A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Network

Roy, Bipraneel; Cheung, Hon

doi:10.1109/atnac.2018.8615294

Cited by 144 publications

(70 citation statements)

References 10 publications

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“…Due to the capabilities of deep learning it has been applied in a variety of security domains, such as, [20,21], and [22] to identify security breaches. Furthermore, deep learning has proven its success in IoT security, it has been proven by successful implementation in studies [23,24] and [25].…”

Section: Motivation and Use Casesmentioning

confidence: 99%

“…Besides, the authors of study [25] have proposed a deep learning technique that enables intrusion detection in IoT networks using the Bidirectional LSTM Recurrent Neural Network (BLSTM RNN). The model has been evaluated using seven metrics, namely accuracy, precision, recall, f1-score, miscalculation rate, FAR, and detection time.…”

Section: Deep Learning and Iot Securitymentioning

confidence: 99%

“…Further, it helps to identify the impact of several attributes involved in security threats. Another study has highlighted that irrespective of using massive real time data the most common existing approaches are ineffective in classifying intrusions as they employ shallow learning [25]. Moreover, these time complexity issues can be resolved easily in deep learning approach by implementing GPU component, as it aids in real time processing and is highly efficient in analysis of the threat in real-time [39].…”

Section: Time Complexitymentioning

confidence: 99%

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Deep learning and big data technologies for IoT security

Amanullah

Habeeb

Nasaruddin

et al. 2020

Computer Communications

273

119

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Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. Contents * Corresponding author.

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Section: Motivation and Use Casesmentioning

confidence: 99%

Section: Deep Learning and Iot Securitymentioning

confidence: 99%

Section: Time Complexitymentioning

confidence: 99%

See 1 more Smart Citation

Deep learning and big data technologies for IoT security

Amanullah

Habeeb

Nasaruddin

et al. 2020

Computer Communications

273

119

View full text Add to dashboard Cite

show abstract

“…They achieved total recall, precision and f1 -score 92%,91% and 91% respectively for time 8 seconds using UNSW-NB15 dataset. Roy et al [31] proposed a novel deep learning technique for detecting attacks within the IoT network using Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM RNN). A multi-layer Deep Learning Neural Network is trained using a novel benchmark data set: UNSWNB15.…”

Section: International Journal Of Engineering and Advanced Technologymentioning

confidence: 99%

Unsw-Nb15 Dataset and Machine Learning Based Intrusion Detection Systems

Sonule¹,

Kalla²,

Jain³

et al. 2020

IJEAT

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The network attacks become the most important security problems in the today’s world. There is a high increase in use of computers, mobiles, sensors,IoTs in networks, Big Data, Web Application/Server,Clouds and other computing resources. With the high increase in network traffic, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. Machine learning algorithms intend to detect anomalies using supervised and unsupervised approaches.Both the detection techniques have been implemented using IDS datasets like DARPA98, KDDCUP99, NSL-KDD, ISCX, ISOT.UNSW-NB15 is the latest dataset. This data set contains nine different modern attack types and wide varieties of real normal activities. In this paper, a detailed survey of various machine learning based techniques applied on UNSW-NB15 data set have been carried out and suggested thatUNSW-NB15 is more complex than other datasets and is assumed as a new benchmark data set for evaluating NIDSs.

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“…The genetic algorithm enhances the detection capacity to 0.77 from 0.71. In [461], an Bi-directional LSTM based approach is applied for detecting intrusion in Iot network. The model learns to differentiate between normal and malicious traffic.…”

Section: A Deep Learning In Intrusion Detectionmentioning

confidence: 99%