( en | de | ru )

 

Disambiguation of "Abuhamad, Mohammed"

Author of the publication

copydeleteadd this publication to your clipboard
Identification and Analysis of the Spread of Misinformation on Social Media.

, , , , , , , , and . CSoNet, volume 14479 of Lecture Notes in Computer Science, page 361-372. Springer, (2023)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Mohammed Mohammed

Mohammed Mohammed

Mohammed Hamed Mohammed

Ammar Mohammed Mohammed

Mohammed Hamed Mohammed

 

Other publications of authors with the same name

Investigating the Effect of Traffic Sampling on Machine Learning-Based Network Intrusion Detection Approaches., , , , , and . IEEE Access, (2022)An Exploration of Geo-temporal Characteristics of Users' Reactions on Social Media During the Pandemic., , , and . CoRR, (2021)Leveraging Spectral Representations of Control Flow Graphs for Efficient Analysis of Windows Malware., , , and . AsiaCCS, page 1240-1242. ACM, (2022)ShellCore: Automating Malicious IoT Software Detection Using Shell Commands Representation., , , , , , , , and . IEEE Internet Things J., 9 (4): 2485-2496 (2022)Sensor-Based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Contemporary Survey., , , and . IEEE Internet Things J., 8 (1): 65-84 (2021)AUToSen: Deep-Learning-Based Implicit Continuous Authentication Using Smartphone Sensors., , , and . IEEE Internet Things J., 7 (6): 5008-5020 (2020)Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks., , , , and . CoRR, (2023)Black-box and Target-specific Attack Against Interpretable Deep Learning Systems., , , and . AsiaCCS, page 1216-1218. ACM, (2022)Code authorship identification using convolutional neural networks., , , , , and . Future Gener. Comput. Syst., (2019)Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning., , , , and . CoRR, (2022)