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Thematische Bibliographien / Anti-computer forensics / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Anti-computer forensics“

Autor: Grafiati

Veröffentlicht am 24. Juni 2021

Zuletzt aktualisiert am 17. Februar 2022

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1

Aziz, Benjamin, Clive Blackwell und Shareeful Islam. „A Framework for Digital Forensics and Investigations“. International Journal of Digital Crime and Forensics 5, Nr. 2 (April 2013): 1–22. http://dx.doi.org/10.4018/jdcf.2013040101.

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Digital forensics investigations are an important task for collecting evidence based on the artifacts left in computer systems for computer related crimes. The requirements of such investigations are often a neglected aspect in most of the existing models of digital investigations. Therefore, a formal and systematic approach is needed to provide a framework for modeling and reasoning about the requirements of digital investigations. In addition, anti-forensics situations make the forensic investigation process challenging by contaminating any stage of the investigation process, its requirements, or by destroying the evidence. Therefore, successful forensic investigations require understanding the possible anti-forensic issues during the investigation. In this paper, the authors present a new method for guiding digital forensics investigations considering the anti-forensics based on goal-driven requirements engineering methodologies, in particular KAOS. Methodologies like KAOS facilitate modeling and reasoning about goals, requirements and obstacles, as well as their operationalization and responsibility assignments. The authors believe that this new method will lead in the future to better management and organization of the various steps of forensics investigations in cyberspace as well as provide more robust grounds for reasoning about forensic evidence.

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2

Yang, Pengpeng, Daniele Baracchi, Rongrong Ni, Yao Zhao, Fabrizio Argenti und Alessandro Piva. „A Survey of Deep Learning-Based Source Image Forensics“. Journal of Imaging 6, Nr. 3 (04.03.2020): 9. http://dx.doi.org/10.3390/jimaging6030009.

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Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.

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3

Zhong, Xiu Yu, und Feng Zeng. „A New Approach of Computer Forensics Based on Steganalysis“. Advanced Materials Research 631-632 (Januar 2013): 1385–89. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1385.

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Criminals often destructed or hided evidence after making crime by computer, they hindered computer forensics by anti-forensics technology. A new approach of computer forensics based on steganalysis is proposed. The common anti-forensics technologies, such as steganography, data encryption delete evidence and make forensics invalid. In order to enhance the evidence efficiency, steganalysis is applied in the computer forensics to collect and transfer evidence. Simulation results show that steganography based on least significant bit (LSB) by java program embeds the text files into the BMP image files, which sizes are nearly invariable. Steganalysis restores the hidden information and provides convenient method for computer forensics.

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4

Chandran, Rahul, und Wei Q. Yan. „Attack Graph Analysis for Network Anti-Forensics“. International Journal of Digital Crime and Forensics 6, Nr. 1 (Januar 2014): 28–50. http://dx.doi.org/10.4018/ijdcf.2014010103.

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The development of technology in computer networks has boosted the percentage of cyber-attacks today. Hackers are now able to penetrate even the strongest IDS and firewalls. With the help of anti-forensic techniques, attackers defend themselves, from being tracked by destroying and distorting evidences. To detect and prevent network attacks, the main modus of operandi in network forensics is the successful implementation and analysis of attack graph from gathered evidences. This paper conveys the main concepts of attack graphs, requirements for modeling and implementation of graphs. It also contributes the aspect of incorporation of anti-forensic techniques in attack graph which will help in analysis of the diverse possibilities of attack path deviations and thus aids in recommendation of various defense strategies for better security. To the best of our knowledge, this is the first time network anti-forensics has been fully discussed and the attack graphs are employed to analyze the network attacks. The experimental analysis of anti-forensic techniques using attack graphs were conducted in the proposed test-bed which helped to evaluate the model proposed and suggests preventive measures for the improvement of security of the networks.

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Dahbur, Kamal, und Bassil Mohammad. „Toward Understanding the Challenges and Countermeasures in Computer Anti-Forensics“. International Journal of Cloud Applications and Computing 1, Nr. 3 (Juli 2011): 22–35. http://dx.doi.org/10.4018/ijcac.2011070103.

