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International Journal of Digital Crime and Forensics最新文献

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A High Capacity Test Disguise Method Combined With Interpolation Backup and Double Authentications 结合插值备份和双重认证的高容量测试伪装方法
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.295815
Haining Lu, Liping Shao, Qinglong Wang
{"title":"A High Capacity Test Disguise Method Combined With Interpolation Backup and Double Authentications","authors":"Haining Lu, Liping Shao, Qinglong Wang","doi":"10.4018/ijdcf.295815","DOIUrl":"https://doi.org/10.4018/ijdcf.295815","url":null,"abstract":"To improve the hidden capacity of a single question, further avoid the absence of authentication and provide self-repair ability, this paper proposes a high capacity test disguise method combined with interpolation backup and double authentications. Firstly, secret byte sequence is backed up and further encoded to a backup index sequence by secret information backup and encoding strategy. Secondly, a test question database divided into eight sets is created. Finally, the backup index sequence is disguised as a stego test paper using 24 different candidate answer orders and 4-bit hash values. In restoration, double authentications are applied to authenticate candidate restored value, and the most reliable candidate restored values are obtained by the reliable calculation to reconstruct secret information. The experimental results and analysis show that the proposed method can distinguish error candidate restored values, and calculate the reliability of each restored byte. Moreover, it has excellent self-repair ability with a higher hidden capacity of a single question.","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":"80190728","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
Identification of Interpolated Frames by Motion-Compensated Frame-Interpolation via Measuring Irregularity of Optical Flow 运动补偿插框法识别插框-测量光流不均匀性
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.295813
Xiangling Ding, Yanming Huang, Dengyong Zhang, Junlin Ouyang
{"title":"Identification of Interpolated Frames by Motion-Compensated Frame-Interpolation via Measuring Irregularity of Optical Flow","authors":"Xiangling Ding, Yanming Huang, Dengyong Zhang, Junlin Ouyang","doi":"10.4018/ijdcf.295813","DOIUrl":"https://doi.org/10.4018/ijdcf.295813","url":null,"abstract":"Motion-compensated frame-interpolation (MCFI), synthesize intermediate frames between input frames guided by estimated motion, can be employed to falsify high bit-rate videos or high frame-rate videos with different frame-rates. Although existing MCFI identification methods have obtained satisfactory results, they are seriously degraded by stronger compression. Therefore, to conquer this issue, a blind forensics method is proposed to identify the adopted MCFI methods by considering the irregularities of optical flow produced by various MCFIs. In this paper, a set of compact features are constructed from the motion-aligned frame difference-weighted histogram of local binary pattern on the basis of optical flow (MAFD-WHLBP). Experimental results show that the proposed approach outperforms existing MCFI detectors under stronger compression.","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":"87545649","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
Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set 基于生物统计特征集的分裂聚类和k近邻网络机器人检测系统
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.302136
Rizwan Ur Rahman, D. Tomar
{"title":"Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set","authors":"Rizwan Ur Rahman, D. Tomar","doi":"10.4018/ijdcf.302136","DOIUrl":"https://doi.org/10.4018/ijdcf.302136","url":null,"abstract":"Web bots are destructive programs that automatically fill the web form and steal the data from web sites. According to numerous web bot traffic reports, web bots traffic comprises of more than fifty percent of the total web traffic. An effective guard against the stealing of the data from web sites and automated web form is to identify and confirm the human user presence on web sites. In this paper, an efficient k-Nearest Neighbor algorithm using hierarchical clustering for web bot detection is proposed. Proposed technique exploits a novel taxonomy of web bot features known as Biostatistics Features. Numerous attack scenarios for web bot attacks such as automatic account registration, automatic form filling, bulk message posting, and web scrapping are created to imitate the zero-day web bot attacks. The proposed technique is evaluated with number of experiments using standard evaluation parameters. The experimental result analysis demonstrates that the proposed technique is extremely efficient in differentiating human users from web bots.","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":"84748660","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
