Journal of Information Security and Applications最新文献_第5页

IDS-DWKAFL: An intrusion detection scheme based on Dynamic Weighted K-asynchronous Federated Learning for smart grid

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-13 DOI: 10.1016/j.jisa.2025.103993

Mi Wen , Yanbo Zhang , Pengsong Zhang , Liduo Chen

{"title":"IDS-DWKAFL: An intrusion detection scheme based on Dynamic Weighted K-asynchronous Federated Learning for smart grid","authors":"Mi Wen , Yanbo Zhang , Pengsong Zhang , Liduo Chen","doi":"10.1016/j.jisa.2025.103993","DOIUrl":"10.1016/j.jisa.2025.103993","url":null,"abstract":"<div><div>With the widespread application of 5G and smart terminals in power systems, malicious traffic and customer privacy issues have become critical security problems that urgently need to be addressed. Currently, intrusion detection systems (IDS) using distributed approaches such as Federated Learning (FL) are primarily employed. However, this method often assumes stable network connections and fails to account for the significant heterogeneity caused by the large number of diverse devices in real-world scenarios, which significantly increases the training time of the mode. To overcome these challenges, this paper proposes a Dynamic Weighted K-Asynchronous Federated Learning (DWKAFL) IDS scheme that determines aggregation eligibility and order based on gradient quality and staleness, thereby improving the efficiency and performance of IDS training in heterogeneous power system scenarios. Specifically, we introduce a node selection algorithm that considers both the quality and staleness of gradients uploaded by clients, as well as their communication capabilities, to dynamically select appropriate nodes for global aggregation. Additionally, we propose an Adaptive Gradient Storage Bucket (AGSB) approach, which stores gradients based on their arrival times and optimizes the timing of aggregation tasks, minimizing the impact of user dropouts on system performance. For the experiments, three publicly available intrusion detection datasets were converted into grayscale maps. The experimental results show that the DWKAFL-IDS scheme demonstrates stronger convergence and higher accuracy during training, achieving approximately 92% accuracy on the CICIDS2017, 91.3% accuracy on the UNSW-NB15 dataset and 85% on the NSL-KDD dataset. Notably, in scenarios with highly heterogeneous devices, the scheme exhibits more significant advantages compared to existing methods.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103993"},"PeriodicalIF":3.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel image encryption algorithm utilizing hybrid chaotic maps and Elliptic Curve Cryptography with genetic algorithm

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-13 DOI: 10.1016/j.jisa.2025.103995

Kartikey Pandey, Deepmala Sharma

{"title":"Novel image encryption algorithm utilizing hybrid chaotic maps and Elliptic Curve Cryptography with genetic algorithm","authors":"Kartikey Pandey, Deepmala Sharma","doi":"10.1016/j.jisa.2025.103995","DOIUrl":"10.1016/j.jisa.2025.103995","url":null,"abstract":"<div><div>In the era of digital communications, securing image data became a hot issue. In this respect, the present paper offers a powerful encryption technique for images while integrating three phases: confusion-diffusion, encryption, and optimization. In the confusion phase, the Lorenz chaotic map applied to improve the randomness of the image data. Diffusion is further made by using a novel hybrid chaotic map known as Logistic-Piecewise Linear Chaotic Map (LPWLCM). This further enhances the image content with the new hybrid of the Logistic and Piecewise Linear Chaotic Maps. The encryption phase uses Elliptic Curve Cryptography (ECC), which offers high security with minimal key sizes such that the encrypted image is resistant to unauthorized access. Finally, the optimization step applies Genetic Algorithm in order to optimize the cipher image to get maximum strength, both in terms of cryptographic quality and performance. Extensive experiments have been performed to show the substantial gains obtained with this proposed technique in security metrics and computational efficiency compared with the existing techniques. Thus, the proposed approach has hope to be taken into account as a good solution for secure image transmission in many applications. The experimental results validate the proposal, hence showing the potential of applying it in real-world implementation of secure digital communication systems.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103995"},"PeriodicalIF":3.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging High-Frequency Diversified Augmentation for general deepfake detection

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-12 DOI: 10.1016/j.jisa.2025.103994

