arXiv subjects

Anagi Gamachchi

Publications and source records attributed to Anagi Gamachchi.

3 recordsLinked to original sources

arXiv2026

Marginal Gains or Meaningful Progress? Exploring Tech Tuber Narratives on Annual Smartphone Innovation

Smartphone manufacturers continue to release new models annually, yet the pace of meaningful innovation has slowed, with most changes limited to incremental updates in design, performance, or software. This study examines whether such updates deliver tangible user benefits, as perceived by expert reviewers. Using a grounded theory approach, guided by Rogers Diffusion of Innovation (DOI) framework, the research analyses reviewer discourse from 2021 2025 across three technology commentators. The analysis identifies three interrelated processes sustaining perceptions of innovation: innovation displacement, capability utility divergence, and market complacency cycles. While some improvements are acknowledged, such as refined aesthetics or extended software support; they are seldom judged sufficient to justify annual releases. These findings highlight a growing disconnect between industry narratives of innovation and expert evaluations of value, raising questions about the strategic and environmental legitimacy of frequent upgrades. The study contributes to debates on responsible innovation, perceived value, and sustainable technology consumption.

cs.ET↗

arXiv2018

A Graph Based Framework for Malicious Insider Threat Detection

While most security projects have focused on fending off attacks coming from outside the organizational boundaries, a real threat has arisen from the people who are inside those perimeter protections. Insider threats have shown their power by hugely affecting national security, financial stability, and the privacy of many thousands of people. What is in the news is the tip of the iceberg, with much more going on under the radar, and some threats never being detected. We propose a hybrid framework based on graphical analysis and anomaly detection approaches, to combat this severe cybersecurity threat. Our framework analyzes heterogeneous data in isolating possible malicious users hiding behind others. Empirical results reveal this framework to be effective in distinguishing the majority of users who demonstrate typical behavior from the minority of users who show suspicious behavior.

cs.CR↗

arXiv2018

Insider Threat Detection Through Attributed Graph Clustering

While most organizations continue to invest in traditional network defences, a formidable security challenge has been brewing within their own boundaries. Malicious insiders with privileged access in the guise of a trusted source have carried out many attacks causing far-reaching damage to financial stability, national security and brand reputation for both public and private sector organizations. Growing exposure and impact of the whistleblower community and concerns about job security with changing organizational dynamics has further aggravated this situation. The unpredictability of malicious attackers, as well as the complexity of malicious actions, necessitates the careful analysis of network, system and user parameters correlated with the insider threat problem. Thus it creates a high dimensional, heterogeneous data analysis problem in isolating suspicious users. This research work proposes an insider threat detection framework, which utilizes the attributed graph clustering techniques and outlier ranking mechanism for enterprise users. Empirical results also confirm the effectiveness of the method by achieving the best area under the curve value of 0.7648 for the receiver operating characteristic curve.

cs.CR↗