research-article

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SoK: exploring the state of the art and the future potential of artificial intelligence in digital forensic investigation

Authors:

Chris Hargreaves,

Asanka Sayakkara,

Nhien-An Le-Khac,

Mark ScanlonAuthors Info & Claims

ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security

Article No.: 46, Pages 1 - 10

https://doi.org/10.1145/3407023.3407068

Published: 25 August 2020 Publication History

Abstract

Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe. Digital forensic investigators are overloaded with the volume of cases requiring their expertise compounded by the volume of data to be processed. Artificial intelligence is often seen as the solution to many big data problems. This paper summarises existing artificial intelligence based tools and approaches in digital forensics. Automated evidence processing leveraging artificial intelligence based techniques shows great promise in expediting the digital forensic analysis process while increasing case processing capacities. For each application of artificial intelligence highlighted, a number of current challenges and future potential impact is discussed.

References

[1]

Razan Abdulhammed, Hassan Musafer, Ali Alessa, Miad Faezipour, and Abdelshakour Abuzneid. 2019. Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection. 8 (March 2019).

[2]

Mohiuddin Ahmed, Abdun. Naser, and Jiankun Hu. 2016. A Survey of Network Anomaly Detection Techniques. J. Netw. Comput. Appl. 60, C (Jan. 2016), 19--31.

Digital Library

[3]

Dogukan Aksu, Serpil Ustebay, Muhammed Ali Aydin, and Tülin Atmaca. 2018. Intrusion Detection with Comparative Analysis of Supervised Learning Techniques and Fisher Score Feature Selection Algorithm. 141--149.

[4]

Mohammad Alauthman, Nauman Aslam, Mouhammd Al-kasassbeh, Suleman Khan, Ahmad Al-Qerem, and Kim-Kwang [Raymond Choo]. 2020. An efficient reinforcement learning-based Botnet detection approach. Journal of Network and Computer Applications 150 (2020), 102479.

Digital Library

[5]

Saed Alrabaee, Paria Shirani, Mourad Debbabi, and Lingyu Wang. 2016. On the feasibility of malware authorship attribution. In International Symposium on Foundations and Practice of Security. Springer, 256--272.

[6]

Amar Amouri, Vishwa Alaparthy, and Salvatore Dominic Morgera. 2018. Cross layer-based intrusion detection based on network behavior for IoT. In 2018 IEEE 19th Wireless and Microwave Technology Conference (WAMICON). 1--4.

[7]

Felix Anda, Nhien-An Le-Khac, and Mark Scanlon. 2020. DeepUAge: Improving Underage Age Estimation Accuracy to Aid CSEM Investigation. Forensic Science International: Digital Investigation 32 (04 2020), 300921.

[8]

Rigan Ap-Apid. 2005. An algorithm for nudity detection. In 5th Philippine Computing Science Congress. 201--205.

[9]

Belhassen Bayar and Matthew C Stamm. 2016. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. ACM, 5--10.

Digital Library

[10]

Andrei Cristian Bechet, Robert Helbet, Iulian Bouleanu, Annamaria Sarbu, Simona Miclaus, and Paul Bechet. 2019. Low Cost Solution Based on Software Defined Radio for the RF Exposure Assessment: A Performance Analysis. In 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE). IEEE, 1--4.

[11]

Ryad Benadjila, Emmanuel Prouff, Rémi Strullu, Eleonora Cagli, and Cécile Dumas. 2018. Study of deep learning techniques for side-channel analysis and introduction to ASCAD database. ANSSI, France & CEA, LETI, MINATEC Campus, France. Online verfügbar unter https://eprint.iacr.org/2018/053.pdf, zuletzt geprüft am 22 (2018), 2018.

[12]

Anuradha D. Biradar and B. Padmavathi. 2020. BotHook: A Supervised Machine Learning Approach for Botnet Detection Using DNS Query Data. In ICCCE 2019, Amit Kumar and Stefan Mozar (Eds.). Springer Singapore, Singapore, 261--269.

