Cybersecurity最新文献_第6页

F3l: an automated and secure function-level low-overhead labeled encrypted traffic dataset construction method for IM in Android F3l：用于安卓即时通讯的自动化安全功能级低开销标记加密流量数据集构建方法

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-01-01 DOI: 10.1186/s42400-023-00185-6

Keya Xu, Guang Cheng

{"title":"F3l: an automated and secure function-level low-overhead labeled encrypted traffic dataset construction method for IM in Android","authors":"Keya Xu, Guang Cheng","doi":"10.1186/s42400-023-00185-6","DOIUrl":"https://doi.org/10.1186/s42400-023-00185-6","url":null,"abstract":"<p>Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security. Machine learning and deep learning have become mainstream methods to analyze traffic, and labeled dataset construction is the basis. Android occupies a huge share of the mobile operating system market. Instant Messaging (IM) applications are important tools for people communication. But such applications have complex functions which frequently switched, so it is difficult to obtain function-level labels. The existing function-level public datasets in Android are rare and noisy, leading to research stagnation. Most labeled samples are collected with WLAN devices, which cannot exclude the operating system background traffic. At the same time, other datasets need to obtain root permission or use scripts to simulate user behavior. These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained. Previous work (Chen et al. in Appl Sci 12(22):11731, 2022) proposed a one-stop automated encrypted traffic labeled sample collection, construction, and correlation system, A3C, running at the application-level in Android. This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android, F3L. The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground. It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions. The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels. Experiments show that the method can match the correct label within 3 s after the user operation.</p>","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":"27 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139070445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

WAS: improved white-box cryptographic algorithm over AS iteration WAS：比 AS 迭代改进的白箱加密算法

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2023-12-08 DOI: 10.1186/s42400-023-00192-7

Yatao Yang, Yuying Zhai, Hui Dong, Yanshuo Zhang

{"title":"WAS: improved white-box cryptographic algorithm over AS iteration","authors":"Yatao Yang, Yuying Zhai, Hui Dong, Yanshuo Zhang","doi":"10.1186/s42400-023-00192-7","DOIUrl":"https://doi.org/10.1186/s42400-023-00192-7","url":null,"abstract":"<p>The attacker in white-box model has full access to software implementation of a cryptographic algorithm and full control over its execution environment. In order to solve the issues of high storage cost and inadequate security about most current white-box cryptographic schemes, WAS, an improved white-box cryptographic algorithm over AS iteration is proposed. This scheme utilizes the AS iterative structure to construct a lookup table with a five-layer ASASA structure, and the maximum distance separable matrix is used as a linear layer to achieve complete diffusion in a small number of rounds. Attackers can be prevented from recovering the key under black-box model. The length of nonlinear layer S and affine layer A in lookup table is 16 bits, which effectively avoids decomposition attack against the ASASA structure and makes the algorithm possess anti-key extraction security under the white-box model, while WAS possesses weak white-box (32 KB, 112)-space hardness to satisfy anti-code lifting security. WAS has provable security and better storage cost than existing schemes, with the same anti-key extraction security and anti-code lifting security, only 128 KB of memory space is required in WAS, which is only 14% of SPACE-16 algorithm and 33% of Yoroi-16 algorithm.</p>","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":"102 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138556256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Minimizing CNOT-count in quantum circuit of the extended Shor’s algorithm for ECDLP ECDLP中扩展Shor算法量子电路中cnot计数最小化

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2023-12-06 DOI: 10.1186/s42400-023-00181-w

