Journal of Information Security and Applications最新文献_第7页

An imperceptible dynamic anticipated backdoor attack in federated learning 联邦学习中不可察觉的动态预期后门攻击

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-11 DOI: 10.1016/j.jisa.2025.104317

Yingqiang Xie , Wei Ren , Tianqing Zhu , Lianchong Zhang

Federated Learning is a distributed machine learning paradigm that allows multiple clients to collaboratively train a global model while preserving privacy by avoiding the exchange of raw data. However, its distributed nature makes it vulnerable to backdoor attacks, which threaten the integrity and security of the model. Existing attacks often rely on fixed triggers or optimizations of the local model, failing to adapt to dynamic updates of the global model. We propose a new and effective attack named IDABA (Imperceptible Dynamic Anticipated Backdoor Attack), a novel dynamic backdoor attack method for FL, addressing these limitations by ensuring visual imperceptibility and persistence. IDABA generates visually imperceptible poisoned samples and employs Model-Contrastive Loss (MOON) to maintain similarity with the global model. It also predicts future global model states to optimize trigger effectiveness. Experiments on CIFAR10, MNIST, GTSRB, and TinyImageNet show that IDABA achieves higher Attack Success Rates (ASR) while maintaining model accuracy. It demonstrates strong adaptability against defense mechanisms such as Krum and Multi-Krum. GradCam analysis and image quality metrics confirm the visual stealthiness of IDABA’s backdoor samples.

联邦学习是一种分布式机器学习范例，它允许多个客户端协作训练全局模型，同时通过避免交换原始数据来保护隐私。然而，它的分布式特性使其容易受到后门攻击，从而威胁到模型的完整性和安全性。现有的攻击通常依赖于固定的触发器或局部模型的优化，无法适应全局模型的动态更新。我们提出了一种新的有效的攻击方法IDABA （imvisible Dynamic预期后门攻击），它通过保证视觉的不可感知性和持久性来解决这些限制。IDABA产生视觉上难以察觉的中毒样本，并使用模型对比损失（MOON）来保持与全局模型的相似性。它还预测未来的全局模型状态，以优化触发器的有效性。在CIFAR10、MNIST、GTSRB和TinyImageNet上的实验表明，IDABA在保持模型精度的同时实现了更高的攻击成功率（ASR）。它对克鲁姆和多克鲁姆等防御机制具有较强的适应性。GradCam分析和图像质量指标证实了IDABA后门样本的视觉隐身性。

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引用次数: 0

Enabling diverse styles coverless image steganography with two-stage latent transformation and diffusion model 利用两级潜伏变换和扩散模型实现多种风格的无盖图像隐写

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-11 DOI: 10.1016/j.jisa.2025.104328

Jiangtao Guo, Buwei Tian, Junyong Jiang, Lu Dong

Coverless image steganography (CIS) aims to map secret images into container images without modifying the original images for concealment purposes. However, most current CIS methods rely heavily on deep learning models, which require extensive training datasets and demonstrate limited robustness to variations in image styles. Particularly when applied to images with substantial stylistic variations, these methods often produce unsatisfactory steganographic results, leading to significant degradation in the quality of steganographic image (stego-image). Furthermore, existing diffusion model-based CIS approaches can only achieve effective concealment between images with similar styles, thereby limiting the diversity of application scenarios. To address these limitations, we propose a training-free CIS method based on the diffusion model (DStyleStego), which does not rely on the traditional training, and can effectively handle different styles of images, guaranteeing the image quality and the security of steganographic information. Specifically, we design a two-stage latent transformation method to improve the security and flexibility of image steganography. In addition, we introduce a detail compensation function to recover detail information lost during the diffusion process to improve the quality and fidelity of the generated images. Extensive experimental results demonstrate that DStyleStego achieves efficient and stable steganography across diverse image datasets (Stego260 and UniStega) while exhibiting significant advantages in terms of image quality preservation.

