… malicious relay attacks in cooperative LTE-A network in PHY layer via unsupervised approach
of machine learning … machine learning algorithms. We consider outlier detection as an …

… to apply machine learning for malicious relay detection in … is determined by unsupervised
learning techniques that employ one-… We consider that the relay node possesses various relay …

… We propose unsupervised learning that exploits network diversity to detect … malicious relays.
However, we considered that there exists at least one reliable path (with no malicious relays…

… of maliciously manipulated packets over one hop, but could not identify malicious nodes …
In [15][16], we utilized the network diversity to identify malicious relay nodes in a mesh network. …

… that are maliciously injected by an attacker to trigger the transmission line protective relays.
The … We now outline an unsupervised deep learning approach to anomaly detection using an …

… contents of packets generated by a malicious agent which infiltrates the communication …
detection in electrical substation circuits via unsupervised machine learning,” in Proc. 2016 …

… We conjecture that, due to some quirk in the topology, learning the pattern for the non-malicious
fault at relay 93 inhibits the ability to detect an injection attack at the same location. We …

… , differential, and distance relays. The aforementioned ideas offer … Without the need to inject
malicious code, command … However, since we would need to identify malicious patterns in …

… -recorded data related to a real fault are maliciously repeated. In each case, during
normal … Leung, “A deep and scalable unsupervised machine learning system for cyber-attack …

… as deep reinforcement learning, supervised learning, and unsupervised learning, are …
learning (DRL) is chosen and focuses on deep learning approach to detect malicious assaults on …