based on redefined long short-term memory deep learning approach is proposed for detecting DoS attacks in IoT networks. The model was tested on benchmark datasets; CICIDS-2017 and NSL-KDS datasets. Three pre-processing procedures, which include encoding, dimensionality reduction, and normalization were deployed for the datasets. Using key classification metrics, experimental results obtained show that the proposed model can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works.

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“…The CNN and the RNN capture local and temporal features, respectively. Convolution Neural Network (CNN) was utilized to extract the accurate representation of data that were classified by Long Short-Term Memory (LSTM) Model [32].…”

Section: Previous Workmentioning

confidence: 99%

Model fusion of deep neural networks for anomaly detection

2021

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Network Anomaly Detection is still an open challenging task that aims to detect anomalous network traffic for security purposes. Usually, the network traffic data are large-scale and imbalanced. Additionally, they have noisy labels. This paper addresses the previous challenges and utilizes million-scale and highly imbalanced ZYELL’s dataset. We propose to train deep neural networks with class weight optimization to learn complex patterns from rare anomalies observed from the traffic data. This paper proposes a novel model fusion that combines two deep neural networks including binary normal/attack classifier and multi-attacks classifier. The proposed solution can detect various network attacks such as Distributed Denial of Service (DDOS), IP probing, PORT probing, and Network Mapper (NMAP) probing. The experiments conducted on a ZYELL’s real-world dataset show promising performance. It was found that the proposed approach outperformed the baseline model in terms of average macro Fβ score and false alarm rate by 17% and 5.3%, respectively.

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Internet of Things attack detection using hybrid Deep Learning Model

Cited by 157 publications

References 20 publications

Federated Learning in Edge Computing: A Systematic Survey

Federated Learning in Edge Computing: A Systematic Survey

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Model fusion of deep neural networks for anomaly detection

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