title: Blind source computer device identification from recorded VoIP calls for forensic investigation.
abstract: The VoIP services provide fertile ground for criminal activity, thus identifying the transmitting computer devices from recorded VoIP call may help the forensic investigator to reveal useful information. It also proves the authenticity of the call recording submitted to the court as evidence. This paper extended the previous study on the use of recorded VoIP call for blind source computer device identification. Although initial results were promising but theoretical reasoning for this is yet to be found. The study suggested computing entropy of mel-frequency cepstrum coefficients (entropy-MFCC) from near-silent segments as an intrinsic feature set that captures the device response function due to the tolerances in the electronic components of individual computer devices. By applying the supervised learning techniques of naïve Bayesian, linear logistic regression, neural networks and support vector machines to the entropy-MFCC features, state-of-the-art identification accuracy of near 99.9% has been achieved on different sets of computer devices for both call recording and microphone recording scenarios. Furthermore, unsupervised learning techniques, including simple k-means, expectation-maximization and density-based spatial clustering of applications with noise (DBSCAN) provided promising results for call recording dataset by assigning the majority of instances to their correct clusters.
authors.affiliation: Center of HELP CAT Information Technology Programmes, HELP College of Arts and Technology, Level 5, Kompleks Metro Pudu, Fraser Business Park, 55100 Kuala Lumpur, Malaysia. Electronic address: mehdi.jahanirad@helpcat.edu.my.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Jahanirad
authors.affiliation: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: badrul@um.edu.my.
authors.forename: Nor Badrul
authors.initials: NB
authors.lastname: Anuar
authors.affiliation: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: ainuddin@um.edu.my.
authors.forename: Ainuddin Wahid Abdul
authors.initials: AWA
authors.lastname: Wahab
mesh_terms: D000465:Algorithms; D003195:Computer Communication Networks; D044707:Forensic Sciences; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D016571:Neural Networks, Computer; D010699:Phonation; D060388:Support Vector Machine
keywords: Audio acoustic features; Audio forensics; Audio source device attribution; Forensic categorization of digital devices