SCG News

Finding and Mitigating Cross-Site Scripting Attack Vectors — Testing different Web Application Security Scanners

Rafael Burkhalter. Finding and Mitigating Cross-Site Scripting Attack Vectors — Testing different Web Application Security Scanners. Bachelor’s thesis, University of Bern, April 2021. Details.

Abstract

The purpose of this thesis is to determine the efficacy and usability of different popular security scanners for web applications. The main focus lies on testing their ability to find cross-site scripting vulnerabilities, i.e. vulnerabilities arising when user input isn’t properly sanitized. To analyze the scanners various criteria are taken into account mainly completeness of the findings, ease of use and installation effort. In a second part an overview on how to analyze a scanner’s result and how Cross-Site Scripting attacks can be mitigated is given.

Posted by scg at 15 June 2021, 3:15 pm comment link

Speculative Analysis for Quality Assessment of Code Comments

Pooja Rani. Speculative Analysis for Quality Assessment of Code Comments. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), p. 299-303, 2021. Details.

Abstract

Previous studies have shown that high-quality code comments assist developers in program comprehension and maintenance tasks. However, the semi-structured nature of comments, unclear conventions for writing good comments, and the lack of quality assessment tools for all aspects of comments make their evaluation and maintenance a non-trivial problem. To achieve high-quality comments, we need a deeper understanding of code comment characteristics and the practices developers follow. In this thesis, we approach the problem of assessing comment quality from three different perspectives: what developers ask about commenting practices, what they write in comments, and how researchers support them in assessing comment quality. Our preliminary findings show that developers embed various kinds of information in class comments across programming languages. Still, they face problems in locating relevant guidelines to write consistent and informative comments, verifying the adherence of their comments to the guidelines, and evaluating the overall state of comment quality. To help developers and researchers in building comment quality assessment tools, we provide: (i) an empirically validated taxonomy of comment convention-related questions from various community forums, (ii) an empirically validated taxonomy of comment information types from various programming languages, (iii) a language-independent approach to automatically identify the information types, and (iv) a comment quality taxonomy prepared from a systematic literature review.

Posted by scg at 24 May 2021, 8:15 pm comment link

Makar: A Framework for Multi-source Studies based on Unstructured Data

Mathias Birrer, Pooja Rani, Sebastiano Panichella, and Oscar Nierstrasz. Makar: A Framework for Multi-source Studies based on Unstructured Data. In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), () p. 577-581, 2021. Details.

Abstract

To perform various development and maintenance tasks, developers frequently seek information on various sources such as mailing lists, Stack Overflow (SO), and Quora. Researchers analyze these sources to understand developer information needs in these tasks. However, extracting and preprocessing unstructured data from various sources, building and maintaining a reusable dataset is often a time-consuming and iterative process. Additionally, the lack of tools for automating this data analysis process complicates the task to reproduce previous results or datasets.To address these concerns we propose Makar, which provides various data extraction and preprocessing methods to support researchers in conducting reproducible multi-source studies. To evaluate Makar, we conduct a case study that analyzes code comment related discussions from SO, Quora, and mailing lists. Our results show that Makar is helpful for preparing reproducible datasets from multiple sources with little effort, and for identifying the relevant data to answer specific research questions in a shorter time compared to state-of-the-art tools, which is of critical importance for studies based on unstructured data. Tool webpage: https://github.com/maethub/makar

Posted by scg at 24 May 2021, 8:15 pm comment link

Stopping DNS Rebinding Attacks in the Browser

Mohammadreza Hazhirpasand, Arash Ale Ebrahim, and Oscar Nierstrasz. Stopping DNS Rebinding Attacks in the Browser. In Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP, 2021. Details.

Abstract

DNS rebinding attacks circumvent the same-origin policy of browsers and severely jeopardize user privacy. Although recent studies have shown that DNS rebinding attacks pose severe security threats to users, up to now little effort has been spent to assess the effectiveness of known solutions to prevent such attacks. We have carried out such a study to assess the protective measures proposed in prior studies. We found that none of the recommended techniques can entirely halt this attack due to various factors, e.g., network layer encryption renders packet inspection infeasible. Examining the previous problematic factors, we realize that a protective measure must be implemented at the browser-level. Therefore, we propose a defensive measure, a browser plug-in called Fail-rebind, that can detect, inform, and protect users in the event of an attack. Afterwards, we discuss the merits and limitations of our method compared to prior methods. Our findings suggest that Fail-rebind does not nec essitate expert knowledge, works on different OSes and smart devices, and is independent of networks and location.

Posted by scg at 23 February 2021, 3:34 pm comment link

Biomimicry-based Algorithms and Their Lack of Generalization

Dean Klopsch. Biomimicry-based Algorithms and Their Lack of Generalization. Bachelor’s thesis, University of Bern, February 2021. Details.

Abstract

Biomimicry has received much attention in engineering, and many breakthrough discoveries have been guided by a solution found in nature. However, many biomimicry-based proposals apply to a specific problem, provide limited context, and lack implementation details. That makes it unnecessarily hard for practitioners to find relevant literature for their problems. To investigate this problem, we performed a literature review on 111 publications related to biomimicry and extracted several characteristics, e.g., meta-data, the solution, and the investigated species. In particular, we were interested in whether the proposed algorithms could be used for other use cases. Our results indicate a structural issue: publications related to new or adapted algorithms very prominently emphasize on a specific use case, instead of the generalized problem category, e.g., clustering. We found that 38% lack generalization at least in one of the introductory elements (i.e., title, abstract, and introduction), and that 53% of them lack generalization entirely. Moreover, 40% of the proposed algorithms lack at least one major characteristic, e.g., code samples or benchmarks against state of the art algorithms. We motivate the found generalization problem with our adapted implementation of an algorithm proposed for load scheduling. Moreover, the artifacts of this study can support practitioners in finding more efficiently existing solutions across research domains.

Posted by scg at 17 February 2021, 4:15 pm comment link
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