3 citations · 5 across the 4 of their papers we have counts for
4 papers
SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
Anum Paracha, Junaid Arshad, Mohamed Ben Farah +1
Data poisoning attacks are a potential threat to machine learning (ML) models, aiming to manipulate training datasets to disrupt their performance. Existing defenses are mostly des…
Outlier-Oriented Poisoning Attack: A Grey-box Approach to Disturb Decision Boundaries by Perturbing Outliers in Multiclass Learning
Anum Paracha, Junaid Arshad, Mohamed Ben Farah +1
Poisoning attacks are a primary threat to machine learning models, aiming to compromise their performance and reliability by manipulating training datasets. This paper introduces a…
A First Look at Privacy Analysis of COVID-19 Contact Tracing Mobile Applications
Muhammad Ajmal Azad, Junaid Arshad, Ali Akmal +4
Today's smartphones are equipped with a large number of powerful value-added sensors and features such as a low power Bluetooth sensor, powerful embedded sensors such as the digita…
A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
Junaid Arshad, Muhammad Ajmal Azad, Khaled Salah +3
Internet of Things (IoT) is a disruptive technology with applications across diverse domains such as transportation and logistics systems, smart grids, smart homes, connected vehic…