Scientific Paper Proposal - Week 6 (#2556)

* Scientific Paper

* Update README.md

contributions/scientific-paper/week6/hocker-nwijkman/README.md

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+# Assignment Proposal
+
+## Title
+
+Machine Learning-Based Run-Time DevSecOps: ChatGPT Against Traditional Approach
+
+## Names and KTH ID
+
+  - Simon Hocker (hocker@kth.se)
+  - Nicole Wijkman (nwijkman@kth.se)
+
+## Deadline
+
+- Week 6
+
+## Category
+
+- Scientific paper
+
+## Description
+
+We will present a recent scientific paper from this year wherein they perform a comparative study between two different approaches to classifying suspicious server log activities and detecting potential threats. Specifically, it compares the classic, traditional machine learning approach of using Weka API for classification with a new proposed novel method of using ChatGPT for performing runtime log analysis. They explore the mechanics and potential of using ChatGPT in Python where context represents labelled data and the questions themselves contain the log records which are being evaluated. Beyond exploring the possibility of it, they also analyse the viability of the novel method, and examine its potential as well as its drawbacks and limitations, and then compares it with the tried and true Weka API method. 
+
+Our presentation will begin by elaborating on why such research is important for the field of DevSecOps, as well as describing the scenario being analysed in the paper. From there, we will go on to detail the workings of both the new and the traditional approach in detail, followed by their respective strengths and weaknesses. Afterwards, we will end the presentation with an explanation of the paper's conclusions.([Machine Learning-Based Run-Time DevSecOps: ChatGPT Against Traditional Approach](https://ieeexplore.ieee.org/document/10192161))
+
+**Relevance**
+
+Security integration in the DevOps pipeline is crucial for maintaining a secure development environment. This paper addresses this need by introducing AI-driven automated log analysis techniques to detect security threats during runtime. It also compares this approach with traditional machine learning practices, providing valuable insights for enhancing security and efficiency in agile development workflows.