UNIFIED MULTIMODAL BIOMETRICS FUSION USING DEEP LEARNING FOR SECURING IOT by Prabhjot Kaur, Chander Kaur

“…Our proposed approach employs “Convolutional Neural Network (CNN)” architectures, notable for their efficacy in computer vision tasks, to extract potent discriminative features from the input images. …”
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Comparative analysis of impact of classification algorithms on security and performance bug reports by Said Maryyam, Bin Faiz Rizwan, Aljaidi Mohammad, Alshammari Muteb

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Security monitoring via sound analysis and voice identification with artificial intelligence by Balabanova Ivelina, Sidorova Kristina, Georgiev Georgi

“…Subsequently, steps were taken to reduce the informative features when searching for similar levels of accuracy in order to limit the necessary computational procedures in neural training, but maintain the threshold of successful user authentication. …”
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HCAP: Hybrid cyber attack prediction model for securing healthcare applications. by Mohanad Faeq Ali, Mohammed Shakir Mohmood, Ban Salman Shukur, Rex Bacarra, Jamil Abedalrahim Jamil Alsayaydeh, Masrullizam Mat Ibrahim, Safarudin Gazali Herawan

“…The extracted features are fed into the lion-optimization technique to fine-tune the hyperparameters of the recurrent neural networks, enhancing the model's ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. …”
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A hybrid model for fake news detection: Leveraging news content and user comments in fake news by Marwan Albahar

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A Hybrid Deep Learning Approach for Secure Biometric Authentication Using Fingerprint Data by Abdulrahman Hussian, Foud Murshed, Mohammed Nasser Alandoli, Ghalib Aljafari

“…Addressing these limitations is crucial for ensuring reliable biometric security in real-world applications, including law enforcement, financial transactions, and border security. …”
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Group feature calibration for sound event detection by Yanzhen Ren, Wuyang Liu, Chenyu Liu, Tingting Zhu

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CRIMINOLOGICAL FEATURES ORGANIZED COUNTERFEITING IN THE DIGITAL AGE by Jamaludin Kh. Umakhanov, Maya G. Dieva

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Deep Fusion Intelligence: Enhancing 5G Security Against Over-the-Air Attacks by Mohammadreza Amini, Ghazal Asemian, Burak Kantarci, Cliff Ellement, Melike Erol-Kantarci

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Regional Short‐Term Wind Power Prediction Based on CEEMDAN‐FTC Feature Mapping and EC‐TCN‐BiLSTM Deep Learning by Guoyuan Qin, Xiaosheng Peng, Zimin Yang

“…To improve the accuracy of regional short‐term WPP, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fine‐to‐coarse (FTC) feature mapping, and error compensation‐temporal convolutional network‐bidirectional Long short‐term memory network (EC‐TCN‐BiLSTM) is proposed in this paper. …”
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Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence by Adil Ali Saleem, Hafeez Ur Rehman Siddiqui, Muhammad Amjad Raza, Sandra Dudley, Julio César Martínez Espinosa, Luis Alonso Dzul López, Isabel de la Torre Díez

“…Gender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. …”
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SH-SDS: a new static-dynamic strategy for substation host security detection by Yang Diao, Hui Chen, Wei Liu, Abdur Rasool

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Robust Network Traffic Classification Based on Information Bottleneck Neural Network by Wei Lin, Yu Chen

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DDoS classification of network traffic in software defined networking SDN using a hybrid convolutional and gated recurrent neural network by Ahmed M. Elshewey, Safia Abbas, Ahmed M. Osman, Eman Abdullah Aldakheel, Yasser Fouad

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Heartbeat Biometrics for Remote Authentication Using Sensor Embedded Computing Devices by Md Saiful Islam

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A survey on secure communication technologies for smart grid cyber physical system by Giriraj Sharma

“…A CPS combines physical components, computational elements, and communication networks. The smart grid CPS (SG-CPS) is an example of such a system, featuring physical devices with diverse communication needs and intermediary networks. …”
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A lightweight framework to secure IoT devices with limited resources in cloud environments by Vivek Kumar Pandey, Dinesh Sahu, Shiv Prakash, Rajkumar Singh Rathore, Pratibha Dixit, Iryna Hunko

“…Finally, the model also makes use of a novel leaf-cut feature optimization strategy and tight adaptive cloud edge intelligence to achieve high accuracy while minimizing memory and computation demand. …”
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GFADE: generalized feature adaptation and discrimination enhancement for deepfake detection by ZhiYong Tian, Junkai Yi

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DynBlock: dynamic data encryption with Toffoli gate for IoT by Mubasher Haq, Ijaz Ali Shoukat, Alamgir Naushad, Mohsin Raza Jafri, Moid Sandhu, Abd Ullah Khan, Hyundong Shin

“…We evaluate DynBlock with both 3 and 5 rounds to assess its security and computational efficiency. While the 5-round configuration offers stronger resistance to cryptanalytic attacks, the 3-round setup already delivers robust security, featuring high entropy and a strong avalanche effect, making it well-suited for resource-constrained environments. …”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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