Cybersecurity最新文献

Cloud EMRs auditing with decentralized (t, n)-threshold ownership transfer 利用分散式（t，n）阈值所有权转移进行云医疗记录审计

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-09-18 DOI: 10.1186/s42400-024-00246-4

Yamei Wang, Weijing You, Yuexin Zhang, Ayong Ye, Li Xu

{"title":"Cloud EMRs auditing with decentralized (t, n)-threshold ownership transfer","authors":"Yamei Wang, Weijing You, Yuexin Zhang, Ayong Ye, Li Xu","doi":"10.1186/s42400-024-00246-4","DOIUrl":"https://doi.org/10.1186/s42400-024-00246-4","url":null,"abstract":"In certain cloud Electronic Medical Records (EMRs) applications, the data ownership may need to be transferred. In practice, not only the data but also the auditing ability should be transferred securely and efficiently. However, we investigate and find that most of the existing data ownership transfer protocols only work well between two individuals, and they become inefficient when dealing between two communities. The proposals for transferring tags between communities are problematic as well since, they require all members get involved or a fully trusted aggregator facilitates ownership transfer, which are unrealistic in certain scenarios. To alleviate these problems, in this paper we develop a secure auditing protocol with decentralized (t, n)-threshold ownership transfer for cloud EMRs. This protocol is designed to operate efficiently without requiring the mandatory participation of every user or the involvement of any trusted third-party. It is achieved by employing the threshold signature. Rigorous security analyses and comprehensive performance evaluations illustrate the security and practicality of our protocol. Specifically, according to the evaluations and comparisons, the communication and computational consumption is independent of the file size, i.e., it is constant in our protocol for both communities.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249405","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

SIFT: Sifting file types—application of explainable artificial intelligence in cyber forensics SIFT：筛选文件类型--可解释人工智能在网络取证中的应用

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-09-11 DOI: 10.1186/s42400-024-00241-9

Shahid Alam, Alper Kamil Demir

{"title":"SIFT: Sifting file types—application of explainable artificial intelligence in cyber forensics","authors":"Shahid Alam, Alper Kamil Demir","doi":"10.1186/s42400-024-00241-9","DOIUrl":"https://doi.org/10.1186/s42400-024-00241-9","url":null,"abstract":"Artificial Intelligence (AI) is being applied to improve the efficiency of software systems used in various domains, especially in the health and forensic sciences. Explainable AI (XAI) is one of the fields of AI that interprets and explains the methods used in AI. One of the techniques used in XAI to provide such interpretations is by computing the relevance of the input features to the output of an AI model. File fragment classification is one of the vital issues of file carving in Cyber Forensics (CF) and becomes challenging when the filesystem metadata is missing. Other major challenges it faces are: proliferation of file formats, file embeddings, automation, We leverage and utilize interpretations provided by XAI to optimize the classification of file fragments and propose a novel sifting approach, named SIFT (Sifting File Types). SIFT employs TF-IDF to assign weight to a byte (feature), which is used to select features from a file fragment. Threshold-based LIME and SHAP (the two XAI techniques) feature relevance values are computed for the selected features to optimize file fragment classification. To improve multinomial classification, a Multilayer Perceptron model is developed and optimized with five hidden layers, each layer with (i times n) neurons, where i = the layer number and n = the total number of classes in the dataset. When tested with 47,482 samples of 20 file types (classes), SIFT achieves a detection rate of 82.1% and outperforms the other state-of-the-art techniques by at least 10%. To the best of our knowledge, this is the first effort of applying XAI in CF for optimizing file fragment classification.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181192","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

Modelling user notification scenarios in privacy policies 隐私政策中的用户通知情景建模

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-09-04 DOI: 10.1186/s42400-024-00234-8

Mikhail Kuznetsov, Evgenia Novikova, Igor Kotenko

引用次数: 0

FLSec-RPL: a fuzzy logic-based intrusion detection scheme for securing RPL-based IoT networks against DIO neighbor suppression attacks FLSec-RPL：基于模糊逻辑的入侵检测方案，用于保护基于 RPL 的物联网网络免受 DIO 邻居压制攻击