无覆盖图像隐写术（CIS）的目的是在不修改原始图像的情况下，将秘密图像映射到容器图像中，以达到隐藏的目的。然而，目前大多数CIS方法严重依赖于深度学习模型，这需要大量的训练数据集，并且对图像风格变化的鲁棒性有限。特别是当应用于具有大量风格变化的图像时，这些方法通常会产生令人不满意的隐写结果，导致隐写图像（隐写图像）质量的显着下降。此外，现有的基于扩散模型的CIS方法只能在样式相似的图像之间实现有效的隐藏，限制了应用场景的多样性。针对这些局限性，我们提出了一种基于扩散模型的无训练CIS方法（DStyleStego），该方法不依赖于传统的训练，可以有效地处理不同风格的图像，保证了图像质量和隐写信息的安全性。具体来说，为了提高图像隐写的安全性和灵活性，我们设计了一种两阶段隐写变换方法。此外，我们引入了细节补偿函数来恢复扩散过程中丢失的细节信息，以提高生成图像的质量和保真度。大量的实验结果表明，DStyleStego在不同的图像数据集（Stego260和UniStega）上实现了高效和稳定的隐写，同时在图像质量保持方面表现出显著的优势。

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引用次数: 0

A class-imbalance-aware intrusion detection system based on spatiotemporal graph neural networks for software-defined vehicles 基于时空图神经网络的软件定义车辆类不平衡感知入侵检测系统

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-11 DOI: 10.1016/j.jisa.2025.104340

Sishan Wang , Youqun Zhao , Xin Fu , Huachao Si , Wentao Wang , Lei Xue

Advanced driving systems (ADS) and vehicle-to-everything (V2X) technologies are accelerating the shift to software-defined vehicles (SDVs), which have dramatically increased the demand for connectivity and bandwidth of in-vehicle networks (IVNs), e.g., automotive Ethernet. The SOME/IP (Scalable service-Oriented MiddlewarE over IP) protocol, a middleware standard for automotive Ethernet, presents unique security challenges: the lack of security mechanisms and its dynamic session behaviors render traditional rule-based Intrusion Detection Systems (IDSs) ineffective. To address these challenges, we propose GATransformer, a novel hybrid Graph Attention Network (GAT) with Transformer architecture that can learn spatial-temporal dependencies for SOME/IP-based IVNs in the class imbalance scenario. A comprehensive evaluation on a SOME/IP dataset built from a real-world Connected and Autonomous Vehicle (CAV) indicates that the GATransformer enhances robustness in a class imbalance scenario and significantly outperforms conventional Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), standalone Transformers, and baseline GAT. The proposed model achieves an F1-score of 0.9999 with 0.004 ms inference latency on server-grade hardware (NVIDIA RTX 3090) and maintains robust performance (F1-score: 0.9983) with sub-millisecond latency (0.151 ms) when deployed on an automotive-grade embedded platform (NVIDIA Jetson Orin Nano). These results validate the possibility of deploying the hybrid Graph Neural Networks (GNNs) for real-time automotive intrusion detection, representing a significant advancement toward securing next-generation service-oriented architectures (SOA) against evolving cyber threats.

先进驾驶系统（ADS）和车联网（V2X）技术正在加速向软件定义车辆（sdv）的转变，这极大地增加了对车载网络（ivn）（如汽车以太网）的连接性和带宽的需求。SOME/IP（基于IP的可伸缩面向服务的中间件）协议是汽车以太网的中间件标准，它提出了独特的安全挑战：缺乏安全机制及其动态会话行为使得传统的基于规则的入侵检测系统（ids）无效。为了解决这些挑战，我们提出了一种具有Transformer架构的新型混合图注意网络（GAT） gattransformer，它可以在类不平衡场景中学习基于SOME/ ip的ivn的时空依赖关系。对现实世界联网和自动驾驶汽车（CAV）构建的SOME/IP数据集的综合评估表明，gattransformer增强了类不平衡场景中的鲁棒性，并显著优于传统的深度学习（DL）架构，包括卷积神经网络（CNN）、长短期记忆（LSTM）、独立变压器和基线GAT。该模型在服务器级硬件（NVIDIA RTX 3090）上实现了0.9999的f1分数和0.004 ms的推理延迟，并在部署在汽车级嵌入式平台（NVIDIA Jetson Orin Nano）上时保持了亚毫秒级延迟（0.151 ms）的稳健性能（f1分数：0.9983）。这些结果验证了在实时汽车入侵检测中部署混合图神经网络（gnn）的可能性，代表了在保护下一代面向服务的架构（SOA）免受不断发展的网络威胁方面取得的重大进展。

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引用次数: 0

Improved LSB substitution based semi-blind fragile watermarking for high-accuracy tamper localization 基于改进LSB替换的半盲脆弱水印高精度篡改定位

