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International Journal of Digital Crime and Forensics最新文献

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P2DF: A Privacy-Preserving Digital Forensics Framework P2DF:一个保护隐私的数字取证框架
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/IJDCF.288547
M. Abulaish, Nur Al Hasan Haldar, Jahiruddin Sharma
{"title":"P2DF: A Privacy-Preserving Digital Forensics Framework","authors":"M. Abulaish, Nur Al Hasan Haldar, Jahiruddin Sharma","doi":"10.4018/IJDCF.288547","DOIUrl":"https://doi.org/10.4018/IJDCF.288547","url":null,"abstract":"The extensive use of digital devices by individuals generates a significant amount of private data which creates challenges for investigation agencies to protect suspects’ privacy. Existing digital forensics models illustrate the steps and actions to be followed during an investigation, but most of them are inadequate to investigate a crime with all the processes in an integrated manner and do not protect suspect privacy. In this paper, the authors propose the development of a privacy-preserving digital forensics (P2DF) framework, which facilitates investigation through maintaining confidentiality of the suspects through various privacy standards and policies. It includes an access control mechanism which allows only authorized investigators to access private data and identified digital evidence. It is also equipped with a digital evidence preservation mechanism which could be helpful for the court of law to ensure the authenticity, confidentiality, and reliability of the evidence and to verify whether privacy of the suspect was preserved during the investigation process.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85412813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Reversible Data Hiding in a Chaotic Encryption Domain Based on Odevity Verification 基于方差验证的混沌加密域可逆数据隐藏
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.20211101.oa9
Lianshan Liu, Xiaoli Wang, Lingzhuang Meng, Gang Tian, Ting Wang
{"title":"Reversible Data Hiding in a Chaotic Encryption Domain Based on Odevity Verification","authors":"Lianshan Liu, Xiaoli Wang, Lingzhuang Meng, Gang Tian, Ting Wang","doi":"10.4018/ijdcf.20211101.oa9","DOIUrl":"https://doi.org/10.4018/ijdcf.20211101.oa9","url":null,"abstract":"On the premise of guaranteeing the visual effect, in order to improve the security of the image containing digital watermarking and restore the carrier image without distortion, reversible data hiding in chaotic encryption domain based on odevity verification was proposed. The original image was scrambled and encrypted by Henon mapping, and the redundancy between the pixels of the encrypted image was lost. Then, the embedding capacity of watermarking can be improved by using odevity verification, and the embedding location of watermarking can be randomly selected by using logistic mapping. When extracting the watermarking, the embedded data was judged according to the odevity of the pixel value of the embedding position of the watermarking, and the carrier image was restored nondestructively by odevity check image. The experimental results show that the peak signal-to-noise ratio (PSNR) of the original image is above 53 decibels after the image is decrypted and restored after embedding the watermarking in the encrypted domain, and the invisibility is good.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78157232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hidden Service Circuit Reconstruction Attacks Based on Middle Node Traffic Analysis 基于中间节点流量分析的隐藏业务电路重构攻击
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.288548
Yitong Meng, Jin-long Fei
{"title":"Hidden Service Circuit Reconstruction Attacks Based on Middle Node Traffic Analysis","authors":"Yitong Meng, Jin-long Fei","doi":"10.4018/ijdcf.288548","DOIUrl":"https://doi.org/10.4018/ijdcf.288548","url":null,"abstract":"Traffic analysis is widely considered as an attack posing a threat to anonymity of the communication and may reveal the real identity of the users. In this paper, a novel anonymous circuit reconstruction attack method that correlates the circuit traffic is proposed. This method then reconstructs a complete communication tunnel using the location of middle nodes found between the hidden and client services. The attack process includes independent determination of the location of the malicious nodes. A traffic correlation framework of AutoEncoder + CNN + BiLSTM is established, based on the Generative Adversarial Networks (GAN) model. BiLSTM applies the packet size and packet interval features of bidirectional traffic and combines the reconstruction loss function with the discrimination loss function to achieve correlated traffic evaluation. After balancing the reconstruction loss and discrimination loss scores, the simulation results confirm that the identification performance of the proposed system is higher than the advanced models.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85007189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Towards Automated Detection of Higher-Order Command Injection Vulnerabilities in IoT Devices: Fuzzing With Dynamic Data Flow Analysis 物联网设备中高阶命令注入漏洞的自动检测:动态数据流分析的模糊测试
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.286755
Lei Yu, Haoyu Wang, Linyu Li, Houhua He
