利用重构机器学习模型检测智能电网网络中的 DDoS 攻击

Sardar Shan Ali Naqvi, Yuancheng Li, Muhammad Uzair
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引用次数: 0

摘要

网络攻击给智能电网网络带来了巨大挑战,这主要是由于存在多个将消费者与电网连接起来的多向通信设备。分布式拒绝服务(DDoS)是可能影响智能电网的网络攻击之一,在这种攻击中,大量受到攻击的电网通信设备/节点向智能电网网络发送错误数据和请求,导致智能电表、数据服务器和状态估算器中断,最终影响为终端用户提供的服务。基于机器学习的策略在解决保护网络免受 DDoS 攻击的挑战方面显示出独特的优势。不过,部署基于机器学习的技术的一个显著障碍是,每当出现新的攻击类别时,都需要重新训练模型。实际上,破坏智能电网的正常运行确实是不可取的。为了有效应对这一挑战,并在不造成重大干扰的情况下检测 DDoS 攻击,我们建议部署重构深度学习技术。我们提出的技术的一个主要优点是,即使在完全部署之后,在引入新的攻击类别时也能将干扰降至最低。我们训练了多个深层和浅层重构模型,分别获得每种攻击类型的表征,并通过基于特定类别重构误差的分类进行攻击检测。我们使用两个公认的专门针对 DDoS 攻击的标准数据库(包括其子集)进行了多次实验,对我们的技术进行了严格评估。随后,我们将我们的成果与同一领域内流行的六种方法进行了比较评估。结果表明,我们的技术获得了更高的准确性,而且在引入新的攻击类别时,无需重新训练整个模型。这种方法不仅能提高智能电网网络的安全性,还能确保正常运行的稳定性和可靠性,保护关键基础设施免受不断发展的网络攻击。随着智能电网的快速发展,我们的方法提出了一种稳健、自适应的方法,以克服网络攻击带来的持续挑战。
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DDoS attack detection in smart grid network using reconstructive machine learning models
Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous compromised communication devices/nodes of the grid flood the smart grid network with false data and requests, leading to disruptions in smart meters, data servers, and the state estimator, ultimately effecting the services for end-users. Machine learning-based strategies show distinctive benefits in resolving the challenge of securing the network from DDoS attacks. Regardless, a notable hindrance in deploying machine learning-based techniques is the requirement of model retraining whenever new attack classes arise. Practically, disrupting the normal operations of smart grid is really discouraged. To handle this challenge effectively and detect DDoS attacks without major disruptions, we propose the deployment of reconstructive deep learning techniques. A primary benefit of our proposed technique is the minimum disruption during the introduction of a new attack class, even after complete deployment. We trained several deep and shallow reconstructive models to get representations for each attack type separately, and we performed attack detection by class-specific reconstruction error-based classification. Our technique experienced rigid evaluation via multiple experiments using two well-acknowledged standard databases exclusively for DDoS attacks, including their subsets. Later, we performed a comparative estimation of our outcomes against six methods prevalent within the same domain. Our outcomes reveal that our technique attained higher accuracy, and notably eliminates the requirement of a complete model retraining in the event of the introduction of new attack classes. This method will not only boost the security of smart grid networks but also ensure the stability and reliability of normal operations, protecting the critical infrastructure from ever-evolving network attacks. As smart grid is advancing rapidly, our approach proposes a robust and adaptive way to overcome the continuous challenges posed by network attacks.
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