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Mobile devices have become an indispensable part of modern society and are used throughout the world on a daily basis. The proliferation of such devices has rendered them a crucial part of criminal investigations and has led to the rapid advancement of the scientific field of Mobile Forensics. The forensic examination of mobile devices provides essential information for authorities in the investigation of cases and their relative importance advances as more evidence and traces of criminal activity can be acquired through the analysis of the corresponding forensic artifacts. Data related to the device user, call logs, text messages, contacts, image and video files, notes, communication records, networking activity and application related data, among others, with correct technical interpretation and correlation through expert analysis, can significantly contribute to the successful completion of digital criminal investigations. The above underline the necessity for advanced forensic tools that will utilize the most prominent achievements in Data Science. In this paper, the current status of Mobile Forensics as a branch of Digital Forensics is examined by exploring the most important challenges that digital forensic examiners face and investigating whether Artificial Intelligence and Machine Learning solutions can revolutionize the daily practice with respect to digital forensics investigations. The utilization of these emerging technologies provides crucial tools and enhances the professional expertise of digital forensic scientists, paving the way to overcome the critical challenges of digital criminal investigations. [ABSTRACT FROM AUTHOR]

Mobile devices have become an indispensable part of modern society and are used throughout the world on a daily basis. The proliferation of such devices has rendered them a crucial part of criminal investigations and has led to the rapid advancement of the scientific field of Mobile Forensics. The forensic examination of mobile devices provides essential information for authorities in the investigation of cases and their relative importance advances as more evidence and traces of criminal activity can be acquired through the analysis of the corresponding forensic artifacts. Data related to the device user, call logs, text messages, contacts, image and video files, notes, communication records, networking activity and application related data, among others, with correct technical interpretation and correlation through expert analysis, can significantly contribute to the successful completion of digital criminal investigations. The above underline the necessity for advanced forensic tools that will utilize the most prominent achievements in Data Science. In this paper, the current status of Mobile Forensics as a branch of Digital Forensics is examined by exploring the most important challenges that digital forensic examiners face and investigating whether Artificial Intelligence and Machine Learning solutions can revolutionize the daily practice with respect to digital forensics investigations. The utilization of these emerging technologies provides crucial tools and enhances the professional expertise of digital forensic scientists, paving the way to overcome the critical challenges of digital criminal investigations. [ABSTRACT FROM AUTHOR]

An alternative technique based on chaotic encryption and conventional cryptography is presented for providing security in two levels.

The nonlinear span of a sequence x is defined as the length of the shortest feedback shift register that generates x. In this correspondence, we solve the problem of designing periodic binary sequences with given linear complexity and maximal possible nonlinear span. We concentrate on the case of sequences with period N = [2.sup.n] - 1 and we introduce two construction methods. Index Terms--Binary periodic sequence, linear complexity, maximal nonlinear span, nonlinear feedback shift register.

The problem of finding the shortest feedback shift register, with quadratic feedback function that generates a given finite-length sequence is considered. An algorithm for the determination of the quadratic span and the feedback function, which takes advantage of the special block structure of the associated system of linear equations, is proposed. Index Terms--Binary sequences, feedback functions, linear span, quadratic span, shift registers.

The problem of finding the length of a shortest feedback shift register that generates a given finite-length sequence is considered. An efficient algorithm for the determination of the span is proposed, that takes advantage of the special block structure of the associated system of linear equations. The span distribution of finite-length binary sequences is also studied. Index Terms--Binary sequences, nonlinear feedback functions, shift registers, span.

The determination of the minimum linear span sequence that differs from a given binary sequence, of period N = [2.sup.n] - 1, by at most one digit Is discussed and three methods are presented: the sequential divisions method, the congruential equations method, and the phase synchronization method. High-level algorithm organizations are provided. Finally, guidelines on sequence characterization and design via the notion of robustness are given. Index Terms--Binary sequences, linear span, sequence approximation, single-error linear span profile, weight complexity.