Modern computers rely on USB and HDMI ports for connecting external peripherals and display devices.Despite their built-in security measures, these ports remain susceptible to passive power-based side-channel attacks.This paper presents a new class of attacks that exploit power consumption patterns at these ports to infer GPU activities.We develop a custom device that plugs into these ports and demonstrates that its high-resolution power measurements can drive successful inferences about GPU processes, such as neural network computations and video rendering.The ubiquitous presence of USB and HDMI ports allows for discreet placement of the device, and its non-interference with data channels ensures that no security alerts are triggered.Our findings underscore the need to reevaluate and strengthen the current generation of HDMI and USB port security defenses. CCS Concepts• Security and privacy → Side-channel analysis and countermeasures; Software reverse engineering.

The transformative influence of Large Language Models (LLMs) is profoundly reshaping the Artificial Intelligence (AI) technology domain.Notably, ChatGPT distinguishes itself within these models, demonstrating remarkable performance in multi-turn conversations and exhibiting code proficiency across an array of languages.In this paper, we carry out a comprehensive evaluation of ChatGPT's coding capabilities based on what is to date the largest catalog of coding challenges.Our focus is on the python programming language and problems centered on data structures and algorithms, two topics at the very foundations of Computer Science.We evaluate ChatGPT for its ability to generate correct solutions to the problems fed to it, its code quality, and nature of run-time errors thrown by its code.Where ChatGPT code successfully executes, but fails to solve the problem at hand, we look into patterns in the test cases passed in order to gain some insights into how wrong ChatGPT code is in these kinds of situations.To infer whether ChatGPT might have directly memorized some of the data that was used to train it, we methodically design an experiment to investigate this phenomena.Making comparisons with human performance whenever feasible, we investigate all the above questions from the context of both its underlying learning models (GPT-3.5 and GPT-4), on a vast array sub-topics within the main topics, and on problems having varying degrees of difficulty.

Deep Neural Networks (DNNs) are at the heart of many of today's most innovative technologies. With companies investing lots of resources to design, build and optimize these networks for their custom products, DNNs are now integral to many companies' tightly guarded Intellectual Property. As is the case for every high-value product, one can expect bad actors to increasingly design techniques aimed to uncover the architectural designs of proprietary DNNs. This paper investigates if the power draw patterns of a GPU on which a DNN runs could be leveraged to glean key details of its design architecture. Based on ten of the most well-known Convolutional Neural Network (CNN) architectures, we study this line of attack under varying assumptions about the kind of data available to the attacker. We show the attack to be highly effective, attaining an accuracy in the 80 percentage range for the best performing attack scenario.

Statistics suggests, proceeding towards IoT generation, is increasing IoT devices at a drastic rate. This will be very challenging for our present-day network infrastructure to manage, this much of data. This may risk, both security and traffic collapsing. We have proposed an infrastructure with Fog Computing. The Fog layer consists two layers, using the concepts of Service oriented Architecture (SOA) and the Agent based composition model which ensures the traffic usage reduction. In order to have a robust and secured system, we have modified the Fog based agent model by replacing the SOA with secured Named Data Network (NDN) protocol. Knowing the fact that NDN has the caching layer, we are combining NDN and with Fog, as it can overcome the forwarding strategy limitation and memory constraints of NDN by the Agent Society, in the Middle layer along with Trust management.