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-11 DOI: 10.1016/j.jisa.2025.104330

Pengfei Li , Weijia Cao , Tao Yu

Fragile watermarking is an effective technique for detecting image tampering, yet current block-based fragile watermarking methods often struggle with limited watermarked image quality and high false-positive rates. To overcome these limitations, this paper proposes an enhanced semi-blind fragile watermarking method based on an improved least significant bit (LSB) substitution (ILSBS), named TLFW. First, the original image is divided into non-overlapping blocks, within which a novel rule-based ILSBS approach dynamically adjusts the watermark embedding bits to minimize pixel distortion effectively. This significantly enhances the visual quality of the watermarked image. Next, a tamper localization optimization (TLO) strategy is introduced to substantially reduce false positives by refining detection results around reference points. Extensive experimental results demonstrate that the TLFW approach improves the peak signal-to-noise ratio (PSNR) of watermarked images from 44 dB to 46 dB, consistently reduces the false positive rate (FPR) across multiple attack scenarios while achieving 45 % FPR reduction and 52 % tamper detection rate (TDR) gain specifically for the text-addition attack, and lowers computational costs significantly compared to existing methods. The proposed TLFW scheme is compatible with both grayscale and color images and does not require the original image during watermark extraction, making it highly suitable for practical image authentication applications.

脆弱水印是检测图像篡改的一种有效技术，但目前基于分块的脆弱水印方法往往存在水印图像质量有限、误报率高的问题。为了克服这些局限性，本文提出了一种基于改进的最低有效位替换（least significant bit substitution， ILSBS）的增强半盲脆弱水印方法TLFW。首先，将原始图像划分为互不重叠的块，在块内采用基于规则的ILSBS方法动态调整水印嵌入位，有效减小像素失真；这大大提高了水印图像的视觉质量。接下来，引入了篡改定位优化（TLO）策略，通过在参考点周围改进检测结果，大大减少误报。大量的实验结果表明，TLFW方法将水印图像的峰值信噪比（PSNR）从44 dB提高到46 dB，在多种攻击场景中持续降低假阳性率（FPR），同时在文本添加攻击中实现45%的FPR降低和52%的篡改检测率（TDR）增益，并且与现有方法相比显著降低了计算成本。所提出的TLFW方案兼容灰度图像和彩色图像，并且在水印提取时不需要原始图像，非常适合实际图像认证应用。

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引用次数: 0

Software supply chain: A taxonomy of attacks, mitigations and risk assessment strategies 软件供应链：攻击、缓解和风险评估策略的分类

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-11 DOI: 10.1016/j.jisa.2025.104324

Betul Gokkaya, Leonardo Aniello, Basel Halak

The software product is a source of cyber-attacks that target organizations by using their software supply chain (SSC) as a distribution vector. As the reliance of software projects on open-source or proprietary modules is increasing drastically, SSC is becoming more and more critical and, therefore, has attracted the interest of cyber attackers. While existing studies primarily focus on software supply chain attacks’ prevention and detection methods, there is a need for a broad overview of attacks and comprehensive risk assessment for software supply chain security. This study conducts a systematic literature review to fill this gap. By analyzing 96 papers published between 2015-2023, we identified 19 distinct SSC attacks, including 6 novel attacks highlighted in recent studies. Additionally, we developed 25 specific security controls and established a precisely mapped taxonomy that transparently links each control to one or more specific attacks. By establishing this relationship, we demonstrate how SSC security controls are strategically designed to counteract specific attack vectors. Furthermore, we emphasize the role of risk assessment as a foundational step in understanding and prioritizing these vulnerabilities. This study introduces a risk assessment methodology tailored to software supply chain environments, focusing on identifying vulnerabilities in software components, dependencies, and suppliers. The proposed methodology enables organizations to systematically prioritize threats and implement appropriate mitigation strategies.

