Topic
- Computing and Processing
- Components, Circuits, Devices and Systems
- Communication, Networking and Broadcast Technologies
- Power, Energy and Industry Applications
- Signal Processing and Analysis
- Robotics and Control Systems
- General Topics for Engineers
- Fields, Waves and Electromagnetics
- Engineered Materials, Dielectrics and Plasmas
- Bioengineering
- Transportation
- Photonics and Electrooptics
- Engineering Profession
- Aerospace
- Geoscience
- Nuclear Engineering
- Career Development
- Emerging Technologies
- Telecommunications
- English for Technical Professionals
Mohammed Abuhamad
Affiliation
Department of Computer Science, Loyola University Chicago, Chicago, USA
Topic
Adversarial Attacks,Attack Success Rate,Deep Learning,Deep Learning Models,Threat Model,Adversarial Examples,Adversarial Perturbations,Defense Techniques,ImageNet Dataset,Structural Similarity Index Measure,Target Model,Adversarial Training,Amount Of Noise,Attack Success,Benign Samples,Black-box Attacks,CIFAR-100 Dataset,Class Activation Maps,Deep Models,Deep Neural Network,Interpretation Of Maps,Learning Algorithms,Model Interpretation,Network Packets,Noise Rate,Onset Time,Packet Size,Top-5 Accuracy,Training Dataset,Accuracy Of Model,Adversarial Setting,Adversary Model,Affect Model Performance,Amount Of Time,Attack Behavior,Attack Methods,Attack Scenarios,Autonomous Vehicles,Average Amount Of Time,Batch Mode,Batch Normalization,Benign Ones,Bilevel Optimization,Broad Range Of Scenarios,Central Server,Certain Amount Of Time,Classifier Training,Complex Models,Confidence Score,Convolutional Feature Maps,
Biography
Mohammed Abuhamad received the PhD degree in computer science from the University of Central Florida in 2020 and the PhD degree in electrical and computer engineering from INHA University in 2020. He is currently an assistant professor of computer science with Loyola University Chicago. His research interests include AI or deep learning based applications in information security, software and mobile or IoT security, and adversarial machine learning.