DDoSNet: A Deep Learning Model for detecting Network Attacks in Cloud Computing

Srilatha, Doddi; Thillaiarasu, N.

doi:10.1109/icirca54612.2022.9985524

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Widespread trials are prepared for various situations of the primary phase and the secondary phase besides two various approaches for the oversampling methods. The authors at [16] proposed a convincing IDS known as DDoSNet. The intellect method of PSO has been presented for the feature.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming

Aburasain

2024

IEEE Access

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Agriculture has played a significant role in the lifespan of human beings for their survival and the better economic growth of the country. Due to its intensified growth, smart agriculture has been a popular domain recently. This aggregates the benefits of any computing technologies, like wireless sensor networks (WSN), drones, Internet of Things (IoT), remote sensing, etc. The IoT system places sensors on agricultural areas to accumulate critical information for the crops and fields to increase the overall rate of productivity. While broadcasting the sensed information in the domains to the target, there is a chance of the existence of cyber-threats, which intruders design to attain access to the content being transferred. Intrusion detection in IoT-based smart farming using deep learning (DL) leverages the power of deep neural networks (DNN) to safeguard agricultural systems from cyber threats. DL algorithms can autonomously detect unauthorized and anomalies activities by analyzing data streams from sensors, IoT devices, and farm management systems. With this motivation, this study designs an Enhanced Black Widow Optimization with Hybrid Deep Learning Enabled Intrusion Detection (EBWO-HDLID) technique in the IoT-based Smart Farming environment. The proposed EBWO-HDLID technique captures complex patterns and detects significant intrusions, assuring the security and reliability of smart farming. In the presented EBWO-HDLID approach, the bald eagle search (BES) algorithm can be used for the feature selection process. For the intrusion detection and classification process, the EBWO-HDLID technique applies the HDL classification model. Finally, the EBWO algorithm can be used for the parameter tuning of the HDL technique. The experimental validation process demonstrates the satisfactory performance of the EBWO-HDLID model on two benchmark datasets: ToN-IoT, and Edge-IIoTset datasets.

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Section: Related Workmentioning

confidence: 99%

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming

Aburasain

2024

IEEE Access

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“…Mahmoud Said Elsayed [11] proposed a Recurrent Neural Network(RNN) with autoencoder for detecting network attacks mainly SDN network. The model has high metrics like recall, F-score, accuracy when compared with other ML techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Intrusion Detection System for Detection of DDoS Attacks in Cloud Environment

et al. 2023

Preprint

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A modern technology which enables users to construct services on demand is cloud computing. Due to its self-service functionality and on demand offerings, cloud computing has seen success. Some security-related risks are brought on by remote cloud-based data evaluation and maintenance, particularly with DDoS Attacks. These assaults are the result of intrusion efforts or hacker attempts to access data stored on the main server or moving between the client and server. Without the data owner's knowledge, the attacker acquires the data and alters it as needed. For the widespread adoption of cloud computing, it is crucial to develop defences against this assault. Over the past few years, intrusion detection (ID) in a cloud setting has drawn a lot of attention. Machine learning-based ID methods are among the most recent techniques that enable us to find unidentified assaults. However, building a cloud based ID system (IDS) that is resistant to a broad variety of unknown attacks remains difficult due to the scarcity of malicious samples and the quick evolution of diverse attacks. This study creates a deep feedforward neural network algorithm-based intruder detection system using Bat optimization. The CICDDOS2019 dataset is used by the suggested technique to analyze performance.

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