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#4015 PC Machine Learning for PC Cybersecurity

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#4015 PC Machine Learning for PC Cybersecurity #4015  Describe a software application to monitor "normal" pc activity on a given machine, to be used for machine learning of a cybersecurity application for that pc Monitoring "normal" activity to train a cybersecurity machine learning (ML) model requires a software application often referred to as a Behavioral Monitoring Agent or a Host-Based Sensor . In an enterprise context, this is the core of Endpoint Detection and Response (EDR) . For your application, the software's primary job is to create a high-fidelity "digital twin" of your PC's standard operations. This involves capturing telemetry across several distinct system layers. 1. The Sensor Architecture (Data Ingestion) To train an ML model effectively, the monitor must act as a transparent "flight recorder" for the operating system. Key Monitoring Modules: Process Monitor: Tracks every execution, parent-child process relationships, ...
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#4014 Probabilistic Computing with FPGAs; +FPGA-Based Cybersecurity

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#4014 Probabilistic Computing with FPGAs; +FPGA-Based Cybersecurity #4014  Given the data for a 15-city Traveling Salesman Problem, how could an FPGA be programmed to provide a Probabilistic Computing solution? For a 15-city Traveling Salesman Problem (TSP), a traditional "brute force" approach requires checking (n-1)! / 2  paths. For 15 cities, that is over 43 billion routes—a heavy lift even for fast CPUs, but a perfect playground for the massive parallelism of an FPGA (Field Programmable Gate Array) using Probabilistic Computing . Instead of searching for the absolute global minimum through exhaustive iteration, a probabilistic approach—specifically an Ising Model or a Simulated Annealing architecture—uses randomness to "settle" into a near-optimal solution very quickly. 1. Mapping TSP to an Ising Hamiltonian To solve TSP on an FPGA probabilistically, you must first translate the distance matrix into an Ising Model . You represent the problem as a grid of ...
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