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Browsing histories can be a valuable resource for cybersecurity, research, and testing. Individuals are often reluctant to share their browsing histories online, and the use of personal data requires obtaining signed informed consent. Research shows that anonymized histories can lead to re-identification, nullifying the anonymity promised by informed consent. In this work, we present 500 synthetic browsing histories valid for 50 countries worldwide. The synthetic histories are compiled based on real browsing data using a series of transformation criteria, including website content, popularity, locality, and language, ensuring their validity for the respective countries. Each history maintains the order of webpage accesses and covers a one-month period. The motivation for publishing this dataset arises from the community's call for browsing histories from different countries for research, development, and education. The published synthetic browsing histories can be used for any purpose without legal restrictions. [ABSTRACT FROM AUTHOR]

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a host behavior based darknet traffic decomposition approach to identifying groups of malicious events from massive historical darknet traffic. In this approach, we segment traffic flows from captured darknet data, distinguish scan from non-scan flows, and categorize scans according to scan width spreads. Consequently, event groups are appraised by applying the criterion that malicious events generated by the same attacker or malicious software should have similar average packet delay, AvgDly. We have applied the proposed approach to 12 months darknet traffic data for malicious events grouping. As a result, several large scale event groups are discovered on host behavior in the category of port scan, IP scan and hybrid scan, respectively. [ABSTRACT FROM AUTHOR]