A Network Intrusion Detection Method Based on Improved Bi-LSTM in Internet of Things Environment

Fan, Xingliang

doi:10.4018/ijitsa.319737

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The Botnet attacks are usually made through sequential data [11]. An LSTM or BiLSTM network is enough to detect this attack accurately [12]. CNNs are designed for grid-like data structures representing images [13].…”

Section: Related Workmentioning

confidence: 99%

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SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization

Faruqui,

Yousuf,

Whaiduzzaman

et al. 2023

Electronics

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The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price in the market. As a result, IoMTs cannot employ advanced security algorithms to defend against cyber-attacks. IoMT has become easy prey for cybercriminals due to its access to valuable data and the rapidly expanding market, as well as being comparatively easier to exploit.As a result, the intrusion rate in IoMT is experiencing a surge. This paper proposes a novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed is the first IDS that protects IoMT devices from malicious image data and sequential network traffic. This innovative IDS ensures an optimized detection rate by trade-off between False Positive Rate (FPR) and Detection Rate (DR). It detects intrusions with an average accuracy of 97.63% with average precision and recall, and has an F1-score of 98.47%, 97%, and 97.73%, respectively. In summary, SafetyMed has the potential to revolutionize many vulnerable sectors (e.g., medical) by ensuring maximum protection against IoMT intrusion.

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Section: Related Workmentioning

confidence: 99%

“…The equivalency (12) shows that RNN and LSTM are mutually inclusive. That is why their union is logically equivalent to either RNN or LSTM.…”

Section: Model Selectionmentioning

confidence: 99%

SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization

Faruqui,

Yousuf,

Whaiduzzaman

et al. 2023

Electronics

View full text Add to dashboard Cite

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An improved federated transfer learning model for intrusion detection in edge computing empowered wireless sensor networks

Raja,

Sakthi,

Vimalnath

et al. 2024

Concurrency and Computation

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SummaryIntrusion Detection (ID) is a critical component in cybersecurity, tasked with identifying and thwarting unauthorized access or malicious activities within networked systems. The advent of Edge Computing (EC) has introduced a paradigm shift, empowering Wireless Sensor Networks (WSNs) with decentralized processing capabilities. However, this transition presents new challenges for ID due to the dynamic and resource‐constrained nature of Edge environments. In response to these challenges, this study presents a pioneering approach: an Improved Federated Transfer Learning Model. This model integrates a pre‐trained ResNet‐18 for transfer learning with a meticulously designed Convolutional Neural Network (CNN), tailored to the intricacies of the NSL‐KDD dataset. The collaborative synergy of these models culminates in an Intrusion Detection System (IDS) with an impressive accuracy of 96.54%. Implemented in Python, the proposed model not only demonstrates its technical prowess but also underscores its practical applicability in fortifying EC‐empowered WSNs against evolving security threats. This research contributes to the ongoing discourse on enhancing cybersecurity measures within emerging computing paradigms.

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A Network Intrusion Detection Method Based on Improved Bi-LSTM in Internet of Things Environment

Cited by 2 publications

References 19 publications

SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization

SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization

An improved federated transfer learning model for intrusion detection in edge computing empowered wireless sensor networks

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