Identifying Forensic Interesting Files in Digital Forensic Corpora by Applying Topic Modelling
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the cyber forensics is an emerging area, where the culprits in a cyber-attack are identified. to perform an investigation, investigator needs to identify the device, backup the data and perform analysis. therefore, as the cybercrimes increase, so the seized devices and its data also increase, and due to the massive amount of data, the investigations are delayed significantly. till today many of the forensic investigators use regular expressions and keyword search to find the evidences, which is a traditional approach. in traditional analysis, when the query is given, only exact searches that are matched to particular query are shown while disregarding the other results. therefore, the main disadvantage with this is that, some sensitive files may not be shown while queried, and also additionally, all the data must be indexed before performing the query which takes huge manual effort as well as time. to overcome this, this research proposes two-tier forensic framework that introduced topical modelling to identify the latent topics and words. existing approaches used latent semantic indexing (lsi) that has synonymy problem. to overcome this, this research introduces latent semantic analysis (lsa) to digital forensics field and applies it on author’s corpora which contain 29.8 million files. interestingly, this research yielded satisfactory results in terms of time and in finding uninteresting as well as interesting files. this paper also gives fair comparison among forensic search techniques in digital corpora and proves that the proposed methodology performance outstands.
KeywordsDisc forensics Uninteresting files Interesting files Latent semantic analysis Topical modelling
- 1.Raghavan S (2013) Digital forensic research: current state of the art. CSI Trans ICT 1(1):91–114.
- 2.Beebe N (2009) Digital forensic research: the good, the bad and the unaddressed. In: Advances in digital forensics V, pp 17–36
- 3.Rogers MK, Seigfried K (2004) The future of computer forensics: a needs analysis survey. Comput Secur 23(1):12–16
- 4.Joseph P, Norman J (2019) An analysis of digital forensics in cyber security. In: First international conference on artificial intelligence and cognitive computing, vol 815, pp 0–7
- 5.Bem D, Feld F, Huebner E, Bem O (2008) Computer forensics—past, present and future. J Inf Sci Technol 5(3):43–59
- 6.Peterson G (2015) Digital Forensics XI. In: Peterson G, Shenoi S (eds) Advances in digital forensics XI 11th. Springer, Orlando, pp 74–89
- 7.Amari K (2009) Techniques and tools for recovering and analyzing data from volatile memory. Boston
- 8.Regional Computer Forensics Laboratory (2016) FBI Fiscal annual report. Mexico. Retrieved from
- 9.Pratap Singh S (2016) Crime in India 2016. New Delhi, India. Retrieved from
- 10.Papadimitriou H, Berkeley UC (1998) Latent semantic indexing: analysis. In: Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, pp 159–168
- 11.Olmos R, León JA, Jorge-Botana G, Escudero I (2009) An introduction to latent semantic analysis. Behav Res Methods 41(3):944–950
- 12.Landauer TK, Foltz PW, Laham D (2009) An introduction to latent semantic analysis. Discourse Process 25(2–3):259–284
- 13.Landauer TK, Dumais ST, A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge
- 14.Joseph P, Norman J (2019) Forensic corpus data reduction techniques for faster analysis by eliminating tedious files. Inf Secur J 28(4–5):136–147.
- 15.Bird S, Loper E, Klein E (2009) Natural language processing with python. O’Reilly Media Inc.
- 16.Garfinkel SL (2006) Forensic feature extraction and cross-drive analysis. Digit Investig 3:71–81
- 17.Trefethern L, Bau D III (1997) Numerical linear algebra, vol 102. Soceity for Industrial and Applied Mathematics, Philadelphia