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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of Computer Science 2025年 1-31页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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Detecting data manipulation errors in android applications using scene-guided exploration

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Empirical Software Engineering 2025年第4期30卷 1-38页

作者： Liu, Shuqi Zhou, Yu Yang, Wenhua Chen, Taolue Gall, Harald College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Computing and Mathematical Sciences Birkbeck University of London London United Kingdom Department of Informatics University of Zurich Zurich Switzerland

Data manipulation functionalities (DMFs) refer to operations such as create, read, update and delete which are crucial for maintaining the integrity of Android application (app) data. Existing testing techniques often fail to explicitly verify DMFs in combination, having limited capability to uncover non-crashing data manipulation errors (DMEs). In this paper, we propose a novel approach SceneData, which utilizes a scene-guided exploration strategy to effectively detect DMEs in Android apps. Particularly, we model the UI pages as scenes and present a scene transition graph (SceneTG) to capture the relation between scenes. By utilizing SceneTG, we explore app states to thoroughly validate DMFs across different scenes. We evaluate SceneData on 17 popular real-world apps, and SceneData discovers 25 previously unknown bugs in their latest releases, 21 of which are non-crashing DMEs. The experiments also show that a significant fraction (15/21) of these bugs cannot be detected by the state-of-the-art techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Android apps Model-based testing Non-crashing functional bugs Scene-based exploration

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Multi-input CNN: a deep learning-based approach for predicting breast cancer prognosis using multi-modal data

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Discover Data 2025年第1期3卷 1-11页

作者： Uma Kandan, Shamita Alketbi, Mariam Mohamed Al Aghbari, Zaher College of Engineering University of Sharjah Sharjah UAE College of Computing and Informatics University of Sharjah Sharjah UAE

Breast cancer prognosis prediction is pivotal to improving the survival chances of breast cancer patients. Recently, deep learning models integrated with multi-modal data have been explored extensively to improve the reliability and accuracy of breast cancer prognosis prediction. However, multi-modal data poses challenges to feature extraction due to the varied distribution and dimensionality across the modalities. To address this, we developed a multi-input convolutional neural network model, which extracts features from each modality in the multi-modal dataset simultaneously using separate convolutional layers, then concatenates and passes them to shared dense layers. The proposed model achieved area under the receiver operating characteristic curve values of 0.893 and 0.865 in 5-fold cross-validation and unseen test data respectively (at threshold = 0.2). These outcomes surpassed those of single-input convolutional neural network models and a state-of-the-art method based on multi-modal data. The multi-input convolutional neural network model efficiently handles multi-modal data and is a promising tool for breast cancer prognosis prediction.

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Underwater Sonar Image Targets Detection Based on Improved RT-DETR

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IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Li, Ang Hamzah, Raseeda Gao, Yousheng Jiujiang Vocational University College of Information Engineering Jiangxi Jiujiang332000 China College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia Melaka College of Computing Informatics and Mathematics Jasin Melaka77300 Malaysia

Underwater sonar imagery is characterized by small target sizes and low resolution, which can result in detection failures or false positives. To counteract these challenges, we introduce the underwater sonar detection transformer (US-DETR), an underwater sonar object detection model derived from the real-time detection transformer (RT-DETR) framework, incorporating attention-based feature fusion. US-DETR includes a novel enhanced feature interaction (EFI) module, which enhances the feature extraction network’s ability to perceive global information of the detected target. In addition, we propose a novel nonlocal attention feature fusion (NAFF) module to heighten the network’s sensitivity to the spatial relationships between feature channels across different scales, thereby enhancing its channel position and global information awareness. Experiments are conducted on a benchmark underwater sonar image dataset. Experimental results show that compared with RT-DETR, US-DETR achieves a 2.2% higher mean average precision (mAP) and a 2.1% higher F1 score compared with RT-DETR. The model also strikes an effective balance between detection speed and accuracy, achieving real-time performance of 126 FPS, which can meet the real-time requirements in industrial production. © 2004-2012 IEEE.

关键词： Underwater acoustics

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A GPU-Accelerated High-Performance Design for CRYSTALS-Dilithium Digital Signature

A GPU-Accelerated High-Performance Design for CRYSTALS-Dilit...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Hien Nguyen Bertrand Cambou Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University USA

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Our approach leverages the massive parallelism and computational power of a modern Graphics Processing Unit (GPU) to accelerate CRYSTALS-Dilithium's key operations. While post-quantum cryptographic algorithms like Dilithium offer robust security, they face computational challenges in high-throughput scenarios. Dilithium's performance bottlenecks, particularly in polynomial arithmetic and random sampling, have limited its practical deployment. To address these challenges, we leverage GPU acceleration as a promising solution to enhance efficiency. Our approach develops novel GPU-centric algorithms and data structures tailored to Dilithium's requirements. We focus on optimizing Number Theoretic Transforms (NTT), poly-nomial arithmetic, and random sampling, with an emphasis on batch processing and efficient memory management to maximize throughput. Experimental results demonstrate significant performance improvements over existing implementations. Our GPU-accelerated version consistently outperforms reference implemen-tations, achieving speed-ups ranging from 53.7% to 59.9% for signature generation and 20.0% to 63.8% for verification across various security levels. These results highlight the potential of our approach in enabling efficient post-quantum cryptography for high-performance applications.

