Proceedings of the International Conference on Applied Cybersecurity (ACS) 2023 760

This book presents the proceedings of the International Conference on Applied Cyber Security 2023 (ACS23), held in Dubai on the April 29, containing seven original contributions. Cybersecurity is continuously attracting the world’s attention and has gained in awareness and media coverage in the last decade. Not a single week passes without a major security incident that affects a company, sector, or governmental agencies. Most of the contributions are about applications of machine learning to accomplish several cybersecurity tasks, such as malware, network intrusion, and spam email detection. Similar trends of increasing AI applications are consistent with the current research and market trends in cybersecurity and other fields. We divided this book into two parts; the first is focused on malicious activity detection using AI, whereas the second groups AI applications to tackle a selection of cybersecurity problems. This book is suitable for cybersecurity researchers, practitioners, enthusiasts, as well as fresh graduates. It is also suitable for artificial intelligence researchers interested in exploring applications in cybersecurity. Prior exposure to basic machine learning concepts is preferable, but not necessary. Preface Conference Organization Contents Malicious Activity Detection Using AI DroidDissector: A Static and Dynamic Analysis Tool for Android Malware Detection 1 Introduction 2 Static Analysis Tool 3 Dynamic Analysis Tool 3.1 Feature Extraction 4 Conclusions References Android Malware Detection Using Control Flow Graphs and Text Analysis 1 Introduction 2 Related Work 3 Framework 3.1 Dataset 3.2 Data Extraction and Preprocessing 4 Experimental Results 5 Conclusion References NTFA: Network Flow Aggregator 1 Introduction 2 Related Work 3 Methodology 4 Evaluation 5 Conclusion References AI Applications in Cybersecurity A Password-Based Mutual Authentication Protocol via Zero-Knowledge Proof Solution 1 Introduction 2 Related Work 3 Proposed Technique 4 Scheme Analysis and Performance 5 Conclusion References A Cross-Validated Fine-Tuned GPT-3 as a Novel Approach to Fake News Detection 1 Introduction 2 Related Work 3 Dataset 4 Model Description 5 Fine-Tuning 6 Results 7 Validation 8 Conclusion References Enhancing Efficiency of Arabic Spam Filtering Based on Gradient Boosting Algorithm and Manual Hyperparameters Tuning 1 Introduction 2 Related Works 3 Proposed Spam Filter 3.1 Pre-processing and Feature Extraction 3.2 Classification 4 Experimentation 4.1 Dataset 4.2 Results 5 Conclusion References Cyberbullying: A BERT Bi-LSTM Solution for Hate Speech Detection 1 Introduction 2 Related Work 3 Proposed Approach 3.1 Preprocessing 3.2 BERT-Bi-LSTM 4 Experiment and Results 5 Conclusion References Author Index