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The term computer anti-forensics (CAF) generally refers to a set of tactical and technical measures intended to circumvent the efforts and objectives of the field of computer and network forensics (CF). Many scientific techniques, procedures, and technological tools have evolved and effectively applied in the field of CF to assist scientists and investigators in acquiring and analyzing digital evidence for the purpose of solving cases that involve the use or misuse of computer systems. CAF has emerged as a CF counterpart that plants obstacles throughout the path of computer investigations. The purpose of this paper is to highlight the challenges introduced by anti-forensics, explore various CAF mechanisms, tools, and techniques, provide a coherent classification for them, and discuss their effectiveness. Moreover, the authors discuss the challenges in implementing effective countermeasures against these techniques. A set of recommendations are presented with future research opportunities.

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6

Jing Peng, Can Wang und Hu Wu. „A Novel File-Concealing Method for Computer Anti-Forensics“. Journal of Convergence Information Technology 8, Nr. 6 (31.03.2013): 1203–10. http://dx.doi.org/10.4156/jcit.vol8.issue6.143.

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7

Chen, Hu. „Dilemmas in Digital Forensics for Computer Equipment Security and Maintenance in Remote Ships“. Advanced Materials Research 490-495 (März 2012): 1382–86. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1382.

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As remote ships have equipped a large number of computer equipments, the maintenance of such equipments confronts a great challenge. Since some embedded devices among them may be hacked by attackers or disabled by Byzantine failure, to discover the attacking originality and fault source present foremost importance. In this article, we discuss digital investigation and forensics as a general viewpoint. We point out some dilemmas that hinder the development of digital forensics, some of which may be fundamental problems. We propose to expand the concept of digital forensics to a wider scope so as to include digital investigation for information instead of only evidence. We also argue that the fostering of novel contributions should be relied on technical experts instead of law experts as emerging new techniques always result in new digital crimes. We promote the divorce between the technical experts who focus on the contribution of technologies, and legal authorities who are responsible to bridge the gap between technologies and standard/formalization. Digital forensics methods are encouraged to be publicly available, but the contributors should be aware of the possibility of anti-forensics.

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Castillo Camacho, Ivan, und Kai Wang. „A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics“. Journal of Imaging 7, Nr. 4 (03.04.2021): 69. http://dx.doi.org/10.3390/jimaging7040069.

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Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering the necessity of specialized knowledge has been the focus for companies who create and sell these tools. Furthermore, image forgeries are presently so realistic that it becomes difficult for the naked eye to differentiate between fake and real media. This can bring different problems, from misleading public opinion to the usage of doctored proof in court. For these reasons, it is important to have tools that can help us discern the truth. This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods. In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification of computer graphics images and detection of emerging Deepfake images. With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them. A review of different image databases and an overview of anti-forensic methods are also presented. Finally, we suggest some future working directions that the research community could consider to tackle in a more effective way the spread of doctored images.

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Sihwail, Rami, Khairuddin Omar, Khairul Zainol Ariffin und Sanad Al Afghani. „Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis“. Applied Sciences 9, Nr. 18 (05.09.2019): 3680. http://dx.doi.org/10.3390/app9183680.

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The need to detect malware before it harms computers, mobile phones and other electronic devices has caught the attention of researchers and the anti-malware industry for many years. To protect users from malware attacks, anti-virus software products are downloaded on the computer. The anti-virus mainly uses signature-based techniques to detect malware. However, this technique fails to detect malware that uses packing, encryption or obfuscation techniques. It also fails to detect unseen (new) ones. This paper proposes an integrated malware detection approach that applies memory forensics to extract malicious artifacts from memory and combines them to features extracted during the execution of malware in a dynamic analysis. Pre-modeling techniques were also applied for feature engineering before training and testing the data set on the machine learning models. The experimental results show a significant improvement in both detection accuracy rate and false positive rate, 98.5% and 1.7% respectively, by applying the support vector machine. The results verify that our integrated analysis approach outperforms other analysis methods. In addition, the proposed approach overcomes the limitation of single path file execution in dynamic analysis by adding more relevant memory artifacts that can reveal the real intention of malicious files.

