ChatGPT-4 with Code Interpreter can be used to solve introductory college-level vector calculus and electromagnetism problems Tanuj Kumar (tanuj.kumar@wisc.edu) Executive summary: We evaluated three modes of ChatGPT -- 3.5, 4, and 4 with Code Interpreter -- on a set of college-level engineering-math and electromagnetism problems, such as those often given to sophomore electrical engineering majors. We selected a set of 13 problems without first testing them with ChatGPT, and had ChatGPT solve them multiple times, using a fresh instance (chat) of ChatGPT each time. The problems range from elementary to medium-level. We were strict in our evaluation of ChatGPT's performance, marking a solution as incorrect if even a small part of the solution was wrong. Our major conclusions are: ChatGPT-4 with Code Interpreter (ChatGPT-4/CI), recently renamed Advanced Data Analysis, was able to satisfactorily solve most problems we tested most of the time. Qualitatively, one could give ChatGPT-4/CI a solid passing grade in introductory engineering math and electromagnetics.

In the recent years, Distributed Denial of Service (DDoS) attacks on Internet of Things (IoT) devices have become one of the prime concerns to Internet users around the world. One of the sources of the attacks on IoT ecosystems are botnets. Intruders force IoT devices to become unavailable for its legitimate users by sending large number of messages within a short interval. This study proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN) based Distributed Denial of Services (DDoS) attack detection framework with mutual correlation as feature selection method which produces a superior result when compared with Long Short Term Memory (LSTM) and simple ANN. Overall, the detection performance achieves approximately 99\% accuracy for the detection of attacks from botnets. In this work, we have designed and compared four different models where two are based on ANN and the other two are based on LSTM to detect the attack types of DDoS.