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-09-03 DOI: 10.1186/s42400-024-00223-x

Chenset Kim, Chakchai So-In, Yanika Kongsorot, Phet Aimtongkham

{"title":"FLSec-RPL: a fuzzy logic-based intrusion detection scheme for securing RPL-based IoT networks against DIO neighbor suppression attacks","authors":"Chenset Kim, Chakchai So-In, Yanika Kongsorot, Phet Aimtongkham","doi":"10.1186/s42400-024-00223-x","DOIUrl":"https://doi.org/10.1186/s42400-024-00223-x","url":null,"abstract":"The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet-connected devices has led to concerns regarding privacy and security. In particular, related to the routing protocol for low-power and lossy networks (RPL), which lacks robust security functions, many IoT devices in RPL networks are resource-constrained, with limited computing power, bandwidth, memory, and battery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifically targets neighboring nodes through DIO messages and poses a significant security threat to RPL-based IoT networks. Recent studies have proposed methods for detecting and mitigating this attack; however, they produce high false-positive and false-negative rates in detection tasks and cannot fully protect RPL networks against this attack type. In this paper, we propose a novel fuzzy logic-based intrusion detection scheme to secure the RPL protocol (FLSec-RPL) to protect against this attack. Our method is built of three key phases consecutively: (1) it tracks attack activity variables to determine potential malicious behaviors; (2) it performs fuzzy logic-based intrusion detection to identify malicious neighbor nodes; and (3) it provides a detection validation and blocking mechanism to ensure that both malicious and suspected malicious nodes are accurately detected and blocked. To evaluate the effectiveness of our method, we conduct comprehensive experiments across diverse scenarios, including Static-RPL and Mobile-RPL networks. We compare the performance of our proposed method with that of the state-of-the-art methods. The results demonstrate that our method outperforms existing methods in terms of the detection accuracy, F1 score, power consumption, end-to-end delay, and packet delivery ratio metrics.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181195","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

New partial key exposure attacks on RSA with additive exponent blinding 利用加法指数致盲对 RSA 进行新的部分密钥暴露攻击

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-09-02 DOI: 10.1186/s42400-024-00214-y

Ziming Jiang, Yongbin Zhou, Yuejun Liu

{"title":"New partial key exposure attacks on RSA with additive exponent blinding","authors":"Ziming Jiang, Yongbin Zhou, Yuejun Liu","doi":"10.1186/s42400-024-00214-y","DOIUrl":"https://doi.org/10.1186/s42400-024-00214-y","url":null,"abstract":"Partial key exposure attacks present a significant threat to RSA-type cryptosystems. These attacks factorize the RSA modulus by utilizing partial knowledge of the decryption exponent, which is typically revealed by side-channel attacks, cold boot attacks, etc. In practice, the RSA implementations typically employ countermeasures to resist physical attacks, such as additive exponent blinding (d' = d + r varphi (N)) with unknown random blinding factor r. Although there are a couple of partial key exposure attacks on blinding RSA, these attacks require a considerable amount of leakage and fail to work when e is up to full size. In this paper, we propose new partial key exposure attacks on RSA with additive exponent blinding, focusing on leakage scenarios where the Most Significant Bits (MSBs) or Least Significant Bits (LSBs) of (d') are revealed. For the case where e is small, we first recover partial information of p by solving the quadratic congruence equation, and then find the small roots of the integer equation to recover entire private key. Our method relaxes the attack requirements, for instance, we reduce the amount of MSBs for a successful attack from 75 to 25% when (e approx N^{0.25}) and (rapprox N^{0}). Furthermore, we propose new attacks using the unique algebraic relationship in blinding RSA, which extend the attack to the case where e is of full size.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181194","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

Dynamic group fuzzy extractor 动态组模糊提取器

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-09-01 DOI: 10.1186/s42400-024-00210-2