ROP Defense Using Trie Graph for System Security 基于三图的系统安全ROP防御
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/IJDCF.20211101.OA7
Alex Zhu, W. Yan, R. Sinha
{"title":"ROP Defense Using Trie Graph for System Security","authors":"Alex Zhu, W. Yan, R. Sinha","doi":"10.4018/IJDCF.20211101.OA7","DOIUrl":"https://doi.org/10.4018/IJDCF.20211101.OA7","url":null,"abstract":"Most intrusion detection systems (IDS)/intrusion prevention systems (IPS) cannot defend the attacks from a return-oriented program (ROP) that applies code reusing and exploiting techniques without the need for code injection. Malicious attackers chain a short sequence as a gadget and execute this gadget as an arbitrary (Turing-complete) behavior in the target program. Lots of ROP defense tools have been developed with satisfactory performance and low costs overhead, but malicious attackers can evade ROP tools. Therefore, it needs security researchers to continually improve existing ROP defense tools because the defense ability of target devices such as smartphones is weak, and such devices are being increasingly targeted. The contribution in this paper is to propose an ROP defense method that has provided a better performance of defense against ROP attacks than existing ROP defense tools.","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":"89541735","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
Two-Step Image-in-Image Steganography via GAN 基于GAN的两步图像中隐写
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.295814
Guan-Zhong Wu, Xiangyu Yu, Hui-hua Liang, Minting Li
{"title":"Two-Step Image-in-Image Steganography via GAN","authors":"Guan-Zhong Wu, Xiangyu Yu, Hui-hua Liang, Minting Li","doi":"10.4018/ijdcf.295814","DOIUrl":"https://doi.org/10.4018/ijdcf.295814","url":null,"abstract":"Recently, convolutional neural network has been introduced to information hiding and deep net- work has shown great potential in steganography. However, one drawback of deep network is that it’s sensitive to small fluctuations. In previous works, the encoder-decoder structure is trained end-to-end, but in practice, encoder and decoder should be used separately. Therefore, end-to-end trained steganography networks are vulnerable to fluctuations and the secret decoded from those networks suffers from unpleasant noise. In this work, we present an image-in-image steganog- raphy method called TISGAN to achieve better results, both in image quality and security. In particular, we divide the training process into two parts. Moreover, perceptual loss is applied to encoder, to improve security in our work. We also append a denoising structure to the end of de- coder to achieve better image quality. Finally, the adversarial structure with useful techniques employed is also used in secret revealed 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":"77639729","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
Cloud-Assisted Image Double Protection System With Encryption and Data Hiding Based on Compressive Sensing 基于压缩感知的云辅助图像加密和数据隐藏双重保护系统
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.295812
Di Xiao, Jia Liang, Y. Xiang, Jiaqi Zhou
{"title":"Cloud-Assisted Image Double Protection System With Encryption and Data Hiding Based on Compressive Sensing","authors":"Di Xiao, Jia Liang, Y. Xiang, Jiaqi Zhou","doi":"10.4018/ijdcf.295812","DOIUrl":"https://doi.org/10.4018/ijdcf.295812","url":null,"abstract":"In this paper, we propose a compressive sensing(CS)-based scheme that combines encryption and data hiding to provide double protection to the image data in the cloud outsourcing. Different domain techniques are integrated for efficiency and security. After the data holder gets the sample of the raw data, he embeds watermark into sample and encrypts it, and then sends the protected sample to cloud for storage and recovery. Cloud cannot get any information about either the original data or watermark in the CS recovery process. Finally, users can extract the watermark and decrypt the data recovered by cloud directly in sparse domain. At the same time, after extracting the watermark, the image data of user will be closer to the original data compared with the data without extraction. Besides, the counter (CTR) mode is introduced to generate the measurement matrix of CS, which can improve security while avoiding the storage of measurement matrixes. The experimental results demonstrate that the scheme can provide both privacy protection and copyright protection with high efficiency.","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":"73953824","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
