Skill UI

A comprehensive guide detailing the entire workflow for analyzing embedded firmware. It covers hardware-level extraction methods (UART, JTAG, SPI dump), filesystem analysis (SquashFS, JFFS2), binary reverse engineering using tools like Ghidra, and identifying common vulnerabilities (Buffer Overflow, Command Injection). Ideal for security researchers and embedded systems engineers.

A comprehensive guide for analyzing firmware images from embedded systems, IoT devices, routers, and specialized components like UEFI/BIOS. This skill covers advanced techniques including filesystem extraction, binary reverse engineering (using Ghidra/IDA), rootkit detection, and deep forensic analysis to uncover backdoors, implants, and supply chain compromises. Essential for cybersecurity professionals investigating compromised hardware.

A comprehensive guide detailing the workflow for firmware reverse engineering. It uses tools like binwalk to identify embedded filesystems (e.g., SquashFS, CramFS), perform entropy analysis to locate hidden data, and extract components. The process covers deep inspection, credential discovery, and reporting for IoT and embedded security assessments.

This guide provides a comprehensive framework for positioning mobile and embedded systems manuscripts for ACM MobiSys. It details venue fit, contribution framing, necessary real-device evidence, and how to differentiate the work from related conferences (e.g., MobiCom, UbiComp). Use this when your research value depends on surviving real-world device constraints, sensing, or edge computing environments.

This guide provides a specialized framework for authors targeting the IACR Conference on Cryptographic Hardware and Embedded Systems (CHES). It details the specific scope, evidence bar, and framing techniques required for cryptographic hardware papers, ensuring submissions meet the high standards of the community. It helps reposition work from journal or general CS styles.

This guide provides a comprehensive audit checklist for designing and evaluating sensor-based systems (SenSys). It mandates rigorous, real-world measurements across five core axes: energy efficiency, on-device latency distribution, ground truth accuracy, deployment realism, and embedded AI footprint. It ensures that research results are based on measured hardware performance, not simulations or idealized baselines.

A comprehensive guide for authors submitting to top systems conferences (e.g., SenSys, MobiCom). It details how to structure the Related Work section by sweeping multiple literature lanes (IoT, embedded AI, networking), proving citation venues, and most importantly, distinguishing your novel mechanism from the single nearest prior system with concrete, measurable differences. Essential for academic publication rigor.