Kaini Chen, Peisong Shen, Kewei Lv, Xue Tian, Chi Chen

引用次数: 0

EvilPromptFuzzer: generating inappropriate content based on text-to-image models EvilPromptFuzzer：基于文本到图像模型生成不当内容

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-08-26 DOI: 10.1186/s42400-024-00279-9

Juntao He, Haoran Dai, Runqi Sui, Xuejing Yuan, Dun Liu, Hao Feng, Xinyue Liu, Wenchuan Yang, Baojiang Cui, Kedan Li

{"title":"EvilPromptFuzzer: generating inappropriate content based on text-to-image models","authors":"Juntao He, Haoran Dai, Runqi Sui, Xuejing Yuan, Dun Liu, Hao Feng, Xinyue Liu, Wenchuan Yang, Baojiang Cui, Kedan Li","doi":"10.1186/s42400-024-00279-9","DOIUrl":"https://doi.org/10.1186/s42400-024-00279-9","url":null,"abstract":"Text-to-image (TTI) models provide huge innovation ability for many industries, while the content security triggered by them has also attracted wide attention. Considerable research has focused on content security threats of large language models (LLMs), yet comprehensive studies on the content security of TTI models are notably scarce. This paper introduces a systematic tool, named EvilPromptFuzzer, designed to fuzz evil prompts in TTI models. For 15 kinds of fine-grained risks, EvilPromptFuzzer employs the strong knowledge-mining ability of LLMs to construct seed banks, in which the seeds cover various types of characters, interrelations, actions, objects, expressions, body parts, locations, surroundings, etc. Subsequently, these seeds are fed into the LLMs to build scene-diverse prompts, which can weaken the semantic sensitivity related to the fine-grained risks. Hence, the prompts can bypass the content audit mechanism of the TTI model, and ultimately help to generate images with inappropriate content. For the risks of violence, horrible, disgusting, animal cruelty, religious bias, political symbol, and extremism, the efficiency of EvilPromptFuzzer for generating inappropriate images based on DALL.E 3 are greater than 30%, namely, more than 30 generated images are malicious among 100 prompts. Specifically, the efficiency of horrible, disgusting, political symbols, and extremism up to 58%, 64%, 71%, and 50%, respectively. Additionally, we analyzed the vulnerability of existing popular content audit platforms, including Amazon, Google, Azure, and Baidu. Even the most effective Google SafeSearch cloud platform identifies only 33.85% of malicious images across three distinct categories.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181197","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

ProcSAGE: an efficient host threat detection method based on graph representation learning ProcSAGE：基于图表示学习的高效主机威胁检测方法

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-08-25 DOI: 10.1186/s42400-024-00240-w

Boyuan Xu, Yiru Gong, Xiaoyu Geng, Yun Li, Cong Dong, Song Liu, Yuling Liu, Bo Jiang, Zhigang Lu

{"title":"ProcSAGE: an efficient host threat detection method based on graph representation learning","authors":"Boyuan Xu, Yiru Gong, Xiaoyu Geng, Yun Li, Cong Dong, Song Liu, Yuling Liu, Bo Jiang, Zhigang Lu","doi":"10.1186/s42400-024-00240-w","DOIUrl":"https://doi.org/10.1186/s42400-024-00240-w","url":null,"abstract":"Advanced Persistent Threats (APTs) achieves internal networks penetration through multiple methods, making it difficult to detect attack clues solely through boundary defense measures. To address this challenge, some research has proposed threat detection methods based on provenance graphs, which leverage entity relationships such as processes, files, and sockets found in host audit logs. However, these methods are generally inefficient, especially when faced with massive audit logs and the computational resource-intensive nature of graph algorithms. Effectively and economically extracting APT attack clues from massive system audit logs remains a significant challenge. To tackle this problem, this paper introduces the ProcSAGE method, which detects threats based on abnormal behavior patterns, offering high accuracy, low cost, and independence from expert knowledge. ProcSAGE focuses on processes or threads in host audit logs during the graph construction phase to effectively control the scale of provenance graphs and reduce performance overhead. Additionally, in the feature extraction phase, ProcSAGE considers information about the processes or threads themselves and their neighboring nodes to accurately characterize them and enhance model accuracy. In order to verify the effectiveness of the ProcSAGE method, this study conducted a comprehensive evaluation on the StreamSpot dataset. The experimental results show that the ProcSAGE method can significantly reduce the time and memory consumption in the threat detection process while improving the accuracy, and the optimization effect becomes more significant as the data size expands.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181199","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