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Berghel, Hal. „Hiding data, forensics, and anti-forensics“. Communications of the ACM 50, Nr. 4 (April 2007): 15–20. http://dx.doi.org/10.1145/1232743.1232761.

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11

Qureshi, Muhammad Ali, und El-Sayed M. El-Alfy. „Bibliography of digital image anti-forensics and anti-anti-forensics techniques“. IET Image Processing 13, Nr. 11 (19.09.2019): 1811–23. http://dx.doi.org/10.1049/iet-ipr.2018.6587.

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12

Sartin, Bryan. „ANTI-Forensics – distorting the evidence“. Computer Fraud & Security 2006, Nr. 5 (Mai 2006): 4–6. http://dx.doi.org/10.1016/s1361-3723(06)70354-2.

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13

Kowalski, Marcin, und Krzysztof Mierzejewski. „Detection of 3D face masks with thermal infrared imaging and deep learning techniques“. Photonics Letters of Poland 13, Nr. 2 (30.06.2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.

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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural network-featured method for detecting presentation attacks. Full Text: PDF ReferencesS.R. Arashloo, J. Kittler, W. Christmas, "Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features", IEEE Trans. Inf. Forensics Secur. 10, 11 (2015). CrossRef A. Anjos, M.M. Chakka, S. Marcel, "Motion-based counter-measures to photo attacks inface recognition", IET Biometrics 3, 3 (2014). CrossRef M. Killioǧlu, M. Taşkiran, N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking", Proc. SAMI IEEE, (2017). CrossRef A. Asaduzzaman, A. Mummidi, M.F. Mridha, F.N. Sibai, "Improving facial recognition accuracy by applying liveness monitoring technique", Proc. ICAEE IEEE, (2015). CrossRef M. Kowalski, "A Study on Presentation Attack Detection in Thermal Infrared", Sensors 20, 14 (2020). CrossRef C. Galdi, et al, "PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study", IET Biometrics 9, 6 (2020). CrossRef D.A. Socolinsky, A. Selinger, J. Neuheisel, "Face recognition with visible and thermal infrared imagery", Comput. Vis Image Underst. 91, 1-2 (2003) CrossRef L. Sun, W. Huang, M. Wu, "TIR/VIS Correlation for Liveness Detection in Face Recognition", Proc. CAIP, (2011). CrossRef J. Seo, I. Chung, "Face Liveness Detection Using Thermal Face-CNN with External Knowledge", Symmetry 2019, 11, 3 (2019). CrossRef A. George, Z. Mostaani, D Geissenbuhler, et al., "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network", IEEE Trans. Inf. Forensics Secur. 15, (2020). CrossRef S. Ren, K. He, R. Girshick, J. Sun, "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition", Proc. CVPR IEEE 39, (2016). CrossRef K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", Proc. CVPR, (2016). CrossRef K. Mierzejewski, M. Mazurek, "A New Framework for Assessing Similarity Measure Impact on Classification Confidence Based on Probabilistic Record Linkage Model", Procedia Manufacturing 44, 245-252 (2020). CrossRef

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14

Distefano, Alessandro, Gianluigi Me und Francesco Pace. „Android anti-forensics through a local paradigm“. Digital Investigation 7 (August 2010): S83—S94. http://dx.doi.org/10.1016/j.diin.2010.05.011.

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15

Lee, Kyoungho, Hyunuk Hwang, Kibom Kim und BongNam Noh. „Robust bootstrapping memory analysis against anti-forensics“. Digital Investigation 18 (August 2016): S23—S32. http://dx.doi.org/10.1016/j.diin.2016.04.009.

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16

Forte, Dario, und Richard Power. „A tour through the realm of anti-forensics“. Computer Fraud & Security 2007, Nr. 6 (Juni 2007): 18–20. http://dx.doi.org/10.1016/s1361-3723(07)70079-9.

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17

Wani, Mohamad Ahtisham, Ali AlZahrani und Wasim Ahmad Bhat. „File system anti-forensics – types, techniques and tools“. Computer Fraud & Security 2020, Nr. 3 (März 2020): 14–19. http://dx.doi.org/10.1016/s1361-3723(20)30030-0.

