WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches by Shehla Gul, Sobia Arshad, Sanay Muhammad Umar Saeed, Adeel Akram, Muhammad Awais Azam

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A deep learning based intrusion detection system for CAN vehicle based on combination of triple attention mechanism and GGO algorithm by Hongwei Yang, Mehdi Effatparvar

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Research on multi-user identity recognition based on Wi-Fi sensing by Zhongcheng WEI, Wei CHEN, Yanhu DONG, Bin LIAN, Wei WANG, Jijun ZHAO

“…With the advancement of wireless sensing technology, research on Wi-Fi-based identity recognition has garnered significant attention in fields such as human-computer interaction and home security.While identity recognition based on Wi-Fi signals has achieved initial success, it is currently primarily suitable for scenarios involving individual user behavior.Identity recognition for multiple users in concurrent behavior scenarios still faces a series of challenges, including issues related to mutual interference between users and poor model robustness.Therefore, a Wiblack system for recognizing multiple user identities in a concurrent distribution behavior scenario was proposed.The core idea was to train a multi-branch deep neural network (Wiblack-Net) to extract unique features for each individual user.Firstly, the common features among multiple users were extracted using the backbone network.Then, a binary classifier was assigned to each user to determine the presence of the target user within a given group, thereby achieving identity recognition for multiple users based on concurrent behavior.In addition, experiments comparing Wiblack with several independent binary classification models and a single multiclassification model were conducted to analyze operational efficiency.System performance experimental results demonstrate that when simultaneously identifying the identities of three users, Wibalck achieves an average accuracy of 92.97%, an average precision of 93.71%, an average recall of 93.24%, and an average F1 score of 92.43%.…”
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PLANT DISEASE DETECTION USING DEEP LEARNING by Amrita, Sanjay Kumar Nayak, Rajiv Kumar, Sakshi Tomar

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Deep Learning-Based Image Watermarking Using Catalan Transform and Non-Negative Matrix Factorization by Md. Fahim Hossain Saikat, Md. Al-Mamun Provath, Kaushik Deb, Pranab Kumar Dhar, Tetsuya Shimamura

“…The main contribution of the proposed method is its new combination of the Catalan transform (CT) and non-negative matrix factorization (NMF) with an artificial neural network (ANN) for enhanced watermark embedding and extraction. …”
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Integration of OWL Password-Authenticated Key Exchange Protocol to Enhance IoT Application Protocols by Yair Rivera Julio, Angel Pinto Mangones, Juan Torres Tovio, María Clara Gómez-Álvarez, Dixon Salcedo

“…Its one-round exchange model and resistance to both passive and active attacks make it particularly well-suited for constrained devices and dynamic network topologies. The originality of the proposal lies in embedding OWL directly into protocols like CoAP, enabling secure session establishment as a native feature rather than as an auxiliary security layer. …”
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A Multicomponent Face Verification and Identification System by Athanasios Douklias, Ioannis Zorzos, Evangelos Maltezos, Vasilis Nousis, Spyridon Nektarios Bolierakis, Lazaros Karagiannidis, Eleftherios Ouzounoglou, Angelos Amditis

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Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection by Iman Akour, Nour Mohamed, Said Salloum

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GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence by Mohd Anjum, Ashit Kumar Dutta, Ali Elrashidi, Sana Shahab, Asma Aldrees, Zaffar Ahmed Shaikh, Abeer Aljohani

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IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning by Qasim Kadhim, Ahmed Qassem Ali Sharhan Al-Sudani, Inas Amjed Almani, Tawfeeq Alghazali, Hasan Khalid Dabis, Atheer Taha Mohammed, Saad Ghazi Talib, Rawnaq Adnan Mahmood, Zahraa Tariq Sahi, Yaqeen Mezaal

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A Deep Learning-Based Ensemble Framework for Robust Android Malware Detection by Sainag Nethala, Pronoy Chopra, Khaja Kamaluddin, Shahid Alam, Soltan Alharbi, Mohammad Alsaffar

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Lightweight Interpretable Deep Learning Model for Nutrient Analysis in Mobile Health Applications by Zvinodashe Revesai, Okuthe P. Kogeda

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AnomLite: Efficient binary and multiclass video anomaly detection by Anna K. Zvereva, Mariam Kaprielova, Andrey Grabovoy

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A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans by Abdullah Al-Saleh, Ghanshyam G. Tejani, Shailendra Mishra, Sunil Kumar Sharma, Seyed Jalaleddin Mousavirad

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Binary classification of Low-Rate DoS attacks using Long Short-Term Memory Feed-Forward (LSTM-FF) Intrusion Detection System (IDS) by Suhaila ZeinElabideen Omer, Fazirulhisyam Hashim, Aduwati Sali, Faisul Arif Ahmad

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DaGAM-Trans: Dual graph attention module-based transformer for offline signature forgery detection by Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir

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Detection of UAV GPS Spoofing Attacks Using a Stacked Ensemble Method by Ting Ma, Xiaofeng Zhang, Zhexin Miao

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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

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Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform by Sajjad Maleki Lonbar, Akram Beigi, Nasour Bagheri, Nasour Bagheri, Pedro Peris-Lopez, Carmen Camara

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Smart Real-time Attendance System for Nigerian Universities by Mikailu Habila, Faseki N. Francisca, Luwani Ishaya, Muhammed Kabir Ahmed, Umar A. Muhammad, Haruna P. Charles

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