Abstract

The illegal activities using mobile phones, computers, and the internet are rising, including pornography, online prostitution, identity theft, phishing, sniffing or snooping attacks, spamming or malware attacks. Internet crime or cyber-attacks play a pivotal role in impacting the system since we started using the internet. In this era, of digital world crimes are increasing at par. The advancement in the technology for detection of these crimes has revolutionary affected the forensic field. Starting from, the detection of digital crimes from small scale like Email Bombing to Denial of Services (DOS) at large scale. Furthermore, the sensitivity of detection is quite decisive hence, it is cardinal responsibility of forensic experts to investigate these types of crimes critically. An attempt has been made in this paper to percolate the significance of digital crimes, cybercrimes, and audio forensics. Additionally, it also focus the investigative tools and techniques for such crime. key messages: This paper elaborate the key features for investigation of Digital crimes, cybercrimes & Audio Forensics.

• 1.   Costello S. How many iPhones have been sold worldwide? [Internet]. https://www.google.com/search?q=About.com; 2015 Mar [cited 2015 Jun 10].
• 2.   Statistic Brain Research Institute. Android phone statistics [Internet]. Statistic Brain; 2015 Mar [cited 2015 Jun 20
• 3.   Page C. Windows Phone market share tumbles almost 10 percent as Lumia sales dry up [Internet]. The Inquirer; 2015 [cited 2015 Jun 20
• 4.   Statista. Number of monthly active WhatsApp users worldwide from April 2013 to April 2015 [Internet]. Statista; 2015 Apr [cited 2015 Jun 20].
• 5.   Ibrahim M. How to decrypt WhatsApp crypt7 database messages [Internet]. Digital Internals; 2015 [cited 2015 Jun 20].
• 6.   Hong J. The state of phishing attacks. Commun ACM. 2012;55(1):74-81.
• 7.   Vidas T, Owusu E, Wang S, et al. The susceptibility of smartphone users to QR code phishing attacks. In: International Conference on Financial Cryptography and Data Security; 2013 Apr; Berlin, Heidelberg. Springer; 2013. p. 52-69.
• 8.   Jakobsson M, Myers S. Phishing and countermeasures: understanding the increasing problem of electronic identity theft. Hoboken (NJ): John Wiley & Sons; 2006.
• 9.   McRae C, Vaughn R. Fighting the phisher: using web bugs and honeytokens to investigate the source of phishing attacks. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences; 2007 Jan 3-6; Waikoloa, HI. IEEE; 2007. p. 1–7.
• 10.   Li S, Schmitz R. A novel anti-phishing framework based on honeypots. In: eCrime Researchers Summit; 2009 Oct 20-21; Tacoma, WA. IEEE; 2009. p. 1–13.
• 11.   Mahmood A, Rajamani L. APD: ARM deceptive phishing detector system - phishing detection in instant messengers using data mining approach. Springer-Verlag Berlin Heidelberg. 2012;269:490-502.
• 12.   Dadkhah M, Lyashenko VV, et al. Methodology of wavelet analysis in research of dynamics of phishing attacks. Int J Adv Intell Paradigms. 2019;12(3-4):220-38.
• 13.   Alto P. HP networking communication: Open SSL vulnerabilities. Hewlett-Packard Development Company, White Paper. 2014:1–4.
• 14.   San Martino A, Perramon X. Phishing secrets: history, effects, and countermeasures. Int J Netw Secur. 2010;11(3):163–71.
• 15.   Arachchilage N, Hameed M. Integrating self-efficacy into a gamified approach to thwart phishing attacks. arXiv preprint arXiv:1706.07748. 2017.
• 16.   Ucci D, Aniello L, Baldoni R. Survey on the usage of machine learning techniques for malware analysis. arXiv preprint arXiv:1710.08189. 2017:1-67.
• 17.   Souri A, Hosseini R. A state-of-the-art survey of malware detection approaches using data mining techniques. Hum Centric Comput Inf Sci. 2018;8(1):1-22.
• 18.   Allix K, Jérome Q, Bissyandé TF, et al. A forensic analysis of Android malware--how is malware written and how it could be detected? In: 2014 IEEE 38th Annual Computer Software and Applications Conference; 2014 Jul 21-25; Vasteras, Sweden. IEEE; 2014. p. 384-93.
• 19.   Casey E, Turnbull B. Digital evidence on mobile devices. In: Digital Evidence and Computer Crime. 3rd ed. 2011. p. 1-44.
• 20.   Faiz M, Umar R, Yudhana A. Analisis live forensics untuk perbandingan kemananan email pada sistem operasi proprietary. ILKOM Jurnal Ilmiah. 2016;8(3):242-7.
• 21.   Kostopoulos C, Samaras S. Mobile phone identification using recorded speech signals. In: 19th International Conference on Digital Signal Processing; 2014 Aug 20-23; Hong Kong. IEEE; 2014. p. 586-91.
• 22.   Garcia-Romero D, Espy-Wilson CY. Automatic acquisition device identification from speech recordings. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing; 2010 Mar 14-19; Dallas, TX. IEEE; 2010. p. 1806-9.
• 23.   Hanilci C, Ertas F, Ertas T, Eskidere Ö. Recognition of brand and models of cell phones from recorded speech signals. IEEE Trans Inf Forensics Secur. 2011;7(2):625-34.
• 24.   Ali Z, Imran M, Alsulaiman M. An automatic digital audio authentication/forensics system. IEEE Access. 2017;5:2994-3007.
• 25.   Price M, Glass J, Chandrakasan A. A low-power speech recognizer and voice activity detector using deep neural networks. IEEE J Solid-State Circuits. 2018;51(1):66–75.