Polychronicity tendency-based online behavioral signature

Abstract

The proliferation of ubiquitous and pervasive computing devices has led to the emergence of research areas like Internet of things, and the Big-Data, which has seen a rise in obfuscation of online identity thus fueling an increase in online anonymity. Online anonymity constitutes a major platform for the exploitation of the potentials of cyber-crime; at the same time, it also inhibits the potential economic power that can be harnessed from the surging Internet population. Methods of online identification, such as usage profiling, demographic profiling, cookie-based identification process, media fingerprinting as well as token-based identification processes, are limited to either system identification or oneto-one identification. Current one-to-one identification mechanisms require huge volumes of templates of known users, and cannot be applied to novel users. This study proposed a psychosocial approach that integrates the human Polyphasia tendency into online identification processes for a one-to-many identification process. To achieve this, the study administered a Polychronic-Monochronic tendency scale measurement instrument to staff members of a research unit in a university, and the server-side network traffic of each respondent was monitored and collected. A logistic model tree was adapted for the one-to-many classification model based on human intrinsic features extracted from the network traffic and Polyphasia dichotomy. High degree of reliable accuracy of >80% was achieved which suggests a reliable model that supports the underlying hypothesis of the proposed model. Based on this accuracy, the approach finds practical relevance in online profiling process for online identification as well as online demographic profiling for e-commerce and e-learning. Furthermore, this approach can be applied to improve recommender systems in areas such as prediction and profile delivery through the extraction of the purpose of online surfing.

Cyberbullying Indicator as a Precursor to a Cyber Construct Development

“…Different dimensions of cyberbullying acts have been identified in the literature (Langos, 2012). Cyber personality has also been defined in existing studies (Adeyemi et al, 2016;Adeyemi, Abd Razak, et al, 2017;Adeyemi, Razak and Salleh, 2017;Ikuesan et al, 2019) with contradictory cyber:physical personalities (Gardini, Cloninger and Venneri, 2009;Mohammadi and Vinciarelli, 2015;Ikuesan, Abd Razak and Salleh, 2016). The next section explores the different dimensions of cyberbullying with a view to highlighting the underlying commonalities.…”

A heuristics for HTTP traffic identification in measuring user dissimilarity

“…Research on user identification in areas of behavioral inference (Fan et al 2014;Yang 2010;Adeyemi et al 2014), biometric dynamics (Ernsberger et al 2017;Ikuesan and Venter 2018;Ikuesan et al 2019), and network traffic analysis (Li et al 2013a;Melnikov and Schönwälder 2010a;Adeyemi et al 2016) are methods adapted for user identification through pattern extraction. The process employed for network traffic pattern extraction includes logs and media scavenging, mining of audit trails, client-side caching, and extraction of flow records from captured network traffic.…”