[13]

Ross Brown, Binh Pham, and Olivier Vel. 2005. Design of a Digital Forensics Image Mining System. Lecture Notes in Computer Science.

Digital Library

[14]

Anna L. Buczak and Erhan Guven. 2016. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys and Tutorials 18, 2 (2016), 1153--1176.

Digital Library

[15]

Robert Callan, Farnaz Behrang, Alenka Zajic, Milos Prvulovic, and Alessandro Orso. 2016. Zero-overhead profiling via em emanations. In Proceedings of the 25th International Symposium on Software Testing and Analysis. ACM, 401--412.

Digital Library

[16]

Robert Locke Callan. 2016. Analyzing software using unintentional electromagnetic emanations from computing devices. Ph.D. Dissertation. Georgia Institute of Technology.

[17]

Brian Carrier and Eugene H Spafford. 2004. An event-based digital forensic investigation framework. In Digital forensic research workshop. 11--13.

[18]

Eoghan Casey, Sean Barnum, Ryan Griffith, Jonathan Snyder, Harm van Beek, and Alex Nelson. 2018. The evolution of expressing and exchanging cyber-investigation information in a standardized form. In Handling and Exchanging Electronic Evidence Across Europe. Springer, 43--58.

[19]

Yoan Chabot, Aurélie Bertaux, Christophe Nicolle, and M-Tahar Kechadi. 2014. A complete formalized knowledge representation model for advanced digital forensics timeline analysis. Digital Investigation 11 (2014), S95--S105.

Digital Library

[20]

Yoan Chabot, Aurélie Bertaux, Christophe Nicolle, and Tahar Kechadi. 2015. An ontology-based approach for the reconstruction and analysis of digital incidents timelines. Digital Investigation 15 (2015), 83--100.

Digital Library

[21]

Suresh Chari, Josyula R Rao, and Pankaj Rohatgi. 2002. Template attacks. In International Workshop on Cryptographic Hardware and Embedded Systems. Springer, 13--28.

[22]

Jiansheng Chen, Xiangui Kang, Ye Liu, and Z Jane Wang. 2015. Median filtering forensics based on convolutional neural networks. IEEE Signal Processing Letters 22, 11 (2015), 1849--1853.

[23]

Qian Chen, Qing Liao, Zoe L Jiang, Junbin Fang, Siuming Yiu, Guikai Xi, Rong Li, Zhengzhong Yi, Xuan Wang, Lucas CK Hui, et al. 2018. File fragment classification using grayscale image conversion and deep learning in digital forensics. In 2018 IEEE Security and Privacy Workshops (SPW). IEEE, 140--147.

[24]

Lianbing Deng, Daming Li, Xiang Yao, David Cox, and Haixiang Wang. 2018. Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Computing (31 Jan 2018).

[25]

Xiaoyu Du, Christopher Hargreaves, John Sheppard, and Mark Scanlon. 2020. TraceGen: User Activity Emulation for Digital Forensic Test Image Generation. Forensic Science International: Digital Investigation (09 2020). Proceedings of DFRWS APAC 2020.

[26]

Xiaoyu Du, Nhien-An Le-Khac, and Mark Scanlon. 2017. Evaluation of Digital Forensic Process Models with Respect to Digital Forensics as a Service. In Proceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS 2017). ACPI, Dublin, Ireland, 573--581.

[27]

Peter Eckersley. 2010. How unique is your web browser?. In International Symposium on Privacy Enhancing Technologies Symposium. Springer, 1--18.

Digital Library

[28]

Mohamed Faisal Elrawy, Ali Ismail Awad, and Hesham F. A. Hamed. 2018. Intrusion detection systems for IoT-based smart environments: a survey. Journal of Cloud Computing 7, 1 (04 Dec 2018), 21.

Digital Library

[29]

EURPOL. 2019. Global Guidelines for Digital Forensic Laboratories. https://www.interpol.int/content/download/13501/file/INTERPOL_DFL_GlobalGuidelinesDigitalForensicsLaboratory.pdf

[30]

Hany Farid. 2009. Image forgery detection. IEEE Signal processing magazine 26, 2 (2009), 16--25.