软件产品是网络攻击的一个来源，通过将组织的软件供应链（SSC）作为分发载体来攻击组织。随着软件项目对开源或专有模块的依赖急剧增加，SSC变得越来越重要，因此引起了网络攻击者的兴趣。现有的研究主要集中在软件供应链攻击的预防和检测方法上，需要对软件供应链安全的攻击进行广泛的概述和全面的风险评估。本研究通过系统的文献综述来填补这一空白。通过分析2015-2023年间发表的96篇论文，我们确定了19种不同的SSC攻击，包括最近研究中突出的6种新型攻击。此外，我们开发了25个特定的安全控件，并建立了一个精确映射的分类法，将每个控件透明地链接到一个或多个特定的攻击。通过建立这种关系，我们演示了如何战略性地设计SSC安全控制来对抗特定的攻击向量。此外，我们强调风险评估作为理解和优先考虑这些脆弱性的基础步骤的作用。本研究介绍了一种针对软件供应链环境的风险评估方法，着重于识别软件组件、依赖关系和供应商中的漏洞。拟议的方法使各组织能够系统地确定威胁的优先次序并实施适当的缓解战略。

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引用次数: 0

Anomaly detection for blockchain nodes based on eBPF and fine-tuning large language model 基于eBPF和大语言模型微调的区块链节点异常检测

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-10 DOI: 10.1016/j.jisa.2025.104329

Jincheng Su , Zhide Chen , Kexin Zhu , Chen Feng

While blockchain technology is widely used across various fields, it faces growing security challenges. Traditional blockchain anomaly detection methods, such as log analysis and fixed pattern recognition, struggle to handle complex and dynamic attack techniques. This paper proposes the Blockchain Live Anomaly Detection with eBPF and LLMs (BLAD-eLLM) scheme, which combines the efficient data capture capabilities of extended Berkeley Packet Filter (eBPF) technology for kernel-level security monitoring with the deep text understanding and semantic analysis power of large language models (LLMs) to enhance the network layer security of blockchain nodes. The proposed approach analyzes blockchain network activities comprehensively, aiming for accurate identification of potential anomalous behaviors. Furthermore, a knowledge-enhanced reasoning framework is developed, integrating Retrieval-Augmented Generation (RAG) for contextual blockchain threat intelligence and Chain-of-Thought (COT) prompting for multi-step attack analysis while employing Weight-Decomposed Low-Rank Adaptation (DoRA) based prompt fine-tuning to optimize the LLMs’ domain-specific adaptability and detection accuracy. Experimental results demonstrate that the proposed scheme significantly improves blockchain anomaly detection accuracy while maintaining minimal impact on system performance, ensuring the stability and security of the blockchain system.

区块链技术在各个领域得到广泛应用的同时，也面临着越来越多的安全挑战。传统的区块链异常检测方法，如日志分析和固定模式识别，难以处理复杂的动态攻击技术。本文提出了区块链实时异常检测与eBPF和llm （blade - ellm）方案，该方案将扩展伯克利包过滤（eBPF）技术用于内核级安全监控的高效数据捕获能力与大型语言模型（llm）的深度文本理解和语义分析能力相结合，以增强区块链节点的网络层安全性。该方法全面分析区块链网络活动，旨在准确识别潜在的异常行为。此外，开发了一个知识增强推理框架，集成了用于上下文区块链威胁情报的检索增强生成（RAG）和用于多步攻击分析的思维链（COT）提示，同时采用基于权重分解的低秩自适应（DoRA）提示微调来优化llm的特定领域适应性和检测精度。实验结果表明，该方案在保证区块链系统稳定性和安全性的同时，显著提高了区块链异常检测精度。

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引用次数: 0

Security analysis of a novel image encryption algorithm based on 3D chaos 一种基于三维混沌的图像加密算法的安全性分析

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-09 DOI: 10.1016/j.jisa.2025.104332

Rong Zhou, Simin Yu

The paper analyzes the security of a novel encryption algorithm based on a 3D chaos from three aspects. The algorithm uses a typical two-round permutation-diffusion structure and consists of three parts: key selection, permutation, and diffusion with feedback. The keys consist of three initial values, three control parameters, one interference parameter, and two correlation coefficients of 3D chaos. The algorithm has passed the tests for histogram, correlation, difference analysis, and trend fluctuation analysis, and its security has been verified. However, after analysis in this paper, it has three major vulnerabilities: first, the algorithm has an equivalent key because the original key is irrelevant to plaintext. Second, diffusion is a linear operation, it results in the separability between the pixel values of the plaintext and the keys that form the pixel values of the ciphertext. Hence, the plaintext can be reconstructed according to the characteristics of ciphertext. Third, the feedback in diffusion makes ciphertext have a strong sensitivity to plaintext, but the sensitivity of plaintext to ciphertext is low and there is no avalanche effect. Therefore, it cannot resist differential attack. This paper analyzes the algorithm from three vulnerabilities, respectively, and proposes three decoding methods. Meanwhile, targeted measures are discussed to resist common attacks. Finally, the three methods are extended to cryptanalysis on general encryption algorithm with similar vulnerabilities. Theoretical analysis and experimental results show that these three decoding methods are effective.