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18

Bosschert, Thijs. „Battling Anti-Forensics: Beating the U3 Stick“. Journal of Digital Forensic Practice 1, Nr. 4 (22.06.2007): 265–73. http://dx.doi.org/10.1080/15567280701417975.

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19

Cadle, Brian A., Kristin C. Rasmus, Juan A. Varela, Leah S. Leverich, Casey E. O'Neill, Ryan K. Bachtell und Donald C. Cooper. „Cellular Phone-Based Image Acquisition and Quantitative Ratiometric Method for Detecting Cocaine and Benzoylecgonine for Biological and Forensic Applications“. Substance Abuse: Research and Treatment 4 (Januar 2010): SART.S5025. http://dx.doi.org/10.4137/sart.s5025.

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Here we describe the first report of using low-cost cellular or web-based digital cameras to image and quantify standardized rapid immunoassay strips as a new point-of-care diagnostic and forensics tool with health applications. Quantitative ratiometric pixel density analysis (QRPDA) is an automated method requiring end-users to utilize inexpensive (~ $1 USD/each) immunotest strips, a commonly available web or mobile phone camera or scanner, and internet or cellular service. A model is described whereby a central computer server and freely available IMAGEJ image analysis software records and analyzes the incoming image data with time-stamp and geo-tag information and performs the QRPDA using custom JAVA based macros ( http://www.neurocloud.org ). To demonstrate QRPDA we developed a standardized method using rapid immunotest strips directed against cocaine and its major metabolite, benzoylecgonine. Images from standardized samples were acquired using several devices, including a mobile phone camera, web cam, and scanner. We performed image analysis of three brands of commercially available dye-conjugated anti-cocaine/benzoylecgonine (COC/BE) antibody test strips in response to three different series of cocaine concentrations ranging from 0.1 to 300 ng/ml and BE concentrations ranging from 0.003 to 0.1 ng/ml. This data was then used to create standard curves to allow quantification of COC/BE in biological samples. Across all devices, QRPDA quantification of COC and BE proved to be a sensitive, economical, and faster alternative to more costly methods, such as gas chromatography-mass spectrometry, tandem mass spectrometry, or high pressure liquid chromatography. The limit of detection was determined to be between 0.1 and 5 ng/ml. To simulate conditions in the field, QRPDA was found to be robust under a variety of image acquisition and testing conditions that varied temperature, lighting, resolution, magnification and concentrations of biological fluid in a sample. To determine the effectiveness of the QRPDA method for quantifying cocaine in biological samples, mice were injected with a sub-locomotor activating dose of cocaine (5 mg/kg; i.p.) and were found to have detectable levels of COC/BE in their urine (160.6 ng/ml) and blood plasma (8.1 ng/ml) after 15–30 minutes. By comparison rats self-administering cocaine in a 4 hour session obtained a final BE blood plasma level of 910 ng/ml with an average of 62.5 infusions. It is concluded that automated QRPDA is a low-cost, rapid and highly sensitive method for the detection of COC/BE with health, forensics, and bioinformatics application and the potential to be used with other rapid immunotest strips directed at several other targets. Thus, this report serves as a general reference and method describing the use of image analysis of lateral flow rapid test strips.

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Wang, Z. J., Min Wu, H. V. Zhao, W. Trappe und K. J. Ray Liu. „Anti-collusion forensics of multimedia fingerprinting using orthogonal modulation“. IEEE Transactions on Image Processing 14, Nr. 6 (Juni 2005): 804–21. http://dx.doi.org/10.1109/tip.2005.847284.

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21

Hilley, S. „Anti-forensics with a small army of exploits“. Digital Investigation 4, Nr. 1 (März 2007): 13–15. http://dx.doi.org/10.1016/j.diin.2007.01.005.

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22

Dallaway, Eleanor. „Steganography is key ingredient to anti-forensics“. Infosecurity 5, Nr. 8 (November 2008): 11. http://dx.doi.org/10.1016/s1754-4548(08)70146-3.

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23

Smith, Aaron. „Describing and Categorizing Disk-Avoiding Anti-Forensics Tools“. Journal of Digital Forensic Practice 1, Nr. 4 (22.06.2007): 309–13. http://dx.doi.org/10.1080/15567280701418155.