{"title":"Towards Automated Detection of Higher-Order Command Injection Vulnerabilities in IoT Devices: Fuzzing With Dynamic Data Flow Analysis","authors":"Lei Yu, Haoyu Wang, Linyu Li, Houhua He","doi":"10.4018/ijdcf.286755","DOIUrl":"https://doi.org/10.4018/ijdcf.286755","url":null,"abstract":"Command injection vulnerabilities are among the most common and dangerous attack vectors in IoT devices. Current detection approaches can detect single-step injection vulnerabilities well by fuzzing tests. However, an attacker could inject malicious commands in an IoT device via a multi-step exploit if he first abuses an interface to store the injection payload and later use it in a command interpreter through another interface. We identify a large class of such multi-step injection attacks to address these stealthy and harmful threats and define them as higher-order command injection vulnerabilities (HOCIVs). We develop an automatic system named Request Linking (ReLink) to detect data stores that would be transferred to command interpreters and then identify HOCIVs. ReLink is validated on an experimental embedded system injected with 150 HOCIVs. According to the experimental results, ReLink is significantly better than existing command injection detection tools in terms of detection rate, test space and time.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90978636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Image Forensic Tool (IFT): Image Retrieval, Tampering Detection, and Classification 图像取证工具(IFT):图像检索、篡改检测和分类
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.287606
Digambar Pawar, Mayank Gajpal
{"title":"Image Forensic Tool (IFT): Image Retrieval, Tampering Detection, and Classification","authors":"Digambar Pawar, Mayank Gajpal","doi":"10.4018/ijdcf.287606","DOIUrl":"https://doi.org/10.4018/ijdcf.287606","url":null,"abstract":"Images now-a-days are often used as an authenticated proof for any cyber-crime. Images that do not remain genuine can mislead the court of law. The fast and dynamically growing technology doubts the trust in the integrity of images. Tampering mostly refers to adding or removing important features from an image without leaving any obvious trace. In earlier days, digital signatures were used to preserve the integrity, but now a days various tools are available to tamper digital signatures as well. Even in various state-of-the-art works in tamper detection, there are various restrictions in the type of inputs and the type of tampering detection. In this paper, the researchers propose a prototype model in the form of a tool that will retrieve all the image files from given digital evidence and detect tampering in the images. For various types of tampering, different tampering detection algorithms have been used. The proposed prototype will detect if tampering has been done or not and will classify the image files into groups based on the type of tampering.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82264842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Coverless Text Steganography by Encoding the Chinese Characters' Component Structures 基于汉字成分结构编码的无盖文本隐写
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.302135
Kaixi Wang, Xiangmei Yu, Ziyi Zou
{"title":"A Coverless Text Steganography by Encoding the Chinese Characters' Component Structures","authors":"Kaixi Wang, Xiangmei Yu, Ziyi Zou","doi":"10.4018/ijdcf.302135","DOIUrl":"https://doi.org/10.4018/ijdcf.302135","url":null,"abstract":"The current coverless text steganography methods have a low steganographic capacity, and yet some of them cannot assure a message can be concealed. How to achieve a high steganographic capacity has become the research hotspot in text steganography. This paper proposes a text coverless steganography method by encoding the Chinese characters’ component structures. Its main idea is that a binary bit string can be conveyed by the Chinese characters’ component structures. The positions of Chinese characters that carry a secret message will be expressed in two systems of the linear remainder equations, whose solutions will be secretly sent to the receiver to extract the secret message. In the method, a single Chinese character can express p bits. The analyses and statistics show that its capacity will be much higher when the same Chinese character is used more than once than existing methods, and it can conceal any message successfully. In addition, this method can also be employed in other languages.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81854810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Survey of Human Gait Analysis and Recognition for Medical and Forensic Applications 人体步态分析和识别的医学和法医应用综述
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.289432
Shantanu Jana, N. Das, Subhadip Basu, M. Nasipuri