[31]

Nhien-An Le-Khac Felix Anda, David Lillis and Mark Scanlon. 2018. Evaluating Automated Facial Age Estimation Techniques for Digital Forensics. In 2018 IEEE Security and Privacy Workshops (SPW). 129--139.

[32]

Simran Fitzgerald, George Mathews, Colin Morris, and Oles Zhulyn. 2012. Using NLP techniques for file fragment classification. Digital Investigation 9 (2012), S44--S49.

[33]

Peter Flach. 2012. Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press.

Digital Library

[34]

David Freire-Obregon, Fabio Narducci, Silvio Barra, and Modesto Castrillon-Santana. 2018. Deep learning for source camera identification on mobile devices. Pattern Recognition Letters (2018).

[35]

Takeshi Fujino, Takaya Kubota, and Mitsuru Shiozaki. 2017. Tamper-resistant cryptographic hardware. IEICE Electronics Express 14, 2 (2017), 20162004--20162004.

[36]

Ekta Gandotra, Divya Bansal, and Sanjeev Sofat. 2014. Malware analysis and classification: A survey. Journal of Information Security 2014 (2014).

[37]

Simson Garfinkel, Paul Farrell, Vassil Roussev, and George Dinolt. 2009. Bringing science to digital forensics with standardized forensic corpora. digital investigation 6 (2009), S2--S11.

[38]

Simson L Garfinkel. 2007. Carving contiguous and fragmented files with fast object validation. digital investigation 4 (2007), 2--12.

[39]

Simson L Garfinkel, Aleatha Parker-Wood, Daniel Huynh, and James Migletz. 2010. An automated solution to the multiuser carved data ascription problem. IEEE Transactions on Information Forensics and Security 5, 4 (2010), 868--882.

Digital Library

[40]

Pavel Gladyshev and Ahmed Patel. 2004. Finite state machine approach to digital event reconstruction. Digital Investigation 1, 2 (2004), 130--149.

Digital Library

[41]

Cinthya Grajeda, Frank Breitinger, and Ibrahim Baggili. 2017. Availability of datasets for digital forensics-and what is missing. Digital Investigation 22 (2017), S94--S105.

Digital Library

[42]

Michał Grega, Andrzej Matiolański, Piotr Guzik, and Mikołaj Leszczuk. 2016. Automated detection of firearms and knives in a CCTV image. Sensors 16, 1 (2016), 47.

[43]

Christopher Hargreaves and Angus Marshall. 2019. SyncTriage: Using synchronisation artefacts to optimise acquisition order. Digital Investigation 28 (2019), S134--S140.

[44]

Christopher Hargreaves and Jonathan Patterson. 2012. An automated timeline reconstruction approach for digital forensic investigations. Digital Investigation 9 (2012), S69--S79.

[45]

Gabriel Hospodar, Benedikt Gierlichs, Elke De Mulder, Ingrid Verbauwhede, and Joos Vandewalle. 2011. Machine learning in side-channel analysis: a first study. Journal of Cryptographic Engineering 1, 4 (2011), 293.

[46]

Wenyi Huang and Jack W Stokes. 2016. MtNet: a multi-task neural network for dynamic malware classification. In International conference on detection of intrusions and malware, and vulnerability assessment. Springer, 399--418.

Digital Library

[47]

J. Jasmine and S. Annadurai. 2019. Real time video image enhancement approach using particle swarm optimisation technique with adaptive cumulative distribution function based histogram equalisation. Measurement 145 (2019), 833 -- 840.

[48]

Sundararaman Jeyaraman and Mikhail J Atallah. 2006. An empirical study of automatic event reconstruction systems. digital investigation 3 (2006), 108--115.

[49]

Vaibhavi Kalgutkar, Ratinder Kaur, Hugo Gonzalez, Natalia Stakhanova, and Alina Matyukhina. 2019. Code authorship attribution: Methods and challenges. ACM Computing Surveys (CSUR) 52, 1 (2019), 1--36.