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24

Ahn, Na Young, und Dong Hoon Lee. „Forensics and Anti-Forensics of a NAND Flash Memory: From a Copy-Back Program Perspective“. IEEE Access 9 (2021): 14130–37. http://dx.doi.org/10.1109/access.2021.3052353.

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25

Sharma, Shishir, Hareesh Ravi, A. V. Subramanyam und Sabu Emmanuel. „Anti-forensics of median filtering and contrast enhancement“. Journal of Visual Communication and Image Representation 66 (Januar 2020): 102682. http://dx.doi.org/10.1016/j.jvcir.2019.102682.

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26

Wu, Jianyuan, und Wei Sun. „Towards multi-operation image anti-forensics with generative adversarial networks“. Computers & Security 100 (Januar 2021): 102083. http://dx.doi.org/10.1016/j.cose.2020.102083.

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27

Sun, Hung-Min, Chi-Yao Weng, Chin-Feng Lee und Cheng-Hsing Yang. „Anti-Forensics with Steganographic Data Embedding in Digital Images“. IEEE Journal on Selected Areas in Communications 29, Nr. 7 (August 2011): 1392–403. http://dx.doi.org/10.1109/jsac.2011.110806.

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28

Chuang, Wei-Hong, Ravi Garg und Min Wu. „Anti-Forensics and Countermeasures of Electrical Network Frequency Analysis“. IEEE Transactions on Information Forensics and Security 8, Nr. 12 (Dezember 2013): 2073–88. http://dx.doi.org/10.1109/tifs.2013.2285515.

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29

Zou, Hao, Pengpeng Yang, Rongrong Ni und Yao Zhao. „Anti-Forensics of Image Contrast Enhancement Based on Generative Adversarial Network“. Security and Communication Networks 2021 (24.03.2021): 1–8. http://dx.doi.org/10.1155/2021/6663486.

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In the multimedia forensics community, anti-forensics of contrast enhancement (CE) in digital images is an important topic to understand the vulnerability of the corresponding CE forensic method. Some traditional CE anti-forensic methods have demonstrated their effective forging ability to erase forensic fingerprints of the contrast-enhanced image in histogram and even gray level cooccurrence matrix (GLCM), while they ignore the problem that their ways of pixel value changes can expose them in the pixel domain. In this paper, we focus on the study of CE anti-forensics based on Generative Adversarial Network (GAN) to handle the problem mentioned above. Firstly, we exploit GAN to process the contrast-enhanced image and make it indistinguishable from the unaltered one in the pixel domain. Secondly, we introduce a specially designed histogram-based loss to enhance the attack effectiveness in the histogram domain and the GLCM domain. Thirdly, we use a pixel-wise loss to keep the visual enhancement effect of the processed image. The experimental results show that our method achieves high anti-forensic attack performance against CE detectors in the pixel domain, the histogram domain, and the GLCM domain, respectively, and maintains the highest image quality compared with traditional CE anti-forensic methods.

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Li, Xiaowen, Diqun Yan, Li Dong und Rangding Wang. „Anti-Forensics of Audio Source Identification Using Generative Adversarial Network“. IEEE Access 7 (2019): 184332–39. http://dx.doi.org/10.1109/access.2019.2960097.

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31

Conlan, Kevin, Ibrahim Baggili und Frank Breitinger. „Anti-forensics: Furthering digital forensic science through a new extended, granular taxonomy“. Digital Investigation 18 (August 2016): S66—S75. http://dx.doi.org/10.1016/j.diin.2016.04.006.

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Kim, Dohyun, Wonhyuk Ahn und Heung-Kyu Lee. „End-to-End Anti-Forensics Network of Single and Double JPEG Detection“. IEEE Access 9 (2021): 13390–402. http://dx.doi.org/10.1109/access.2021.3051678.

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33

Forte, Dario. „Dealing with forensic software vulnerabilities: is anti-forensics a real danger?“ Network Security 2008, Nr. 12 (Dezember 2008): 18–20. http://dx.doi.org/10.1016/s1353-4858(08)70143-0.