关键词： Performance evaluation Government Memory management Graphics processing units Transforms Parallel processing Throughput Polynomials Digital signatures Arithmetic

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Latency Insensitivity Testing for Dataflow HLS Designs 25

Latency Insensitivity Testing for Dataflow HLS Designs

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33rd ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA 2025

作者： Cheng, Jianyi Wang, Lianghui Jiang, Zijian Bao, Yungang Shi, Kan School of Informatics The University of Edinburgh Edinburgh United Kingdom Institute of Computing Technology Chinese Academy of Sciences University of Science and Technology of China Beijing China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400713965

Dataflow high-level synthesis (HLS) tools automatically map a high-level software program to a dataflow hardware design. When testing the design, the HLS tool takes a testing function written in the same software language and translates it into the corresponding hardware testbench for cycle-accurate simulation. However, the generated testbench only follows a fixed schedule to feed inputs and collect outputs, without considering external dynamic behaviors of the test data (also known as latency insensitivity testing). Overlooking these test cases could miss bugs introduced by the designer that occur in certain events, such as late input arrival and output backpressure, leading to higher debugging costs. In this paper, we present an HLS testing toolflow named PEDIA that automatically generates testbenches to detect latency insensitivity bugs. We expand the test space from varying input values to varying input delays. PEDIA automatically determines an efficient test space of input delays based on the HLS schedule for better scalability;and tests the HLS design on a parallel hardware testing platform for high performance. Over two representative sets of existing benchmarks, we show that PEDIA can help identify potential bugs missed by existing approaches and achieves an average speedup of 4.53× on running 1500 test cases. © 2025 Copyright held by the owner/author(s).

关键词： Software testing

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Cultural Heritage in The Digital Age: Evaluating User Experience in Virtual Reality Cultural Heritage Learning Application 27

Cultural Heritage in The Digital Age: Evaluating User Experi...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Afendi bin Abdul Ghani, Mohamad Shahfik Kiffli, Sudirman bin Kuan, Tenh Hock Shamsuddin, Syadiah Nor binti Wan Rahim, Normala binti Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan Kelantan Bachok16300 Malaysia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Kuala Nerus21300 Malaysia

ISBN: (纸本)9791188428137

The Fourth Industrial Revolution (IR4.0) has transformed various sectors, including cultural heritage preservation, education, and tourism, through advanced technologies like virtual reality (VR). This study explores the intersection of technology and cultural heritage learning, highlighting the crucial role of user experience (UX) in VR applications. While VR holds significant potential for cultural heritage learning, existing research often emphasizes utility and usability over UX. To address this gap, the study evaluates the UX of VR applications designed for cultural heritage, specifically focusing on the Istana Jahar historical site. The objectives include assessing dimensions such as effectiveness, efficiency, attractiveness, satisfaction, emotion, engagement, attention, and perception. The research employs quantitative methods using the Design Science Research Methodology (DSRM) to guide the development and evaluation of the VR application. A cluster random sampling technique was used to recruit 113 students from Universiti Malaysia Kelantan (UMK), ensuring a representative sample with relevant educational backgrounds. Participants interacted with the VR application and completed a survey based on the developed conceptual framework. Findings indicate high mean scores across UX dimensions: Effectiveness (4.487), Efficiency (4.363), Attractiveness (4.528), Satisfaction (4.363), Emotion (4.401), Engagement (4.385), Attention (4.366), and Perception (4.416). These results demonstrate that the elements within the conceptual framework effectively enhance the UX in cultural heritage learning applications. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Virtual environments

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Exploring Quantum Processors in Decentralized Edge computing for Improved Data Processing

Exploring Quantum Processors in Decentralized Edge Computing...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Harshad Pitkar Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems.

关键词： Quantum computing Accuracy Program processors Computational modeling Autoencoders Machine learning Real-time systems Computational efficiency Internet of Things Edge computing

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Efficient Medical File Protection via Adaptive CRP-based Ephemeral Key Encapsulation and Robust Error Correction

Efficient Medical File Protection via Adaptive CRP-based Eph...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Adil Alshammari Dina Ghanai Miandoab Sareh Assiri Bertrand Cambou Brit Riggs School of Informatics and Computing Northern Arizona University Flagstaff Arizona USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Ahstract- The healthcare sector faces significant challenges in balancing adaptability, security, and resource efficiency in medi-cal file management. This paper introduces a lightweight protocol that optimizes cryptographic components for using ephemeral key encapsulation, combining adaptive Challenge Response Pair with robust error correction. Designed for limited resource environments, the proposed approach minimizes computational overhead, ensures reliable key recovery, and provides privacy protection. The execution results validate its performance, making it a practical solution for a decentralized healthcare system.

关键词： Encapsulation Technological innovation Privacy Protocols Medical services Reliability engineering Error correction Cryptography Protection Low latency communication

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Optimizing VPN Security in Financial Services: Implementing 256-Bit Encryption and Tunneling Protocols for Secure Data Transmission 19

Optimizing VPN Security in Financial Services: Implementing ...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ilias, Irma Syarlina Che Sameon, Sera Syarmila Zulkifli, Muhammad Zaqwan Ezzad Salleh, Mohammad Zahir Mat Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia College of Computing and Informatics Universiti Tenaga Nasional Selangor Malaysia CyberSecurity RawSec Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

This study examines the essential importance of security in contemporary network settings, emphasizing the increasing dependence on Virtual Private Networks (VPNs) for safe data transfer. The emphasis is on assessing the security attributes of VPNs to enhance existing network systems. This study specifically contrasts the performance and efficacy of two tunneling protocols—Layer 2 Tunneling Protocol version 3 with IPsec (L2TPv3/IPsec) and Generic Routing Encapsulation (GRE) utilizing IPsec—in a site-to-site connection. The study offers an economical solution by leveraging current infrastructure, minimizing the requirement for extra gear like antennas and rented space. Moreover, the study follows the National Institute of Standards and Technology (NIST) security protocols, specifically designed for the financial services sector, guaranteeing a secure and cost-effective approach for data transfer. Initial findings indicate that the best arrangement of VPN tunnels improves security in financial services networks, providing an effective method for protecting direct connections. © 2025 IEEE.

关键词： Cost effectiveness

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