Signal, Image and Video Processing ( IF 2.0 ) Pub Date : 2023-11-17 , DOI: 10.1007/s11760-023-02813-7
Ahmed Abdulmunem Mhmood , Özgür Ergül , Javad Rahebi
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This study introduces an innovative smart grid (SG) intrusion detection system, integrating Game Theory, swarm intelligence, and deep learning (DL) to protect against complex cyber-attacks. This method balances training samples by employing conditional DL using Game Theory and CGAN. The Aquila optimizer (AO) algorithm selects features, mapping them onto the dataset and converting them into RGB color images for training a VGG19 neural network. AO optimizes meta-parameters, enhancing VGG19 accuracy. Testing on the NSL-KDD dataset generates remarkable results: 99.82% accuracy, 99.69% sensitivity, and 99.76% precision in detecting attacks. Notably, the CGAN technique significantly improves performance over GAN. Importantly, this method surpasses various deep learning techniques such as VGG19, CNN-GRU, CNN-GRU-FL, LSTM, and CNN in accuracy. Addressing the critical need for robust SG intrusion detection, our work merges Game Theory, swarm intelligence, and deep learning, yielding superior security accuracy. The novelty of this study is implanted in the integrated approach, distinguishing it from previous research and contributing to effective protection against cyber threats in smart grids.
中文翻译:
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使用改进的 VGG19 深度神经网络架构和 Aquila 优化器算法检测智能电网的网络攻击
本研究介绍了一种创新的智能电网 (SG) 入侵检测系统,该系统集成了博弈论、群体智能和深度学习 (DL),以防范复杂的网络攻击。该方法通过使用博弈论和 CGAN 的条件深度学习来平衡训练样本。Aquila 优化器 (AO) 算法选择特征,将它们映射到数据集并将其转换为 RGB 彩色图像以训练 VGG19 神经网络。AO优化元参数,提高VGG19精度。对 NSL-KDD 数据集的测试产生了显着的结果:检测攻击的准确度为 99.82%,灵敏度为 99.69%,精确度为 99.76%。值得注意的是,CGAN 技术比 GAN 显着提高了性能。重要的是,该方法在准确度上超越了VGG19、CNN-GRU、CNN-GRU-FL、LSTM和CNN等各种深度学习技术。为了满足强大的 SG 入侵检测的关键需求,我们的工作融合了博弈论、群体智能和深度学习,产生了卓越的安全准确性。这项研究的新颖之处在于集成方法,使其与之前的研究区分开来,有助于有效防御智能电网中的网络威胁。