Advancing Author Gender Identification in Modern Standard Arabic with Innovative Deep Learning and Textual Feature Techniques by Hanen Himdi, Khaled Shaalan

Get full text

Flash Attack Prognosis by Ensemble Supervised Learning for IoT Networks by M. Jagadeesh Babu, A.R Reddy

Get full text

Network-based intrusion detection using deep learning technique by Muhammad Farhan, Hafiz Waheed ud din, Saadat Ullah, Muhammad Sajjad Hussain, Muhammad Amir Khan, Tehseen Mazhar, Umar Farooq Khattak, Ines Hilali Jaghdam

“…These findings underscore the effectiveness of deep learning architectures enhanced with optimized feature selection in detecting network intrusions, making the proposed system a promising solution for securing critical infrastructure in sectors such as finance, healthcare, and government networks.…”
Get full text

Towards edge-collaborative, lightweight and privacy-preserving classification framework by Jinbo XIONG, Yongjie ZHOU, Renwan BI, Liang WAN, Youliang TIAN

“…Aiming at the problems of data leakage of perceptual image and computational inefficiency of privacy-preserving classification framework in edge-side computing environment, a lightweight and privacy-preserving classification framework (PPCF) was proposed to supports encryption feature extraction and classification, and achieve the goal of data transmission and computing security under the collaborative classification process of edge nodes.Firstly, a series of secure computing protocols were designed based on additive secret sharing.Furthermore, two non-collusive edge servers were used to perform secure convolution, secure batch normalization, secure activation, secure pooling and other deep neural network computing layers to realize PPCF.Theoretical and security analysis indicate that PPCF has excellent accuracy and proved to be security.Actual performance evaluation show that PPCF can achieve the same classification accuracy as plaintext environment.At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPCF has obvious advantages in terms of computational cost and communication overhead.…”
Get full text

Transformer-based tokenization for IoT traffic classification across diverse network environments by Firdaus Afifi, Faiz Zaki, Hazim Hanif, Nik Aqil, Nor Badrul Anuar

Get full text

(IoT) Network intrusion detection system using optimization algorithms by Luo Shan

“…Compared with traditional models like the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Support Vector Machine (SVM), the proposed framework significantly improves the sensitivity and generalization ability for detecting various types of attacks through dynamic feature selection and parameter optimization. …”
Get full text

Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier by Ali Saeed Almuflih, Ilyos Abdullayev, Sergey Bakhvalov, Rustem Shichiyakh, Bibhuti Bhusan Dash, K. B. V. Brahma Rao, Kritika Bansal

Get full text

Advanced intrusion detection in internet of things using graph attention networks by Aamir S. Ahanger, Sajad M. Khan, Faheem Masoodi, Ayodeji Olalekan Salau

Get full text

A comprehensive review on the users’ identity privacy for 5G networks by Mamoon M. Saeed, Mohammad Kamrul Hasan, Ahmed J. Obaid, Rashid A. Saeed, Rania A. Mokhtar, Elmustafa Sayed Ali, Md Akhtaruzzaman, Sanaz Amanlou, A. K. M. Zakir Hossain

Get full text

An Improved Software Source Code Vulnerability Detection Method: Combination of Multi-Feature Screening and Integrated Sampling Model by Xin He, Asiya, Daoqi Han, Shuncheng Zhou, Xueliang Fu, Honghui Li

Get full text

Psychomedical named entity recognition method based on multi-level feature extraction and multi-granularity embedding fusion by Zixuan Liu, Guofang Zhang, Yanguang Shen

Get full text

CLA-BERT: A Hybrid Model for Accurate Encrypted Traffic Classification by Combining Packet and Byte-Level Features by Hong Huang, Yinghang Zhou, Feng Jiang

Get full text

Dynamic Hypergraph Convolutional Networks for Hand Motion Gesture Sequence Recognition by Dong-Xing Jing, Kui Huang, Shi-Jian Liu, Zheng Zou, Chih-Yu Hsu

Get full text

Anomaly detection in encrypted network traffic using self-supervised learning by Sadaf Sattar, Shumaila Khan, Muhammad Ismail Khan, Ainur Akhmediyarova, Orken Mamyrbayev, Dinara Kassymova, Dina Oralbekova, Janna Alimkulova

“…Abstract Privacy and security in network communication have been enhanced via encryption and traditional anomaly detection methods are no longer effective because of their payload inspection. …”
Get full text

RMFKAN: Network Spammers Detection Method Based on Improved Graph Mamba by WANG Yuzhe, YAN Jinghua, BU Fanliang, WANG Yifan, LI Jia, HAN Zhuxuan

Get full text

An Open Conformance Test System towards the Standardization of Wireless Sensor Networks by Zhonghua Zhao, Wei Huangfu, Linmin Sun, Zhiqiang Shi, Wei Gan

Get full text

Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions by Mustafa Ghaleb, Mosab Hamdan, Abdulaziz Y. Barnawi, Muhammad Gambo, Abubakar Danasabe, Saheed Bello, Aliyu Habib

“…With the rapid growth of internet usage and the increasing number of connected devices, there is a critical need for advanced Network Traffic Classification (NTC) solutions to ensure optimal performance and robust security. …”
Get full text

CGTS: graph transformer-based anomaly detection in controller area networks by Xue Zhou, Guihe Qin, Yanhua Liang, Jiaru Song, Wanning Liu, Qingxin Liu

Get full text

Research on power data security full-link monitoring technology based on alternative evolutionary graph neural architecture search and multimodal data fusion by Zhenwan Zou, Bin Wang, Tao Chen, Jia Chen

“…The current evolutionary graph neural architecture search (GNAS) method mainly focuses on the topological connection and feature fusion between network layers, but it often requires a lot of computing resources, and the real-time performance of the GNN (graph neural network) model is difficult to meet the requirements when facing dynamic attacks and changing data. …”
Get full text

A deep decentralized privacy-preservation framework for online social networks by Samuel Akwasi Frimpong, Mu Han, Emmanuel Kwame Effah, Joseph Kwame Adjei, Isaac Hanson, Percy Brown

Get full text