Zhimao Lai , Yun Zhang , Dong Li , Jiangqun Ni

{"title":"Leveraging High-Frequency Diversified Augmentation for general deepfake detection","authors":"Zhimao Lai , Yun Zhang , Dong Li , Jiangqun Ni","doi":"10.1016/j.jisa.2025.103994","DOIUrl":"10.1016/j.jisa.2025.103994","url":null,"abstract":"<div><div>With the rapid advancement of deepfake technology, the visual quality of synthesized faces has significantly improved, raising serious security concerns about the misuse of facial manipulation techniques. As a result, deepfake detection has become a central focus within the multimedia forensics community. Recent studies have highlighted discrepancies between forged and genuine images in the high-frequency components. However, these studies have not fully addressed the inconsistency in high-frequency distributions across different datasets, which can lead to overfitting since models are trained on a limited range of high-frequency features. To overcome this challenge, we propose a High-Frequency Diversified Augmentation (HFDA) method designed to broaden the variation range of high-frequency features in training images. Specifically, our approach perturbs the amplitude spectra of the training data to generate augmented images with enhanced diversity in the high-frequency bands. Additionally, we introduce a forgery artifact consistency learning strategy to guide discriminative feature learning, aligning augmented images with their corresponding raw images. Extensive experiments demonstrate that the proposed HFDA method achieves superior or comparable performance to state-of-the-art methods across several widely used datasets. The code is available at <span><span>https://github.com/laizhm/HFDA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103994"},"PeriodicalIF":3.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strengthening ICS defense: Modbus-NFA behavior model for enhanced anomaly detection

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-11 DOI: 10.1016/j.jisa.2025.103990

Eslam Amer , Bander Ali Saleh Al-rimy , Shaker El-Sappagh

{"title":"Strengthening ICS defense: Modbus-NFA behavior model for enhanced anomaly detection","authors":"Eslam Amer , Bander Ali Saleh Al-rimy , Shaker El-Sappagh","doi":"10.1016/j.jisa.2025.103990","DOIUrl":"10.1016/j.jisa.2025.103990","url":null,"abstract":"<div><div>The rise of the Internet of Things (IoT) has significantly transformed Industrial Control Systems (ICS) by increasing their dependence on interconnected devices for automating processes. This growing integration of IoT technologies within ICS has heightened concerns about security and privacy, underscoring the importance of protecting sensitive data. This paper addresses the challenge of detecting anomalies within ICS environments that utilize the Modbus protocol. Modbus requests are encapsulated in Modbus frames, which direct devices on the specific actions to undertake. Thus, the sequence of Modbus frames in network traffic serves as a comprehensive indicator of device behavior on the network. To tackle this challenge, we introduce a novel approach for anomaly detection by modeling device interactions on the network through the analysis of Modbus frame sequences using a Non-deterministic Finite Automaton (NFA) framework, termed the Modbus-NFA Behavior Distinguisher (MNBD) model. The NFA framework is particularly effective for this purpose as it can represent multiple potential states and transitions within a network, thereby capturing the complexity and variability of network behaviors. This capability allows the MNBD model to detect deviations from normal behavior, identifying potential anomalies with high accuracy. Our MNBD model was evaluated against several existing ICS network traffic datasets. The results demonstrate that the Modbus-NFA approach not only surpasses traditional machine learning models but also outperforms sequence-based deep learning models. Additionally, cross-dataset testing reveals that the MNBD model exhibits superior generalization capabilities compared to deep learning-based approaches. These findings highlight the MNBD model’s potential as a robust tool for anomaly detection, advancing research and development efforts in ICS security.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103990"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MSAUPL: A multi-server authentication and key agreement protocol for industrial IoT based on user privacy level

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-11 DOI: 10.1016/j.jisa.2025.103991

Yi Wu , Tao Feng , Chunhua Su , Chunyan Liu

{"title":"MSAUPL: A multi-server authentication and key agreement protocol for industrial IoT based on user privacy level","authors":"Yi Wu , Tao Feng , Chunhua Su , Chunyan Liu","doi":"10.1016/j.jisa.2025.103991","DOIUrl":"10.1016/j.jisa.2025.103991","url":null,"abstract":"<div><div>With the rapid development of the Industrial Internet of Things (IIoT), industrial control systems are characterized by increasing complexity of access users and diversity of data sources, making it crucial to implement hierarchical data transmission protocols for industrial servers based on user privacy level. However, traditional industrial systems often lack the flexibility to provide hierarchical services to access users according to their privacy level, leading to frequent incidents of data or privacy disclosure. This study addresses the need for hierarchical data services for various access users in an IIoT environment by proposing a multi-server authentication and key agreement protocol based on user privacy level (MSAUPL). To enhance the security and integrity of message transmission, a multi-factor authentication mechanism is adopted. Considering the computational and storage limitations of IIoT devices, the MSAUPL protocol primarily relies on hash functions for authentication and key agreement. Moreover, to allow access users to derive keys with lower privilege level after completing a single authentication for their privacy level, a key derivation scheme based on a directed graph is introduced. Additionally, to alleviate the storage burden on servers, a multi-level user privilege scheme based on a Merkle tree structure is proposed, enabling servers to efficiently compute different user access level. Finally, security analysis and comprehensive performance evaluation demonstrate that the MSAUPL protocol not only enhances functionality but also significantly reduces resource consumption, making it well-suited for multi-server IIoT environments.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103991"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient adaptive defense scheme for differential privacy in federated learning

