Distributed denial-of-service (DDoS) attack detection has been widely studied in the past decade by academia. Despite progress having been made, recent surveys show that detection in environments such ...

This project implements a GAN-based approach for detecting anomalies in smart meter readings using the Large-scale Energy Anomaly Detection (LEAD) dataset. The model uses LSTM-based Generator and ...

Detecting cancer in the earliest stages could dramatically reduce cancer deaths because cancers are usually easier to treat when caught early. To help achieve that goal, MIT and Microsoft researchers ...

Abstract: Data Imbalance is a prominent as well as challenging problem in the real-world datasets. Even though there are many methods to rectify this problem using supervised algorithms, data ...

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?

Information and communication technology (ICT) is crucial for maintaining efficient communications, enhancing processes, and enabling digital transformation. As ICT becomes increasingly significant in ...

Presence detection is a key part of many Automated Equipment Identification (AEI) systems and yard operations. They detect movement in a specific area or zone, often to trigger the start or stop of a ...

ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...

A couple of years ago, a curious, then-16-year-old hacker named Reynaldo Vasquez-Garcia was on his laptop at his Portland-area high school, seeing what computer systems he could connect to via the ...