Digital Library

[50]

Muhammad Naeem Ahmed Khan. 2012. Performance analysis of Bayesian networks and neural networks in classification of file system activities. Computers & Security 31, 4 (2012), 391--401.

Digital Library

[51]

Mehdi Kharrazi, Husrev T Sencar, and Nasir Memon. 2004. Blind source camera identification. In Image Processing, 2004. ICIP'04. 2004 International Conference on, Vol. 1. IEEE, 709--712.

[52]

Taesung Kim, Seungkwang Lee, Dooho Choi, and Hyunsoo Yoon. 2016. Protecting secret keys in networked devices with table encoding against power analysis attacks. Journal of High Speed Networks 22, 4 (2016), 293--307.

[53]

Paul Kocher, Joshua Jaffe, and Benjamin Jun. 1999. Differential power analysis. In Advances in Cryptology (CRYPTO '99). Springer, 789--789.

[54]

Paul Kocher, Joshua Jaffe, Benjamin Jun, and Pankaj Rohatgi. 2011. Introduction to differential power analysis. Journal of Cryptographic Engineering 1, 1 (2011), 5--27.

[55]

Michael Donovan Kohn, Mariki M Eloff, and Jan HP Eloff. 2013. Integrated digital forensic process model. Computers & Security 38 (2013), 103--115.

Digital Library

[56]

Bojan Kolosnjaji, Apostolis Zarras, George Webster, and Claudia Eckert. 2016. Deep learning for classification of malware system call sequences. In Australasian Joint Conference on Artificial Intelligence. Springer, 137--149.

[57]

Gierad Laput, Chouchang Yang, Robert Xiao, Alanson Sample, and Chris Harrison. 2015. Em-sense: Touch recognition of uninstrumented, electrical and electromechanical objects. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology. ACM, 157--166.

Digital Library

[58]

Arash Habibi Lashkari, Gerard Draper-Gil, Mohammad Saiful Islam Mamun, and Ali A. Ghorbani. 2017. Characterization of Tor Traffic Using Time Based Features. In In the proceeding of the 3rd International Conference on Information System Security and Privacy, SCITEPRESS (Portugal).

[59]

Quan Le, Oisín Boydell, Brian Mac Namee, and Mark Scanlon. 2018. Deep learning at the shallow end: Malware classification for non-domain experts. Digital Investigation 26 (2018), S118--S126.

Digital Library

[60]

Thanh-Ha Le, Jessy Clédière, Christine Serviere, and Jean-Louis Lacoume. 2007. Efficient solution for misalignment of signal in side channel analysis. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07, Vol. 2. IEEE, II--257.

[61]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436--444.

[62]

Liran Lerman, Gianluca Bontempi, and Olivier Markowitch. 2011. Side channel attack: an approach based on machine learning. In Proceedings of 2nd International Workshop on Constructive Side-Channel Analysis and Security Design (COSADE). Schindler and Huss, 29--41.

[63]

Stefan Lessmann, Bart Baesens, Hsin-Vonn Seow, and Lyn C Thomas. 2015. Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research 247, 1 (2015), 124--136.

[64]

David Lillis, Brett Becker, Tadhg O'Sullivan, and Mark Scanlon. 2016. Current Challenges and Future Research Areas for Digital Forensic Investigation. In The 11th ADFSL Conference on Digital Forensics, Security and Law (CDFSL 2016). ADFSL, Daytona Beach, FL, USA, 9--20.

[65]

Liqun Liu, Bing Xu, Xiaoping Zhang, and Xianjun Wu. 2018. An intrusion detection method for internet of things based on suppressed fuzzy clustering. EURASIP Journal on Wireless Communications and Networking (2018).

[66]

Jan Lukas, Jessica Fridrich, and Miroslav Goljan. 2006. Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security 1, 2 (2006), 205--214.

Digital Library

[67]

Alison D MacEachern, Divya Jindal-Snape, and Sharon Jackson. 2011. Child abuse investigation: police officers and secondary traumatic stress. International journal of occupational safety and ergonomics 17, 4 (2011), 329--339.

