Phishing is fundamental to cyber attacks. This research determined the effect of Internet user age and email content such as weapons of influence (persuasive techniques that attackers can use to lure individuals to fall for an attack) and life domains (a specific topic or aspect of an individual's life that attackers can focus an email on) on spear-phishing (targeted phishing) susceptibility. In total, 100 young and 58 older users received, without their knowledge, daily simulated phishing emails over 21 days. A browser plugin recorded their clicking on links in the emails as an indicator of their susceptibility. Forty-three percent of users fell for the simulated phishing emails, with older women showing the highest susceptibility. While susceptibility in young users declined across the study, susceptibility in older users remained stable. The relative effectiveness of the attacks differed by weapons of influence and life domains with age-group variability. In addition, older compared to young users reported lower susceptibility awareness. These findings support effects of Internet user demographics and email content on susceptibility to phishing and emphasize the need for personalization of the next generation of security solutions. CCS Concepts: • Security and privacy → Phishing; Social aspects of security and privacy; • Humancentered computing → Empirical studies in HCI; • Social and professional topics → Seniors;
Reverse engineering (RE) is the only foolproof method of establishing trust and assurance in hardware. This is especially important in today's climate, where new threats are arising daily. A Printed Circuit Board (PCB) serves at the heart of virtually all electronic systems and, for that reason, a precious target amongst attackers. Therefore, it is increasingly necessary to validate and verify these hardware boards both accurately and efficiently. When discussing PCBs, the current state-of-the-art is non-destructive RE through X-ray Computed Tomography (CT); however, it remains a predominantly manual process. Our work in this paper aims at paving the way for future developments in the automation of PCB RE by presenting automatic detection of vias, a key component to every PCB design. We provide a via detection framework that utilizes the Hough circle transform for the initial detection, and is followed by an iterative false removal process developed specifically for detecting vias. We discuss the challenges of detecting vias, our proposed solution, and lastly, evaluate our methodology not only from an accuracy perspective but the insights gained through iteratively removing false-positive circles as well. We also compare our proposed methodology to an off-the-shelf implementation with minimal adjustments of Mask R-CNN; a fast object detection algorithm that, although is not optimized for our application, is a reasonable deep learning model to measure our work against. The Mask R-CNN we utilize is a network pretrained on MS COCO followed by fine tuning/training on prepared PCB via images. Finally, we evaluate our results on two datasets, one PCB designed in house and another commercial PCB, and achieve peak results of 0.886, 0.936, 0.973, for intersection over union (IoU), Dice Coefficient, and Structural Similarity Index. These results vastly outperform our tuned implementation of Mask R-CNN.
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