本文从三个方面分析了一种基于三维混沌的新型加密算法的安全性。该算法采用典型的两轮置换扩散结构，由键选择、置换和带反馈的扩散三部分组成。键由3个初始值、3个控制参数、1个干涉参数和2个三维混沌相关系数组成。该算法通过了直方图、相关性、差异分析、趋势波动分析等测试，验证了算法的安全性。然而，经过本文的分析，该算法存在三大漏洞：首先，由于原密钥与明文无关，该算法具有等效密钥。其次，扩散是一种线性操作，它导致了明文的像素值与构成密文像素值的密钥之间的可分离性。因此，可以根据密文的特点对明文进行重构。第三，扩散中的反馈使得密文对明文具有较强的敏感性，但明文对密文的敏感性较低，不存在雪崩效应。因此，它无法抵抗差分攻击。本文分别从三个漏洞对该算法进行了分析，并提出了三种解码方法。同时，讨论了针对常见攻击的针对性措施。最后，将这三种方法推广到具有类似漏洞的通用加密算法的密码分析中。理论分析和实验结果表明，这三种译码方法都是有效的。

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引用次数: 0

BISE: Enhance data sharing security through consortium blockchain and IPFS BISE：通过联盟区块链和IPFS增强数据共享安全性

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-05 DOI: 10.1016/j.jisa.2025.104320

Mingxuan Chen , Puhe Hao , Weizhi Meng , Yasen Aizezi , Guozi Sun

Data sharing is pivotal in sectors such as healthcare, finance, and social networking. Encrypting sensitive data, while essential for privacy protection, introduces complexity to data sharing and poses privacy risks when leveraging cloud servers. Blockchain-based searchable encryption offers a balance between privacy preservation and data availability; however, user anonymity remains a significant concern. Traditional storage systems, which rely on centralized servers, limit data stability and scalability. To address these challenges, we have introduced BISE, a solution that leverages the power of blockchain to achieve data integrity, using searchable encryption for secure searches and IPFS for decentralized storage. Constructed on Hyperledger Fabric and IPFS, our system demonstrates efficiency through simulations. This integrated approach ensures data privacy, integrity, and availability, with efficient updates and queries, making it a robust solution for sensitive data sharing in various domains.

数据共享在医疗保健、金融和社交网络等领域至关重要。对敏感数据进行加密虽然对隐私保护至关重要，但会给数据共享带来复杂性，并在利用云服务器时带来隐私风险。基于区块链的可搜索加密在隐私保护和数据可用性之间提供了平衡；然而，用户匿名仍然是一个重大问题。传统的存储系统依赖于集中式服务器，限制了数据的稳定性和可扩展性。为了应对这些挑战，我们引入了BISE，这是一种利用区块链功能实现数据完整性的解决方案，使用可搜索加密进行安全搜索，使用IPFS进行分散存储。本系统基于Hyperledger Fabric和IPFS架构，通过仿真验证了系统的有效性。这种集成的方法确保数据隐私、完整性和可用性，并具有高效的更新和查询，使其成为各种领域中敏感数据共享的健壮解决方案。

引用次数: 0

Lattice-based puncturable attribute-based proxy re-encryption scheme in cloud computing 云计算中基于格的可穿透属性代理重加密方案

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-05 DOI: 10.1016/j.jisa.2025.104325

Mengdi Zhao, Huiyan Chen, Xi Lin

Governmental and military organizations frequently manage sensitive documents that require both secure distribution and long-term preservation. These documents are typically encrypted and disseminated across multiple departments or agencies under access policies. To ensure confidentiality and accountability, attribute-based proxy re-encryption (ABPRE) allows flexible one-to-many data sharing. However, once receivers’ keys are exposed, unauthorized decryption of historical ciphertexts becomes possible, creating severe risks to national security and organizational integrity. The central challenge lies in reconciling document archiving with effective protection against post-compromise leakage. To tackle this issue, we present a lattice-based puncturable key-policy attribute-based proxy re-encryption (P-KP-ABPRE) scheme. In our design, recipients may autonomously revoke decryption capability for specific tags, thereby revoking access to selected ciphertexts without requiring data owner involvement or global re-encryption. This recipient-driven revocation mechanism not only achieves forward security but also reduces system overhead while preserving the reusability of ciphertexts. Built upon the learning with errors (LWE) assumption, our scheme supports multi-bit encryption, and demonstrates security against quantum attacks and chosen-plaintext attacks (CPA).