[68]

José Raúl Machado-Fernández. 2015. Software defined radio: Basic principles and applications. Revista Facultad de Ingeniería 24, 38 (2015), 79--96.

[69]

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017).

[70]

Houssem Maghrebi, Thibault Portigliatti, and Emmanuel Prouff. 2016. Breaking cryptographic implementations using deep learning techniques. In International Conference on Security, Privacy, and Applied Cryptography Engineering. Springer, 3--26.

[71]

Andrew Marrington, Ibrahim Baggili, George Mohay, and Andrew Clark. 2011. CAT Detect (Computer Activity Timeline Detection): A tool for detecting inconsistency in computer activity timelines. digital investigation 8 (2011), S52--S61.

[72]

Fabio Marturana, Gianluigi Me, Rosamaria Berte, and Simone Tacconi. 2011. A quantitative approach to triaging in mobile forensics. In 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications. IEEE, 582--588.

Digital Library

[73]

Fabio Marturana and Simone Tacconi. 2013. A Machine Learning-based Triage methodology for automated categorization of digital media. Digital Investigation 10, 2 (2013), 193--204.

Digital Library

[74]

Loïc Masure, Cécile Dumas, and Emmanuel Prouff. 2020. A comprehensive study of deep learning for side-channel analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems (2020), 348--375.

[75]

Christian A Meissner and Saul M Kassin. 2002. "He's guilty!": Investigator bias in judgments of truth and deception. Law and human behavior 26, 5 (2002), 469--480.

[76]

Chris R. Chatwin Muhammad Naeem Khan and Rupert CD Young. 2007. A framework for post-event timeline reconstruction using neural networks. digital investigation 4, 3-4 (2007), 146--157.

[77]

Hiran V Nath and Babu M Mehtre. 2014. Static malware analysis using machine learning methods. In International Conference on Security in Computer Networks and Distributed Systems. Springer, 440--450.

[78]

Mohammed Murtaz Amir Naviq, Hassan Azwar, Syed Baqir Ali, and Saad Rehman. 2018. A framework for Android Malware detection and classification. In 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS). 1--5.

[79]

Alireza Nazari, Nader Sehatbakhsh, Monjur Alam, Alenka Zajic, and Milos Prvulovic. 2017. EDDIE: EM-Based Detection of Deviations in Program Execution. In Proceedings of the 44th Annual International Symposium on Computer Architecture. ACM, 333--346.

Digital Library

[80]

Fudong Nian, Teng Li, Yan Wang, Mingliang Xu, and Jun Wu. 2016. Pornographic image detection utilizing deep convolutional neural networks. Neurocomputing 210 (2016), 283 -- 293. SI:Behavior Analysis In SN.

Digital Library

[81]

Jiankun Hu Nour Moustafa and Jill Slay. 2019. A holistic review of Network Anomaly Detection Systems: A comprehensive survey. J. Network and Computer Applications 128 (2019), 33--55.

[82]

Seong Joon Oh, Bernt Schiele, and Mario Fritz. 2019. Towards reverse-engineering black-box neural networks. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer, 121--144.

[83]

Morteza Safaei Pour, Elias Bou-Harb, Kavita Varma, Nataliia Neshenko, Dimitris A. Pados, and Kim-Kwang Raymond Choo. 2019. Comprehending the IoT cyber threat landscape: A data dimensionality reduction technique to infer and characterize Internet-scale IoT probing campaigns. Digital Investigation 28 (2019), S40 -- S49.

[84]

Yuan Rao and Jiangqun Ni. 2016. A deep learning approach to detection of splicing and copy-move forgeries in images. In Information Forensics and Security (WIFS), 2016 IEEE International Workshop on. IEEE, 1--6.

[85]

Chiadighikaobi Ikenna Rene and Johari Abdullah. 2017. Malicious Code Intrusion Detection using Machine Learning And Indicators of Compromise. International Journal of Computer Science and Information Security (IJCSIS) 15, 9 (September 2017).