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34

Fan, Wei, Kai Wang, Francois Cayre und Zhang Xiong. „JPEG Anti-Forensics With Improved Tradeoff Between Forensic Undetectability and Image Quality“. IEEE Transactions on Information Forensics and Security 9, Nr. 8 (August 2014): 1211–26. http://dx.doi.org/10.1109/tifs.2014.2317949.

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35

Akbar, Muh Hajar, Sunardi Sunardi und Imam Riadi. „Steganalysis Bukti Digital pada Media Storage Menggunakan Metode GCFIM“. JISKA (Jurnal Informatika Sunan Kalijaga) 5, Nr. 2 (10.09.2020): 96. http://dx.doi.org/10.14421/jiska.2020.52-04.

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Steganography is an anti-forensic technique that allows a criminal to hide information in other messages, so that during an examination it will be difficult to obtain evidence of the crime information. Therefore we need a technique to detect hidden messages in the data. This technique is known as steganalysis. Steganalysis is an anti-steganography science whose main purpose is to study the hiding characteristics of data on digital media and detect the existence of secret messages that are hidden using steganography techniques. The purpose of this study is to apply steganalysis techniques to detect the presence of messages that are hidden in other messages by using the forensic method, namely Generic Computer Forensic Investigation Model (GCFIM). In this study, the process of inserting steganographic messages using the Hiderman application, while the steganalysis process uses the StegSpy application. The results obtained in this study were the process of steganalysis using the help of the StegSpy application proved to be successful in detecting the presence of hidden messages in the five files that were scanned by steganographic messages.

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Das, Tanmoy Kanti. „Anti-forensics of JPEG compression detection schemes using approximation of DCT coefficients“. Multimedia Tools and Applications 77, Nr. 24 (12.06.2018): 31835–54. http://dx.doi.org/10.1007/s11042-018-6170-7.

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37

Göbel, Thomas, und Harald Baier. „Anti-forensics in ext4: On secrecy and usability of timestamp-based data hiding“. Digital Investigation 24 (März 2018): S111—S120. http://dx.doi.org/10.1016/j.diin.2018.01.014.

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38

Goh, Weihan, Peng Chor Leong und Chai Kiat Yeo. „A Plausibly-Deniable, Practical Trusted Platform Module Based Anti-Forensics Client-Server System“. IEEE Journal on Selected Areas in Communications 29, Nr. 7 (August 2011): 1377–91. http://dx.doi.org/10.1109/jsac.2011.110805.

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39

Cui, Qi, Ruohan Meng, Zhili Zhou, Xingming Sun und Kaiwen Zhu. „An anti-forensic scheme on computer graphic images and natural images using generative adversarial networks“. Mathematical Biosciences and Engineering 16, Nr. 5 (2019): 4923–35. http://dx.doi.org/10.3934/mbe.2019248.

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Singh, Kulbir, Ankush Kansal und Gurinder Singh. „An improved median filtering anti-forensics with better image quality and forensic undetectability“. Multidimensional Systems and Signal Processing 30, Nr. 4 (27.02.2019): 1951–74. http://dx.doi.org/10.1007/s11045-019-00637-8.

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Bharathiraja S und Rajesh Kanna B. „Anti-Forensics Contrast Enhancement Detection (AFCED) Technique in Images Based on Laplace Derivative Histogram“. Mobile Networks and Applications 24, Nr. 4 (19.04.2019): 1174–80. http://dx.doi.org/10.1007/s11036-019-01255-1.

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Jung, Seungwon, Seunghee Seo, Yeog Kim und Changhoon Lee. „Memory Layout Extraction and Verification Method for Reliable Physical Memory Acquisition“. Electronics 10, Nr. 12 (09.06.2021): 1380. http://dx.doi.org/10.3390/electronics10121380.

