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Full-Text Articles in Computer Sciences
On The Feasibility Of Profiling, Forecasting And Authenticating Internet Usage Based On Privacy Preserving Netflow Logs, Soheil Sarmadi
On The Feasibility Of Profiling, Forecasting And Authenticating Internet Usage Based On Privacy Preserving Netflow Logs, Soheil Sarmadi
USF Tampa Graduate Theses and Dissertations
Understanding Internet user behavior and Internet usage patterns is fundamental in developing future access networks and services that meet technical as well as Internet user needs. User behavior is routinely studied and measured, but with different methods depending on the research discipline of the investigator, and these disciplines rarely cross. We tackle this challenge by developing frameworks that the Internet usage statistics used as the main features in understanding Internet user behaviors, with the purpose of finding a complete picture of the user behavior and working towards a unified analysis methodology. In this dissertation we collected Internet usage statistics via …
Automatic Multimodal Assessment Of Neonatal Pain, Ghada Zamzmi
Automatic Multimodal Assessment Of Neonatal Pain, Ghada Zamzmi
USF Tampa Graduate Theses and Dissertations
For several decades, pediatricians used to believe that neonates do not feel pain. The American Academy of Pediatrics (AAP) recognized neonates' sense of pain in 1987. Since then, there have been many studies reporting a strong association between repeated pain exposure (under-treatment) and alterations in brain structure and function. This association has led to the increased use of anesthetic medications. However, recent studies found that the excessive use of analgesic medications (over-treatment) can cause many side effects. The current standard for assessing neonatal pain is discontinuous and suffers from inter-observer variations, which can lead to over- or under-treatment. Therefore, it …
Machine Learning Methods For Network Intrusion Detection And Intrusion Prevention Systems, Zheni Svetoslavova Stefanova
Machine Learning Methods For Network Intrusion Detection And Intrusion Prevention Systems, Zheni Svetoslavova Stefanova
USF Tampa Graduate Theses and Dissertations
Given the continuing advancement of networking applications and our increased dependence upon software-based systems, there is a pressing need to develop improved security techniques for defending modern information technology (IT) systems from malicious cyber-attacks. Indeed, anyone can be impacted by such activities, including individuals, corporations, and governments. Furthermore, the sustained expansion of the network user base and its associated set of applications is also introducing additional vulnerabilities which can lead to criminal breaches and loss of critical data. As a result, the broader cybersecurity problem area has emerged as a significant concern, with many solution strategies being proposed for both …
Similarity Based Large Scale Malware Analysis: Techniques And Implications, Yuping Li
Similarity Based Large Scale Malware Analysis: Techniques And Implications, Yuping Li
USF Tampa Graduate Theses and Dissertations
Malware analysis and detection continues to be one of the central battlefields for cybersecurity industry. For the desktop malware domain, we observed multiple significant ransomware attacks in the past several years, e.g., it was estimated that in 2017 the WannaCry ransomware attack affected more than 200,000 computers across 150 countries with hundreds of millions damages. Similarly, we witnessed the increased impacts of Android malware on global individuals due to the popular smartphone and IoT devices worldwide. In this dissertation, we describe similarity comparison based novel techniques that can be applied to achieve large scale desktop and Android malware analysis, and …