Xia Liu, Huan Yang, Li Yang

{"title":"Minimizing CNOT-count in quantum circuit of the extended Shor’s algorithm for ECDLP","authors":"Xia Liu, Huan Yang, Li Yang","doi":"10.1186/s42400-023-00181-w","DOIUrl":"https://doi.org/10.1186/s42400-023-00181-w","url":null,"abstract":"<p>The elliptic curve discrete logarithm problem (ECDLP) is a popular choice for cryptosystems due to its high level of security. However, with the advent of the extended Shor’s algorithm, there is concern that ECDLP may soon be vulnerable. While the algorithm does offer hope in solving ECDLP, it is still uncertain whether it can pose a real threat in practice. From the perspective of the quantum circuits of the algorithm, this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s algorithm. We give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime fields, including modular subtraction, three different modular multiplication, and modular inverse. Additionally, we incorporate and improve upon windowed arithmetic in the circuits to reduce the CNOT-counts. Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit, we focus on minimizing the number of CNOT gates in the circuit, which greatly affects the running time of the algorithm on an ion trap quantum computer. Specifically, we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts, along with improved constructions for modular inverse, point addition, and windowed arithmetic. Next, we precisely estimate that, to execute the extended Shor’s algorithm with the improved circuits to factor an <i>n</i>-bit integer, the CNOT-count required is <span>(1237n^3/log n+2n^2+n)</span>. Finally, we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.</p>","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":"789 ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Towards the transferable audio adversarial attack via ensemble methods 基于集成方法的可转移音频对抗性攻击

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2023-12-05 DOI: 10.1186/s42400-023-00175-8

Feng Guo, Zheng Sun, Yuxuan Chen, Lei Ju

引用次数: 0

LayerCFL: an efficient federated learning with layer-wised clustering LayerCFL:一种具有分层聚类的高效联邦学习

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2023-12-04 DOI: 10.1186/s42400-023-00172-x

Jie Yuan, Rui Qian, Tingting Yuan, Mingliang Sun, Jirui Li, Xiaoyong Li

引用次数: 0

A novel botnet attack detection for IoT networks based on communication graphs 基于通信图的物联网僵尸网络攻击检测

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2023-12-03 DOI: 10.1186/s42400-023-00169-6

David Concejal Muñoz, Antonio del-Corte Valiente

{"title":"A novel botnet attack detection for IoT networks based on communication graphs","authors":"David Concejal Muñoz, Antonio del-Corte Valiente","doi":"10.1186/s42400-023-00169-6","DOIUrl":"https://doi.org/10.1186/s42400-023-00169-6","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Intrusion detection systems have been proposed for the detection of botnet attacks. Various types of centralized or distributed cloud-based machine learning and deep learning models have been suggested. However, the emergence of the Internet of Things (IoT) has brought about a huge increase in connected devices, necessitating a different approach. In this paper, we propose to perform detection on IoT-edge devices. The suggested architecture includes an anomaly intrusion detection system in the application layer of IoT-edge devices, arranged in software-defined networks. IoT-edge devices request information from the software-defined networks controller about their own behaviour in the network. This behaviour is represented by communication graphs and is novel for IoT networks. This representation better characterizes the behaviour of the device than the traditional analysis of network traffic, with a lower volume of information. Botnet attack scenarios are simulated with the IoT-23 dataset. Experimental results show that attacks are detected with high accuracy using a deep learning model with low device memory requirements and significant storage reduction for training.\u0000</p><h3 data-test=\"abstract-sub-heading\">Graphical abstract</h3>\u0000","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":"761 ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Machine learning based fileless malware traffic classification using image visualization 基于机器学习的无文件恶意软件流量分类

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2023-12-02 DOI: 10.1186/s42400-023-00170-z

Fikirte Ayalke Demmese, Ajaya Neupane, Sajad Khorsandroo, May Wang, Kaushik Roy, Yu Fu

{"title":"Machine learning based fileless malware traffic classification using image visualization","authors":"Fikirte Ayalke Demmese, Ajaya Neupane, Sajad Khorsandroo, May Wang, Kaushik Roy, Yu Fu","doi":"10.1186/s42400-023-00170-z","DOIUrl":"https://doi.org/10.1186/s42400-023-00170-z","url":null,"abstract":"<p>In today’s interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software.</p>","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":"766 ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Full-round impossible differential attack on shadow block cipher 对影子区块密码的全轮不可能差分攻击

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2023-12-01 DOI: 10.1186/s42400-023-00184-7

Yuting Liu, Yongqiang Li, Huiqin Chen, Mingsheng Wang

引用次数: 0

Research on privacy information retrieval model based on hybrid homomorphic encryption 基于混合同态加密的隐私信息检索模型研究

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2023-12-01 DOI: 10.1186/s42400-023-00168-7

Wei-tao Song, Guang Zeng, Wen-zheng Zhang, Dian-hua Tang

引用次数: 0

Performance evaluation of Cuckoo filters as an enhancement tool for password cracking 杜鹃滤波器作为密码破解增强工具的性能评价