[86]

Ronald L Rivest. 1991. Cryptography and machine learning. In International Conference on the Theory and Application of Cryptology. Springer, 427--439.

[87]

Anderson Rocha, Walter J Scheirer, Christopher W Forstall, Thiago Cavalcante, Antonio Theophilo, Bingyu Shen, Ariadne RB Carvalho, and Efstathios Stamatatos. 2016. Authorship attribution for social media forensics. IEEE Transactions on Information Forensics and Security 12, 1 (2016), 5--33.

Digital Library

[88]

Marcus K Rogers, James Goldman, Rick Mislan, Timothy Wedge, and Steve Debrota. 2006. Computer forensics field triage process model. Journal of Digital Forensics, Security and Law 1, 2 (2006), 2.

[89]

Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016).

[90]

Laura Sanchez, Cinthya Grajeda, Ibrahim Baggili, and Cory Hall. 2019. A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, & Safer Presentation for Investigating Child Sexual Abuse Material (CSAM). Digital Investigation 29 (2019), S124--S142.

Digital Library

[91]

Hendra Saputra, Narayanan Vijaykrishnan, M Kandemir, Mary Jane Irwin, R Brooks, Soontae Kim, and Wei Zhang. 2003. Masking the energy behavior of DES encryption. In Proceedings of the conference on Design, Automation and Test in Europe-Volume 1. IEEE Computer Society, 10084.

Digital Library

[92]

Fadl Mutaher Ba-Alwi Saud Mohammed Othman, Nabeel T Alsohybe and Ammar Thabit Zahary. 2018. Survey on Intrusion Detection System. International Journal of Cyber-Security and Digital Forensics (IJCSDF) (December 2018).

[93]

Asanka Sayakkara, Nhien-An Le-Khac, and Mark Scanlon. 2019. A survey of electromagnetic side-channel attacks and discussion on their case-progressing potential for digital forensics. Digital Investigation (2019).

[94]

Mark Scanlon. 2016. Battling the digital forensic backlog through data deduplication. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH). IEEE, 10--14.

[95]

Johannes Schneider and Frank Breitinger. 2020. AI Forensics: Did the Artificial Intelligence System Do It? Why? (2020).

[96]

Husrev T Sencar and Nasir Memon. 2009. Overview of state-of-the-art in digital image forensics. In Algorithms, Architectures and Information Systems Security. World Scientific, 325--347.

[97]

Shai Shalev-Shwartz and Shai Ben-David. 2014. Understanding machine learning: From theory to algorithms. Cambridge university press.

Digital Library

[98]

Devashish Shankar, Sujay Narumanchi, HA Ananya, Pramod Kompalli, and Krishnendu Chaudhury. 2017. Deep learning based large scale visual recommendation and search for e-commerce. arXiv preprint arXiv:1703.02344 (2017).

[99]

Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani. 2018. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. In 4th International Conference on Information Systems Security and Privacy (ICISSP) (Portugal).

[100]

Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani. 2019. Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy. In IEEE 53rd International Carnahan Conference on Security Technology (India).

[101]

Manmeet Singh, Maninder Singh, and Sanmeet Kaur. 2019. Detecting bot-infected machines using DNS fingerprinting. Digital Investigation 28 (2019), 14 -- 33.

[102]

Raphael Spreitzer, Veelasha Moonsamy, Thomas Korak, and Stefan Mangard. 2018. Systematic classification of side-channel attacks: a case study for mobile devices. IEEE Communications Surveys & Tutorials 20, 1 (2018), 465--488.

[103]

Barron Stone and Samuel Stone. 2016. Comparison of Radio Frequency Based Techniques for Device Discrimination and Operation Identification. In 11th International Conference on Cyber Warfare and Security: ICCWS2016. Academic Conferences and Publishing Limited, 475.

[104]

Hudan Studiawan, Ferdous Sohel, and Christian Payne. 2020. Sentiment Analysis in a Forensic Timeline with Deep Learning. IEEE Access (2020).

