在根据训练后的掩码计算重要性分数时,需要考虑到,该分数为[0,1]之内的连续值,而‘图中某条边是否存在’需要通过二进制数值表示,直接使用连续值会引入噪声,导致“introduced evidence”问题(《Explainability in Graph Neural Networks: A Taxonomic Survey》)。但是,使用二进制数值又损失了很多可以区分重要性的信息,可...
Neural NetworkInternet of ThingsBlockchainCybersecurityUser ManagementAccess credentialsThe Internet of Things (IoT) enables increased connectivity between devices; however, this benefit also intrinsically increases cybersecurity risks as cyber attackers are provided with expanded network access and additional ...
Deep learning, which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as multi-layer perceptron, convolutional neural network, ...
RNNSecureNet: Recurrent neural networks for Cyber security use-cases Recurrent neural network (RNN) is an effective neural network in solving very complex supervised and unsupervised tasks. There has been a significant improvement in RNN field such as natural language processing, speech processing, ...
What is an artificial neural network? Machine learning vs deep learning vs neural networks: What’s the difference? Latest about Neural Network What is deep learning? ByGeorge Fitzmauricelast updatedApril 25, 2024 In-depthA brief guide to deep learning – the phenomenon behind some of today's ...
Cybersecurity领域DNN可解释性工作参见: LEMNA: Explaining Deep Learning based Security Applications. CCS'18 Explaining Graph Neural Networks for Vulnerability Discovery. AISec‘21 (【论文笔记】) Evaluating Explanation Methods for Deep Learning in Security. Euro S&P 20 ILLUMINATI : Towards Explaining Graph...
Internet of Things (IoT) driven systems have been sharply growing in the recent times but this evolution is hampered by cybersecurity threats like spoofing, denial of service (DoS), distributed denial of service (DDoS) attacks, intrusions, malwares, authentication problems or other fatal attacks....
However, these initiatives present vulnerabilities in their designs that cyberattackers can exploit to cause brain damage. Specifically, the literature has documented the applicability of neural cyberattacks, threats capable of stimulating or inhibiting individual neurons to alter spontaneous neural activity....
Recently, several deep learning models have been employed to detect a cyber threat such as network attack, malware infiltration, or phishing website; nevertheless, they suffer from not being explainable to security experts. Security experts not only do need to detect the incoming threat but also ...
OWASP also started work on an AI Security and Privacy Guide [35]. 2.2. DLIDS Several research groups have proposed deep learning for network intrusion detection in recent years with very good results [1,2,3,4,5,6,7,8,9,10,11,36]. A survey by Ullah et al. [7] already analyzed ...