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Physical memory acquisition is a prerequisite when performing memory forensics, referring to a set of techniques for acquiring and analyzing traces associated with user activity information, malware analysis, cyber incident response, and similar areas when the traces remain in the physical RAM. However, certain types of malware have applied anti-memory forensics techniques to evade memory analysis strategies or to make the acquisition process impossible. To disturb the acquisition process of physical memory, an attacker hooks the kernel API, which returns a map of the physical memory spaces, and modifies the return value of the API, specifically that typically used by memory acquisition tools. Moreover, an attacker modifies the kernel object referenced by the kernel API. This causes the system to crash during the memory acquisition process or causes the memory acquisition tools to incorrectly proceed with the acquisition. Even with a modification of one byte, called a one-byte modification attack, some tools fail to acquire memory. Therefore, specialized countermeasure techniques are needed for these anti-memory forensics techniques. In this paper, we propose a memory layout acquisition method which is robust to kernel API hooking and the one-byte modification attack on NumberOfRuns, the kernel object used to construct the memory layout in Windows. The proposed acquisition method directly accesses the memory, extracts the byte array, and parses it in the form of a memory layout. When we access the memory, we extract the _PHYSICAL_MEMORY_DESCRIPTOR structure, which is the basis of the memory layout without using the existing memory layout acquisition API. Furthermore, we propose a verification method that selects a reliable memory layout. We realize the verification method by comparing NumberOfRuns and the memory layout acquired via the kernel API, the registry, and the proposed method. The proposed verification method guarantees the reliability of the memory layout and helps secure memory image acquisition through a comparative verification with existing memory layout acquisition methods. We also conduct experiments to prove that the proposed method is resistant to anti-memory forensics techniques, confirming that there are no significant differences in time compared to the existing tools.

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Wang, Yongwei, Xin Ding, Yixin Yang, Li Ding, Rabab Ward und Z. Jane Wang. „Perception matters: Exploring imperceptible and transferable anti-forensics for GAN-generated fake face imagery detection“. Pattern Recognition Letters 146 (Juni 2021): 15–22. http://dx.doi.org/10.1016/j.patrec.2021.03.009.

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Fan, Wei, Kai Wang, Francois Cayre und Zhang Xiong. „Corrections to “JPEG Anti-Forensics With Improved Tradeoff Between Forensic Undetectability and Image Quality” [Aug 14 1211-1226]“. IEEE Transactions on Information Forensics and Security 11, Nr. 11 (November 2016): 2628. http://dx.doi.org/10.1109/tifs.2016.2585398.

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Wesley Lane, Simon. „Are local authority fraud teams fit for purpose?“ Journal of Financial Crime 18, Nr. 2 (10.05.2011): 195–213. http://dx.doi.org/10.1108/13590791111127769.

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PurposeThe purpose of this paper is to analyse fraud investigative practice in London local authorities with reference to recognised best practice and two comparator organisations, the Department for Work and Pensions (DWP) and National Health Service (NHS).Design/methodology/approachPrimary research was undertaken through questionnaires to all London Boroughs and interviews with key personnel in two comparator organisations.FindingsEach London Borough has a specialist anti‐fraud response with professionally qualified investigators, demonstrates compliance with best practice and excels in areas such as case supervision and joint working. However, concerns remain, regarding a lack of agreed national standards and some failing to use the full range of investigative techniques, such as surveillance and computer forensic examination.Research limitations/implicationsThe research was limited to London local government and further work is needed outside the capital.Practical implicationsRecommendations are made for: the introduction of national professional guidance to investigators; minimum competency standards for fraud investigation; research into the applicability of the National Intelligence Model to high volume fraud; and a less fragmented approach both within and across local authorities.Originality/valueThere has been no previous research of this type and it may be useful to government when considering how to deal with fraud, local authorities and those with an interest in public sector fraud.

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Agarwal, Saurabh, und Ki-Hyun Jung. „HSB-SPAM: An Efficient Image Filtering Detection Technique“. Applied Sciences 11, Nr. 9 (21.04.2021): 3749. http://dx.doi.org/10.3390/app11093749.

