Detection of Botnet Traffic using Deep Learning Approach

Srinarayani, K; Padmavathi, B.; Kavitha, D.

doi:10.1109/icscds56580.2023.10104633

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…That is why CNN and LSTM have been selected as DL models to detect intrusion in IoMT. The Equation (11) shows that CNN's role is exclusive in this experiment.…”

Section: Model Selectionmentioning

confidence: 89%

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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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“…That is why CNN and LSTM have been selected as DL models to detect intrusion in IoMT. The Equation (11) shows that CNN's role is exclusive in this experiment.…”

Section: Model Selectionmentioning

confidence: 89%

“…While this methodology achieves 98.34% accuracy, it detects only Botnet attacks. The Botnet attacks are usually made through sequential data [11]. An LSTM or BiLSTM network is enough to detect this attack accurately [12].…”

Section: Related Workmentioning

confidence: 99%

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

Faruqui,

Yousuf,

Whaiduzzaman

et al. 2023

Electronics

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“…DL algorithms have a limited range of applications due to their computational complexity and data requirements [74]. Furthermore, because of their tendency for overfitting, DL may perform excellently on training data but poorly on new or untested data [75].…”

Section: Deep Learning Overviewmentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

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Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

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Enhanced DGA detection in Botnet traffic: leveraging N-Gram, topic modeling, and attention BiLSTM

Harishkumar,

Bhuvaneswaran

2024

Peer-to-Peer Netw. Appl.

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Detection of Botnet Traffic using Deep Learning Approach

Cited by 5 publications

References 15 publications

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

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

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Enhanced DGA detection in Botnet traffic: leveraging N-Gram, topic modeling, and attention BiLSTM

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