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Using Machine Learning for Detecting Timing Side-Channel Attacks in SD by Faizan Shoaib, Yang Wai Chow et al

Software-Defined Networking (SDN) is a networking technology that allows for the programming and efficient management of networks. Due to the separation of the data plane and the control plane, SDN is prone to timing side-channel attacks. The adversary can use timing information to obtain data about the network such as flow tables, routes, controller types, ports, and so on. The focus of current mitigation strategies for timing side-channel attacks is largely on minimizing them through network architectural changes. This adds considerable overhead to the SDNs and makes establishing the origin of the attack a challenge. In this paper, we propose a machine learning-based approach for detecting timing side-channel attacks and identifying their source in SDNs. We adopt the machine learning methodology for this solution since it delivers faster and more accurate output. As opposed to conventional methods, it can precisely detect timing side-channel activity in SDN and determine the attacker

New Inception attack leaks sensitive data from all AMD Zen CPUs

Researchers have discovered a new and powerful transient execution attack called Inception that can leak privileged secrets and data using unprivileged processes on all AMD Zen CPUs, including the latest models.

Zoom Calls Vulnerable to Keystroke Eavesdropping: Researchers Reveal Alarming Threat

A New AI-Driven Cyberattack Can Steal Your Data Just By Listening to You Type

A new study published by a number of British researchers reveals a hypothetical cyberattack in which a hacker could leverage recorded audio of a person typing to steal their personal data. The attack uses a home-made deep-learning-based algorithm that can acoustically analyze keystroke noises and automatically decode what that person is typing. The research showed that typing could be accurately de-coded in this fashion 95 percent of the time.

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