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Median filtering is being used extensively for image enhancement and anti-forensics. It is also being used to disguise the traces of image processing operations such as JPEG compression and image resampling when utilized in image de-noising and smoothing tool. In this paper, a robust image forensic technique namely HSB-SPAM is proposed to assist in median filtering detection. The proposed technique considers the higher significant bit-plane (HSB) of the image to highlight the statistical changes efficiently. Further, multiple difference arrays along with the first order pixel difference is used to separate the pixel difference, and Laplacian pixel difference is applied to extract a robust feature set. To compact the size of feature vectors, the operation of thresholding on the difference arrays is also utilized. As a result, the proposed detector is able to detect median, mean and Gaussian filtering operations with higher accuracy than the existing detectors. In the experimental results, the performance of the proposed detector is validated on the small size and post JPEG compressed images, where it is shown that the proposed method outperforms the state of art detectors in the most of the cases.

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de Capoa, A., C. Grappelli, F. R. Febbo, A. Span�, A. Niveleau, A. Cafolla, I. Cordone und R. Foa. „Methylation levels of normal and chronic lymphocytic leukemia B lymphocytes: computer-assisted quantitative analysis of anti-5-methylcytosine antibody binding to individual nuclei“. Cytometry 36, Nr. 2 (01.06.1999): 157–59. http://dx.doi.org/10.1002/(sici)1097-0320(19990601)36:2<157::aid-cyto10>3.0.co;2-k.

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Salamh, Fahad E., Umit Karabiyik, Marcus K. Rogers und Eric T. Matson. „A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges“. Drones 5, Nr. 2 (21.05.2021): 42. http://dx.doi.org/10.3390/drones5020042.

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The raising accessibility of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, is rapidly increasing. Recent studies have discussed challenges that may come in tow with the growing use of this technology. These studies note that in-depth examination is required, especially when addressing challenges that carry a high volume of software data between sensors, actuators, and control commands. This work underlines static and live digital evidence traceability challenges to further enhance the UAV incident response plan. To study the live UAV forensic traceability issues, we apply the ‘purple-teaming’ exercise on small UAVs while conducting UAV forensic examination to determine technical challenges related to data integrity and repeatability. In addition, this research highlights current static technical challenges that could pose more challenges in justifying the discovered digital evidence. Additionally, this study discusses potential drone anti-forensic techniques and their association with the type of use, environment, attack vector, and level of expertise. To this end, we propose the UAV Kill Chain and categorize the impact and complexity of all highlighted challenges based on the conducted examination and the presented scientific contribution in this work. To the best of our knowledge, there has not been any contribution that incorporates ‘Purple-Teaming’ tactics to evaluate UAV-related research in cybersecurity and digital forensics. This work also proposes a categorization model that classifies the discovered UAV static and live digital evidence challenges based on their complexity and impact levels.

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Widmaier, Moritz, Tobias Wiestler, Jill Walker, Craig Barker, Marietta L. Scott, Farzad Sekhavati, Alexei Budco et al. „Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis“. Modern Pathology 33, Nr. 3 (16.09.2019): 380–90. http://dx.doi.org/10.1038/s41379-019-0349-y.

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Abstract Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays.

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Rahardjo, Budi, und I. Putu Agus Eka Pratama. „Pengujian Dan Analisa Anti Komputer Forensik Menggunakan Shred Tool“. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 01.08.2016, 104. http://dx.doi.org/10.24843/lkjiti.2016.v07.i02.p04.

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Computer forensics and anti computer forensics are two opposing fields. Computer forensics is done by a computer forensics expert in order to obtain accurate data and evidence of cyber crime cases for investigation, while the anti-computer forensics conducted by the attacker to remove traces at once difficult computer forensics expert in performing its duties. For the attacker, the selection of anti-computer forensics tool that default on the target machine, more effective and faster than installing it first on the victim machine. For this reason the author chose shred as anti computer forensics applications on GNU / Linux machine. If anti forensic work, forensic experts would be difficult to perform computer forensics to data as evidence of cyber crime. This paper describes the anti-forensics performed by the attacker to remote machines GNU / Linux for cyber crime cases in a computer network. Anti forensic performed using shred the syslog file to remove traces of the crime at the same time make it difficult for the forensic process by computer forensics expert. Tests performed on three pieces of computer-based GNU / Linux on System Signals Lab intranet ITB. Each act as the target machine (server), firewall machine, and the machine attacker. Doing the anti computer forensics and computer forensics at the server machine. The test results are recorded and analyzed in order to then be deduced.

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