政府和军事组织经常管理需要安全分发和长期保存的敏感文件。这些文档通常是加密的，并根据访问策略在多个部门或机构之间传播。为了确保机密性和可问责性，基于属性的代理重加密（ABPRE）允许灵活的一对多数据共享。然而，一旦接收者的密钥被暴露，就有可能对历史密文进行未经授权的解密，从而对国家安全和组织完整性造成严重威胁。核心的挑战在于协调文件存档与有效防止泄漏后的保护。为了解决这个问题，我们提出了一种基于格子的可穿透密钥策略属性的代理重加密方案（P-KP-ABPRE）。在我们的设计中，接收者可以自主撤销特定标签的解密能力，从而撤销对选定密文的访问，而不需要数据所有者参与或全局重新加密。这种由接收方驱动的撤销机制不仅实现了前向安全性，而且在保证密文可重用性的同时降低了系统开销。基于错误学习（LWE）假设，我们的方案支持多比特加密，并演示了对量子攻击和选择明文攻击（CPA）的安全性。

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引用次数: 0

MVNIDS: A multiview-based network intrusion detection system MVNIDS：基于多视图的网络入侵检测系统

IF 3.7 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Information Security and Applications

Pub Date : 2025-12-02 DOI: 10.1016/j.jisa.2025.104321

Sunit Kumar Nandi, Ritesh Ratti, Sanasam Ranbir Singh, Sukumar Nandi

Traditional Network Intrusion Detection Systems (NIDS) predominantly rely on signature-based and supervised learning approaches that require large volumes of labeled attack data. However, generating such labeled data is costly, time-consuming, and often impractical, especially in the presence of class imbalance and zero-day attacks. To address these limitations, this paper proposes MVNIDS, a Multiview-based Network Intrusion Detection System employing a self-supervised learning paradigm. The proposed method constructs three complementary views from raw packet capture data, namely, Network View, Flow View, and Image View, each capturing distinct protocol, temporal, and structural characteristics of network traffic. Independent autoencoder models are trained on benign samples for each view, and their reconstruction errors are fused through a majority-voting mechanism to automatically generate “Attack” and “Benign” pseudo-labels. These labels are subsequently used to train a binary classifier for final intrusion detection. Experimental evaluation on the CICIDS2018 dataset, focusing on FTP BruteForce and UDP DoS attacks, demonstrates that MVNIDS outperforms most view-specific and supervised baselines, achieving up to 98.3 % F1-score and 98.5 % accuracy. The multiview representation enhances detection robustness and enables effective identification of zero-day and variant attacks, highlighting MVNIDS as a scalable, computationally efficient, and generalizable framework for modern network security applications.

传统的网络入侵检测系统（NIDS）主要依赖于基于签名和监督学习的方法，这些方法需要大量标记攻击数据。然而，生成这样的标记数据是昂贵的、耗时的，而且通常是不切实际的，特别是在存在类不平衡和零日攻击的情况下。为了解决这些限制，本文提出了MVNIDS，一种采用自监督学习范式的基于多视图的网络入侵检测系统。该方法从原始数据包捕获数据构建三个互补视图，即网络视图、流视图和图像视图，每个视图捕获网络流量的不同协议、时间和结构特征。在每个视图的良性样本上训练独立的自编码器模型，并通过多数投票机制融合其重建误差，自动生成“攻击”和“良性”伪标签。这些标签随后用于训练用于最终入侵检测的二值分类器。对CICIDS2018数据集的实验评估，重点是FTP暴力攻击和UDP DoS攻击，表明MVNIDS优于大多数特定视图和监督基线，达到98.3%的f1得分和98.5%的准确率。多视图表示增强了检测鲁棒性，能够有效识别零日攻击和变体攻击，突出了MVNIDS作为现代网络安全应用的可扩展、计算效率高和可通用的框架。

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引用次数: 0