Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities

Saheed, Yakub Kayode; Abdulganiyu, Oluwadamilare Harazeem; Tchakoucht, Taha Ait

doi:10.1016/j.asoc.2024.111434

Cited by 15 publications

(1 citation statement)

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“…Finally, there's an approach DIDDOS [49] and SDN that uses a type of computer unit called gated recurrent units to spot and identify cyberattacks known as distributed denial of service attacks. [41], [45], and [48,50] authored by Saheed, Abdulganiyu, and Ait-Tchakoucht explore various approaches, including modified genetic algorithms, LSTM networks, and hybrid ensemble learning, for intrusion detection in IoT networks and industrial sensor systems. Additionally, [47] introduces a novel lightweight ResNet50-CNN transfer learning-based approach for intrusion detection in cyber-physical systems.…”

Section: Related Workmentioning

confidence: 99%

Detecting Unbalanced Network Traffic Intrusions With Deep Learning

Pavithra,

Venkata Vikas

2024

IEEE Access

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The growth of cyber threats demands a robust and adaptive intrusion detection system (IDS) capable of effectively recognizing malicious activities from network traffic. However, the existing imbalance of class in network data possess a significant challenge to traditional IDS. To overcome these challenges, this project proposes a novel hybrid Intrusion Detection System using machine learning algorithms, which includes XGBoost, Long Short-Term Memory (LSTM), Mini-VGGNet, and AlexNet, which is used to handle the unbalanced network traffic data. Furthermore, the Random Forest Regressor is used to ascertain the importance of features for enhancing detection accuracy and interpretability. Addressing the inherent class imbalance in network data is crucial for ensuring the IDS's effectiveness. The proposed system employs a combination of oversampling techniques for minority classes and under sampling techniques for majority classes during data preprocessing. This balanced representation of network traffic data helps prevent the IDS from being biased towards the majority class and improves its ability to detect rare or novel intrusions. The utilization of Random Forest Regressor for feature extraction serves a dual purpose. It helps identify the most relevant features within the network traffic data that contribute significantly to detecting intrusions. It enables the system to prioritize and focus on these important features during model training, thereby enhancing detection accuracy while reducing computational complexity. This research contributes to the ongoing efforts to mitigate cyber threats and safeguard critical network infrastructures.

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