{"title":"Survey of Human Gait Analysis and Recognition for Medical and Forensic Applications","authors":"Shantanu Jana, N. Das, Subhadip Basu, M. Nasipuri","doi":"10.4018/ijdcf.289432","DOIUrl":"https://doi.org/10.4018/ijdcf.289432","url":null,"abstract":"Gait is a behavioural biometric which sometimes changes due to diseases but it is still a strong identification metric that is widely used in forensic works, state biometric preserve sectors, and medical laboratories. Gait analysis sometimes helps to identify person’s present mental state which reflects on physiological therapy for improved biological system. There are various gait measurement forms which expand the research area from crime detection to medical enhancement. Many research works have been done so far for gait recognition. Many researchers focused on skeleton image of people to extract gait features and many worked on stride length. Various sensors have been used to detect gait in various light forms. This paper is a brief survey of works on gait recognition, collected from various sources of science and technology literature. We have discussed few efficient models that worked best as well as we have discussed about few data sets available.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86747359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
An Incremental Acquisition Method for Web Forensics 一种用于网络取证的增量获取方法
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/IJDCF.2021110116
Guangxuan Chen, Guangxiao Chen, Lei Zhang, Qiang Liu
{"title":"An Incremental Acquisition Method for Web Forensics","authors":"Guangxuan Chen, Guangxiao Chen, Lei Zhang, Qiang Liu","doi":"10.4018/IJDCF.2021110116","DOIUrl":"https://doi.org/10.4018/IJDCF.2021110116","url":null,"abstract":"Inordertosolvetheproblemsofrepeatedacquisition,dataredundancy,andlowefficiencyintheprocessofwebsiteforensics,thispaperproposesanincrementalacquisitionmethodorientedtodynamicwebsites.Thismethodrealizedtheincrementalcollectionondynamicallyupdatedwebsitesthroughacquiringandparsingwebpages,URLdeduplication,webpagedenoising,webpagecontentextraction,andhashing.Experimentsshowthatthealgorithmhasrelativehighacquisitionprecisionandrecallrateandcanbecombinedwithotherdatatoperformeffectivedigitalforensicsondynamicallyupdatedreal-timewebsites.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75882434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
An Optimal NIDS for VCN Using Feature Selection and Deep Learning Technique: IDS for VCN 基于特征选择和深度学习技术的VCN网络入侵检测系统
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/IJDCF.20211101.OA10
P. Keserwani, M. C. Govil, E. Pilli, Prajjval Govil
{"title":"An Optimal NIDS for VCN Using Feature Selection and Deep Learning Technique: IDS for VCN","authors":"P. Keserwani, M. C. Govil, E. Pilli, Prajjval Govil","doi":"10.4018/IJDCF.20211101.OA10","DOIUrl":"https://doi.org/10.4018/IJDCF.20211101.OA10","url":null,"abstract":"In this modern era, due to demand for cloud environments in business, the size, complexity, and chance of attacks to virtual cloud network (VCN) are increased. The protection of VCN is required to maintain the faith of the cloud users. Intrusion detection is essential to secure any network. The existing approaches that use the conventional neural network cannot utilize all information for identifying the intrusions. In this paper, the anomaly-based NIDS for VCN is proposed. For feature selection, grey wolf optimization (GWO) is hybridized with a bald eagle search (BES) algorithm. For classification, a deep learning approach—deep sparse auto-encoder (DSAE)—is employed. In this way, this paper proposes a NIDS model for VCN named GWO-DES-DSAE. The proposed system is simulated in the python programming environment. The proposed NIDS model’s performance is compared with other recent approaches for both binary and multi-class classification on the considered datasets—NSL-KDD, UNSW-NB15, and CICIDS 2017—and found better than other methods. Deep Sparse Autoencoder (DSAE) has been utilized to learn the underlying traffic data structure. The proposed system improves performance and, hence producing reliable predictions. Evaluation of the results shows the quality and effectiveness of the proposed NIDS model, and the main contributions of this work are as follows:","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89231526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Detection of Anonymising Proxies Using Machine Learning 使用机器学习检测匿名代理
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.286756
Shane Miller, K. Curran, T. Lunney
{"title":"Detection of Anonymising Proxies Using Machine Learning","authors":"Shane Miller, K. Curran, T. Lunney","doi":"10.4018/ijdcf.286756","DOIUrl":"https://doi.org/10.4018/ijdcf.286756","url":null,"abstract":"Network Proxies and Virtual Private Networks (VPN) are tools that are used every day to facilitate various business functions. However, they have gained popularity amongst unintended userbases as tools that can be used to hide mask identities while using websites and web-services. Anonymising Proxies and/or VPNs act as an intermediary between a user and a web server with a Proxy and/or VPN IP address taking the place of the user’s IP address that is forwarded to the web server. This paper presents computational models based on intelligent machine learning techniques to address the limitations currently experienced by unauthorised user detection systems. A model to detect usage of anonymising proxies was developed using a Multi-layered perceptron neural network that was trained using data found in the Transmission Control Protocol (TCP) header of captured network packets","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74553389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
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