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The visualization of future generation Wireless Communication Network WCN redirects the presumption of onward innovations, the fulfillment of user demands in the form of high data rates, energy efficiency, low latency, and long-range services. To content these demands, various technologies such as massive MIMO Multiple Input Multiple Output, UDN Ultra Dense Network, spectrum sharing, D2D Device to Device communication were improvised in the next generation WCN. In comparison to previous technologies, these technologies exhibit flat architecture, the involvement of clouds in the network, centralized architecture incorporating small cells which creates vulnerable breaches initiating menaces to the security of the network. The half-duplex attack is another threat to the WCN, where the resource spoofing mechanism is attained in the downlink phase of D2D communication. Instead of triggering an attack on both uplink and downlink, solely downlink is targeted by the attacker. This scheme allows the reduced failed attempt rate of the attacker as compared to the conventional attacks. The analysis is determined on the basis of Poissons distribution to determine the probability of failed attempts of half duplex attack in contrast to a full duplex attack
With the evolution of WCN (Wireless communication networks), the absolute fulfillment of security occupies the fundamental concern. In view of security, we have identified another research direction based on the attenuation impact of rain in WCN. An
With the Rise of Adversarial Machine Learning and increasingly robust adversarial attacks, the security of applications utilizing the power of Machine Learning has been questioned. Over the past few years, applications of Deep Learning using Deep Neu
Ransomware, a type of malicious software that encrypts a victims files and only releases the cryptographic key once a ransom is paid, has emerged as a potentially devastating class of cybercrimes in the past few years. In this paper, we present RAPTO
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Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of infected models will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger. Currently, most exist