HEVC Information-Hiding Algorithm Based on Intra-Prediction and Matrix Coding 基于内部预测和矩阵编码的HEVC信息隐藏算法
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/ijdcf.20211101.oa11
Yong Liu, Dawen Xu
{"title":"HEVC Information-Hiding Algorithm Based on Intra-Prediction and Matrix Coding","authors":"Yong Liu, Dawen Xu","doi":"10.4018/ijdcf.20211101.oa11","DOIUrl":"https://doi.org/10.4018/ijdcf.20211101.oa11","url":null,"abstract":"Aiming at the problem that the data hiding algorithm of high efficiency video coding (HEVC) has great influence on the video bit rate and visual quality, an information hiding algorithm based on intra prediction mode and matrix coding is proposed. Firstly, 8 prediction modes are selected from 4×4 luminance blocks in I frame to embed the hidden information. Then, the Least Significant Bit (LSB) algorithm is used to modulate the LSB of the last prediction mode. Finally, the modulated luminance block is re-encoded to embed 4 bits secret information. Experimental results show that the algorithm improves the embedding capacity, guarantees the subjective and objective quality of the video, and the bit rate increases by 1.14% on average.","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":"90073092","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
Identifying the Use of Anonymising Proxies to Conceal Source IP Addresses 识别使用匿名代理隐藏源IP地址
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-11-01 DOI: 10.4018/IJDCF.20211101.OA8
Shane Miller, K. Curran, T. Lunney
{"title":"Identifying the Use of Anonymising Proxies to Conceal Source IP Addresses","authors":"Shane Miller, K. Curran, T. Lunney","doi":"10.4018/IJDCF.20211101.OA8","DOIUrl":"https://doi.org/10.4018/IJDCF.20211101.OA8","url":null,"abstract":"The detection of unauthorised users can be problematic for techniques that are available at present if the nefarious actors are using identity hiding tools such as anonymising proxies or virtual private networks (VPNs). This work presents computational models to address the limitations currently experienced in detecting VPN traffic. The experiments conducted to classify OpenVPN usage found that the neural network was able to correctly identify the VPN traffic with an overall accuracy of 93.71%. These results demonstrate a significant advancement in the detection of unauthorised user access with evidence showing that there could be further advances for research in this field particularly in the application of business security where the detection of VPN usage is important to an organization.","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":"88261248","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
Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks 基于网络参数挖掘的无线传感器网络恶意节点检测
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-09-01 DOI: 10.4018/IJDCF.20210901.OA8
R. Sunitha, J. Chandrika
{"title":"Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks","authors":"R. Sunitha, J. Chandrika","doi":"10.4018/IJDCF.20210901.OA8","DOIUrl":"https://doi.org/10.4018/IJDCF.20210901.OA8","url":null,"abstract":"The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82496891","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 Model of Cloud Forensic Application With Assurance of Cloud Log 具有云日志保障的云取证应用模型
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-09-01 DOI: 10.4018/IJDCF.20210901.OA7
M. S. Das, A. Govardhan, D. Vijayalakshmi
{"title":"A Model of Cloud Forensic Application With Assurance of Cloud Log","authors":"M. S. Das, A. Govardhan, D. Vijayalakshmi","doi":"10.4018/IJDCF.20210901.OA7","DOIUrl":"https://doi.org/10.4018/IJDCF.20210901.OA7","url":null,"abstract":"The key concepts of digital forensic investigation in cloud computing are examination and investigation. Cybercriminals target cloud-based web applications due to presence of vulnerabilities. Forensic investigation is a complex process, where a set of activities are involved. The cloud log history plays an important role in the investigation and evidence collection. The existing model in cloud log information requires more security. The proposed model used for forensic application with the assurance of cloud log that helps the digital and cloud forensic investigators for collecting forensic scientific evidences. The cloud preservation and cloud log data encryption method is implemented in java. The real-time dataset, network dataset results tell that attacks with the highest attack type are generic type, and a case conducted chat log will predict the attacks in advance by keywork antology learning process, NLP, and AI techniques.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89529590","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
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