[105]

Abby Stylianou, Jessica Schreier, Richard Souvenir, and Robert Pless. 2017. Traffickcam: Crowdsourced and computer vision based approaches to fighting sex trafficking. In 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE, 1--8.

[106]

Laya Taheri, Andi Fitriah Abdul Kadir, and Arash Habibi Lashkari. 2019. Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls. In 2019 International Carnahan Conference on Security Technology (ICCST). 1--8.

[107]

Qizhi Tian and Sorin A Huss. 2012. On clock frequency effects in side channel attacks of symmetric block ciphers. In 2012 5th International Conference on New Technologies, Mobility and Security (NTMS). IEEE, 1--5.

[108]

Min Jen Tsai, Cheng Liang Lai, and Jung Liu. 2007. Camera/mobile phone source identification for digital forensics. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2 (2007), 221--224.

[109]

Benjamin Turnbull and Suneel Randhawa. 2015. Automated event and social network extraction from digital evidence sources with ontological mapping. Digital Investigation 13 (2015), 94--106.

Digital Library

[110]

Serpil Ustebay, Zeynep Turgutand, and Muhammed Ali Aydin. 2018. Intrusion Detection System with Recursive Feature Elimination by Using Random Forest and Deep Learning Classifier. In 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT). 71--76.

[111]

Michael Veale, Reuben Binns, and Lilian Edwards. 2018. Algorithms that remember: model inversion attacks and data protection law. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, 2133 (2018), 20180083.

[112]

Vladimír Veselý and Martin Žádník. 2019. How to detect cryptocurrency miners? By traffic forensics! Digital Investigation 31, 31 (2019), 1--25.

[113]

R. Vinayakumar, Mamoun Alazab, K. P. Soman, Prabaharan Poornachandran, Ameer Al-Nemrat, and Sitalakshmi Venkatraman. 2019. Deep Learning Approach for Intelligent Intrusion Detection System. IEEE Access 7 (2019), 41525--41550.

[114]

Eva A Vincze. 2016. Challenges in digital forensics. Police Practice and Research 17, 2 (2016), 183--194.

[115]

Kristijan Vulinović, Lucija Ivković, Juraj Petrović, Kristian Skračić, and Predrag Pale. 2019. Neural Networks for File Fragment Classification. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 1194--1198.

[116]

Xinxi Wang and Ye Wang. 2014. Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the 22nd ACM international conference on Multimedia. 627--636.

Digital Library

[117]

Xiao Wang, Quan Zhou, Jacob Harer, Gavin Brown, Shangran Qiu, Zhi Dou, John Wang, Alan Hinton, Carlos Aguayo Gonzalez, and Peter Chin. 2018. Deep learning-based classification and anomaly detection of side-channel signals. In Cyber Sensing 2018, Vol. 10630. International Society for Optics and Photonics, 1063006.

[118]

Janis Wolak and Kimberly J Mitchell. 2009. Work exposure to child pornography in ICAC task forces and affiliates. Retrieved from Crimes against Children Research Center: http://www.unh.edu/ccrc/pdf/Law%20Enforcement%20Work%20Exposure%20to%20CP.pdf (2009).

[119]

Jianyu Xiao, Shancang Li, and Qingliang Xu. 2019. Video-based evidence analysis and extraction in digital forensic investigation. IEEE Access 7 (2019), 55432--55442.

[120]

Xitong Yang and Jiebo Luo. 2017. Tracking Illicit Drug Dealing and Abuse on Instagram Using Multimodal Analysis. ACM Trans. Intell. Syst. Technol. 8, 4, Article 58 (Feb. 2017), 15 pages.

Digital Library

[121]

Dawei Yin, Yuening Hu, Jiliang Tang, Tim Daly, Mianwei Zhou, Hua Ouyang, Jianhui Chen, Changsung Kang, Hongbo Deng, Chikashi Nobata, et al. 2016. Ranking relevance in yahoo search. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 323--332.

