Essays about: "Detektionsmetoder"
Showing result 1 - 5 of 14 essays containing the word Detektionsmetoder.
-
1. Static Analysis Of Client-Side JavaScript Code To Detect Server-Side Business Logic Vulnerabilities
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In the real world, web applications are crucial in various domains, from e-commerce to finance and healthcare. However, these applications are not immune to vulnerabilities, particularly in business logic. Detecting such vulnerabilities can be challenging due to the complexity and diversity of application functionality. READ MORE
-
2. Birefringence: Effects and Implications on In-Ice Radio Detection of High-Energy Neutrinos
University essay from Uppsala universitet/HögenergifysikAbstract : The detection of high-energy neutrinos in the EeV range requires new detection techniques to cope with the small expected flux. The radio detection method, utilizing Askaryan emission, can be used to detect these neutrinos in polar ice. READ MORE
-
3. Road Damage Segmentation for Mobile Hardware
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The detection and early repair of road damage are paramount for the quality and safety of roads. Current detection efforts typically rely on Deep Learning methods for object detection with bounding boxes, with calculations performed on high-performance hardware. READ MORE
-
4. A monthly snapshot-based approach for threat hunting within Windows IT environments
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This work aims at implementing a threat hunting method based on monthly data snapshots in Windows IT environments, and at assessing whether and how gaps between month-on-month snapshots may be analysed to enhance threat detection. The objective of our approach is to control monthly whether a set of IT systems has been compromised by an attacker, separately from the deployed monitoring systems (e. READ MORE
-
5. Evaluating Unsupervised Methods for Out-of-Distribution Detection on Semantically Similar Image Data
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Out-of-distribution detection considers methods used to detect data that deviates from the underlying data distribution used to train some machine learning model. This is an important topic, as artificial neural networks have previously been shown to be capable of producing arbitrarily confident predictions, even for anomalous samples that deviate from the training distribution. READ MORE