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-10 DOI: 10.1016/j.jisa.2025.103992

Fangfang Shan , Yanlong Lu , Shuaifeng Li , Shiqi Mao , Yuang Li , Xin Wang

{"title":"Efficient adaptive defense scheme for differential privacy in federated learning","authors":"Fangfang Shan , Yanlong Lu , Shuaifeng Li , Shiqi Mao , Yuang Li , Xin Wang","doi":"10.1016/j.jisa.2025.103992","DOIUrl":"10.1016/j.jisa.2025.103992","url":null,"abstract":"<div><div>Federated learning, as an emerging technology in the field of artificial intelligence, effectively addresses the issue of data islands while ensuring privacy protection. However, studies have shown that by analyzing gradient updates, leaked gradient information can still be used to reconstruct original data, thus inferring private information. In recent years, differential privacy techniques have been widely applied to federated learning to enhance data privacy protection. However, the noise introduced often significantly reduces the learning performance. Previous studies typically employed a fixed gradient clipping strategy with added fixed noise. Although this method offers privacy protection, it remains vulnerable to gradient leakage attacks, and training performance is often subpar. Although subsequent proposals of dynamic differential privacy parameters aim to address the issue of model utility, frequent parameter adjustments lead to reduced efficiency. To solve these issues, this paper proposes an efficient federated learning differential privacy protection framework with noise attenuation and automatic pruning (EADS-DPFL). This framework not only effectively defends against gradient leakage attacks but also significantly improves the training performance of federated learning models.</div><div>Extensive experimental results demonstrate that our framework outperforms existing differential privacy federated learning schemes in terms of model accuracy, convergence speed, and resistance to attacks.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103992"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DMRP: Privacy-Preserving Deep Learning Model with Dynamic Masking and Random Permutation

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-10 DOI: 10.1016/j.jisa.2025.103987

Chongzhen Zhang , Zhiwang Hu , Xiangrui Xu , Yong Liu , Bin Wang , Jian Shen , Tao Li , Yu Huang , Baigen Cai , Wei Wang

{"title":"DMRP: Privacy-Preserving Deep Learning Model with Dynamic Masking and Random Permutation","authors":"Chongzhen Zhang , Zhiwang Hu , Xiangrui Xu , Yong Liu , Bin Wang , Jian Shen , Tao Li , Yu Huang , Baigen Cai , Wei Wang","doi":"10.1016/j.jisa.2025.103987","DOIUrl":"10.1016/j.jisa.2025.103987","url":null,"abstract":"<div><div>Large AI models exhibit significant efficiency and precision in addressing complex problems. Despite their considerable advantages in various domains, these models encounter numerous challenges, notably high training costs. Currently, the training of distributed large AI models offers a solution to mitigate these elevated costs. However, distributed large AI models remain susceptible to data reconstruction attacks. A malicious server could leverage the intermediate results uploaded by clients to reconstruct the original data within the framework of distributed large AI models. This study first examines the underlying principles of data reconstruction attacks and proposes a privacy protection scheme. Our approach begins by obfuscating the mapping relationship between embeddings and the original data to ensure privacy protection. Specifically, during the upload of embedding data by clients to the server, genuine embeddings are concealed to prevent unauthorized access by malicious servers. Building on this concept, we introduce <em>DMRP</em>, a defensive mechanism featuring Dynamic Masking and Random Permutation, designed to mitigate data reconstruction attacks while maintaining the accuracy of the primary task. Our experiments, conducted across three models and four datasets, demonstrate the effectiveness of DMRP in countering data reconstruction attacks within distributed large-scale AI models.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103987"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Public data-enhanced multi-stage differentially private graph neural networks

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-09 DOI: 10.1016/j.jisa.2025.103985