Lightweight ring-neighbor-based user authentication and group-key agreement for internet of drones 无人机互联网基于邻圈的轻量级用户身份验证和群组密钥协议

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-08-18 DOI: 10.1186/s42400-024-00247-3

Zhuo Zhao, Chingfang Hsu, Lein Harn, Zhe Xia, Xinyu Jiang, Liu Liu

{"title":"Lightweight ring-neighbor-based user authentication and group-key agreement for internet of drones","authors":"Zhuo Zhao, Chingfang Hsu, Lein Harn, Zhe Xia, Xinyu Jiang, Liu Liu","doi":"10.1186/s42400-024-00247-3","DOIUrl":"https://doi.org/10.1186/s42400-024-00247-3","url":null,"abstract":"As mobile internet and Internet of Things technologies continue to advance, the application scenarios of peer-to-peer Internet of Drones (IoD) are becoming increasingly diverse. However, the development of IoD also faces significant challenges, such as security, privacy protection, and limited computing power, which require technological innovation to overcome. For group secure communication, it is necessary to provide two basic services, user authentication and group key agreement. Due to the limited storage of IoD devices, group key negotiation requires lightweight calculations, and conventional schemes cannot satisfy the requirements of group communication in the IoD. To this end, a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD, which not only realizes the identity verification of user and group key negotiation, but also improves computational efficiency on each group member side. A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defined security requirements. More importantly, in our proposal, the computational cost on the user side remains unaffected by the variability of the number of members participating in group communication, as members communicate in a non-interactive manner through broadcasting. As a result, the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes. Hence, this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181198","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

A multi-channel spatial information feature based human pose estimation algorithm 基于多通道空间信息特征的人体姿态估计算法

IF 3.1 4区计算机科学

Cybersecurity Pub Date : 2024-08-11 DOI: 10.1186/s42400-024-00248-2

Yinghong Xie, Yan Hao, Xiaowei Han, Qiang Gao, Biao Yin

{"title":"A multi-channel spatial information feature based human pose estimation algorithm","authors":"Yinghong Xie, Yan Hao, Xiaowei Han, Qiang Gao, Biao Yin","doi":"10.1186/s42400-024-00248-2","DOIUrl":"https://doi.org/10.1186/s42400-024-00248-2","url":null,"abstract":"Human pose estimation is an important task in computer vision, which can provide key point detection of human body and obtain bone information. At present, human pose estimation is mainly utilized for detection of large targets, and there is no solution for detection of small targets. This paper proposes a multi-channel spatial information feature based human pose (MCSF-Pose) estimation algorithm to address the issue of medium and small targets inaccurate detection of human key points in scenarios involving occlusion and multiple poses. The MCSF-Pose network is a bottom-up regression network. Firstly, an UP-Focus module is designed to expand the feature information while reducing parameter computation during the up-sampling process. Then, the channel segmentation strategy is adopted to cut the features, and the feature information of multiple dimensions is retained through different convolutional groups, which reduces the parameter lightweight network model and makes up for the loss of the feature information associated with the depth of the network. Finally, the three-layer PANet structure is designed to reduce the complexity of the model. With the aid of the structure, it also to improve the detection accuracy and anti-interference ability of human key points. The experimental results indicate that the proposed algorithm outperforms YOLO-Pose and other human pose estimation algorithms on COCO2017 and MPII human pose datasets.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932915","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