Digital Library

[122]

Andreas Zankl, Hermann Seuschek, Gorka Irazoqui, and Berk Gulmezoglu. 2018. Side-Channel Attacks in the Internet of Things: Threats and Challenges. In Solutions for Cyber-Physical Systems Ubiquity. IGI Global, 325--357.

[123]

Ying Zhang, Jonathan Goh, Lei Lei Win, and Vrizlynn LL Thing. 2016. Image Region Forgery Detection: A Deep Learning Approach. SG-CRC 2016 (2016), 1--11.

[124]

Yuanyuan Zhou and François-Xavier Standaert. 2019. Deep learning mitigates but does not annihilate the need of aligned traces and a generalized resnet model for side-channel attacks. Journal of Cryptographic Engineering (2019), 1--11.

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ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security

August 2020

1073 pages

ISBN:9781450388337

DOI:10.1145/3407023

Program Chairs:
Melanie Volkamer
Karlsruhe Institute of Technologie (KIT), Competence Center for Applied Security Technology (KASTEL)
,
Christian Wressnegger
Karlsruhe Institute of Technologie (KIT), Competence Center for Applied Security Technology (KASTEL)

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 25 August 2020

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ARES 2020

ARES 2020: The 15th International Conference on Availability, Reliability and Security

August 25 - 28, 2020

Virtual Event, Ireland

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Overall Acceptance Rate 228 of 451 submissions, 51%

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https://doi.org/10.4018/978-1-6684-6361-1.ch015
Puchalski DPawlicki MKozik RRenk RChoraś M(2024)Trustworthy AI-based Cyber-Attack Detector for Network Cyber Crime ForensicsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3670880(1-8)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3670880
Wickramasekara AScanlon M(2024)A Framework for Integrated Digital Forensic Investigation Employing AutoGen AI Agents2024 12th International Symposium on Digital Forensics and Security (ISDFS)10.1109/ISDFS60797.2024.10527235(01-06)Online publication date: 29-Apr-2024
https://doi.org/10.1109/ISDFS60797.2024.10527235
Dwivedi ASingh UKathuria SLande JNegi P(2024)Mobile Forensics in Digital World using Artificial Intelligence2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL)10.1109/ICSADL61749.2024.00051(278-283)Online publication date: 13-Mar-2024
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Nikolakopoulos AEvangelatos SVeroni EChasapas KGousetis NApostolaras ANikolopoulos CKorakis T(2024)Large Language Models in Modern Forensic Investigations: Harnessing the Power of Generative Artificial Intelligence in Crime Resolution and Suspect Identification2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE)10.1109/EEITE61750.2024.10654427(1-5)Online publication date: 29-May-2024
https://doi.org/10.1109/EEITE61750.2024.10654427
Kanta ACoisel IScanlon M(2024)A comprehensive evaluation on the benefits of context based password cracking for digital forensicsJournal of Information Security and Applications10.1016/j.jisa.2024.10380984:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.jisa.2024.103809
Bellizzi JLosiouk EConti MColombo CVella M(2023)VEDRANDO: A Novel Way to Reveal Stealthy Attack Steps on Android through Memory ForensicsJournal of Cybersecurity and Privacy10.3390/jcp30300193:3(364-395)Online publication date: 10-Jul-2023
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Javed AAhmed WPandya SMaddikunta PAlazab MGadekallu T(2023)A Survey of Explainable Artificial Intelligence for Smart CitiesElectronics10.3390/electronics1204102012:4(1020)Online publication date: 18-Feb-2023
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Tageldin LVenter H(2023)Machine-Learning Forensics: State of the Art in the Use of Machine-Learning Techniques for Digital Forensic Investigations within Smart EnvironmentsApplied Sciences10.3390/app13181016913:18(10169)Online publication date: 10-Sep-2023
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Hildebrandt JSchomakers EZiefle MCalero Valdez A(2023)Understanding indirect users' privacy concerns in mobile forensics — A mixed method conjoint approachFrontiers in Computer Science10.3389/fcomp.2023.9721865Online publication date: 13-Jul-2023
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