Bingbing Zhang , Heyuan Huang , Lingbo Wei , Chi Zhang

{"title":"Public data-enhanced multi-stage differentially private graph neural networks","authors":"Bingbing Zhang , Heyuan Huang , Lingbo Wei , Chi Zhang","doi":"10.1016/j.jisa.2025.103985","DOIUrl":"10.1016/j.jisa.2025.103985","url":null,"abstract":"<div><div>Existing differential privacy algorithms for graph neural networks (GNNs) typically rely on adding noise to private graph data to prevent the leakage of sensitive information. While the addition of noise often leads to significant performance degradation, the incorporation of additional public graph data can effectively mitigate these effects, thereby improving the privacy-utility trade-off in differentially private GNNs. To enhance the trade-off, we propose a method that utilizes public graph data in multi-stage training algorithms. First, to increase the ability to extract useful information from graph data, we introduce a public graph and apply an unsupervised pretraining algorithm, which is then integrated into the private model training through parameter transfer. Second, we utilize multi-stage GNNs to transform the neighborhood aggregation into a preprocessing step to prevent privacy budget accumulation from occurring in the embedding layer, hence enhancing model performance under the same privacy constraints. This method is applicable to both node differential privacy and edge differential privacy in GNNs. Third, for edge differential privacy, we introduce an aggregation perturbation mechanism, which trains an edge prediction model on a basis of node features using the public graph data. We apply this trained model to the private graph data to predict potential neighbors for each node. We then calculate an additional aggregation result based on these predicted neighbors and combine with the aggregation result derived from the true edges, ensuring that the aggregation perturbation result retains valuable information even under very low privacy budgets. Our results show that incorporating public graph data can enhance the accuracy of differentially private GNNs by approximately 5% under the same privacy settings.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103985"},"PeriodicalIF":3.8,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perceptual visual security index: Analyzing image content leakage for vision language models

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-08 DOI: 10.1016/j.jisa.2025.103988

Lishuang Hu , Tao Xiang , Shangwei Guo , Xiaoguo Li , Ying Yang

{"title":"Perceptual visual security index: Analyzing image content leakage for vision language models","authors":"Lishuang Hu , Tao Xiang , Shangwei Guo , Xiaoguo Li , Ying Yang","doi":"10.1016/j.jisa.2025.103988","DOIUrl":"10.1016/j.jisa.2025.103988","url":null,"abstract":"<div><div>During the training phase of vision language models (VLMs), the privacy storage and sharing of images are of paramount importance. While the Visual Security Index (VSI) is commonly used for content leakage analysis, it usually focuses on comparing content similarity between plain and protected or encrypted images, neglecting the threat model of visual security. In this paper, considering the functionality of the human visual capability, we comprehensively analyze the system model of VSIs and propose a novel perceptual visual security index (PVSI) to evaluate the content leakage of perceptually encrypted images for VLMs. In particular, we take visual perception (<strong>VP</strong>) as the adversary’s capability and present the definition of VSI under an honest-but-curious threat model. To evaluate the content leakage of encrypted images under the <strong>VP</strong> assumption, we first present a robust feature descriptor and obtain the semantic content sets of both plain and encrypted images. Then, we propose a systematic method to reduce the impact of different encryption algorithms. We further evaluate the similarity between semantic content sets to obtain the proposed PVSI. We also analyze the consistency between the proposed visual security definition and PVSI. Extensive experiments are performed on five publicly available image databases. Our experimental results demonstrate that compared with many existing state-of-the-art visual security metrics, the proposed PVSI exhibits better performance not only on images generated from specific image encryption algorithms but also on publicly available image databases.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103988"},"PeriodicalIF":3.8,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A heuristic assisted cyber attack detection system using multi-scale and attention-based adaptive hybrid network

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-02-07 DOI: 10.1016/j.jisa.2025.103970

R. Lakshman Naik , Dr. Sourabh Jain , Dr. Manjula Bairam

{"title":"A heuristic assisted cyber attack detection system using multi-scale and attention-based adaptive hybrid network","authors":"R. Lakshman Naik , Dr. Sourabh Jain , Dr. Manjula Bairam","doi":"10.1016/j.jisa.2025.103970","DOIUrl":"10.1016/j.jisa.2025.103970","url":null,"abstract":"<div><div>Business domains have employed distributed platforms, and these domains use networks and communication services to send vital information that must be secured. To secure confidentiality, the information security system is introduced, which is described as the generation of the data, the network, and the hardware systems. Practically, all of our daily activities depend upon information and communication technology, which is vulnerable to threats. To rectify these issues, a deep learning-related cyber security system is developed to protect the data from various cyber-attacks. Initially, the cyber attacks are detected using Multi-scale and Attention-based Adaptive Hybrid Network (MA-AHNet), where the networks such as Dilated Long Short Term Memory (LSTM) and Deep Temporal Convolutional Network (DTCN) are integrated to construct MA-AHNet. The parameters from MA-AHNet are tuned with the support of the Fitness-based Ebola Optimization Algorithm (FEOA) to improve the detection performance. Then, the authorized user detection is carried out via the same MA-AHNet. Finally, the risk prediction is done via the same MA-AHNet to identify the level of risk in the network. These cyber-attacks, user authorization, and risk detection processes provide higher security. The experimental findings are validated with the traditional cyber security systems concerning various performance measures.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"89 ","pages":"Article 103970"},"PeriodicalIF":3.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143232569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0