(In)Security of Mobile Apps in Developing Countries: A Systematic Literature Review
- URL: http://arxiv.org/abs/2405.05117v2
- Date: Tue, 24 Sep 2024 12:24:51 GMT
- Title: (In)Security of Mobile Apps in Developing Countries: A Systematic Literature Review
- Authors: Alioune Diallo, Jordan Samhi, Tegawendé Bissyandé, Jacques Klein,
- Abstract summary: In developing countries, several key sectors, including education, finance, agriculture, and healthcare, mainly deliver their services via mobile app technology on handheld devices.
Mobile app security has emerged as a paramount issue in developing countries.
- Score: 4.906685634163683
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In developing countries, several key sectors, including education, finance, agriculture, and healthcare, mainly deliver their services via mobile app technology on handheld devices. As a result, mobile app security has emerged as a paramount issue in developing countries. In this paper, we investigate the state of research on mobile app security, focusing on developing countries. More specifically, we performed a systematic literature review exploring the research directions taken by existing works, the different security concerns addressed, and the techniques used by researchers to highlight or address app security issues. Our main findings are: (1) the literature includes only a few studies on mobile app security in the context of developing countries ; (2) among the different security concerns that researchers study, vulnerability detection appears to be the leading research topic; (3) FinTech apps are revealed as the main target in the relevant literature. Overall, our work highlights that there is largely room for developing further specialized techniques addressing mobile app security in the context of developing countries.
Related papers
- Mobile App Security Trends and Topics: An Examination of Questions From Stack Overflow [10.342268145364242]
We mine Stack Overflow for questions on mobile app security, which we analyze using quantitative and qualitative techniques.
The findings reveal that Stack Overflow is a major resource for developers seeking help with mobile app security, especially for Android apps.
Insights from this research can inform the development of tools, techniques, and resources by the research and vendor community.
arXiv Detail & Related papers (2024-09-12T10:45:45Z) - A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges [10.342268145364242]
This study explores the common practices and challenges that developers face in securing their apps.
Our findings show that developers place high importance on security, frequently implementing features such as authentication and secure storage.
We envision our findings leading to improved security practices, better-designed tools and resources, and more effective training programs.
arXiv Detail & Related papers (2024-08-16T22:03:06Z) - Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context.
We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
arXiv Detail & Related papers (2024-07-31T17:59:24Z) - Networking Systems for Video Anomaly Detection: A Tutorial and Survey [56.44953602790945]
Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community.
This article offers an exhaustive tutorial for novices in NSVAD.
We showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD.
arXiv Detail & Related papers (2024-05-16T02:00:44Z) - Against The Achilles' Heel: A Survey on Red Teaming for Generative Models [60.21722603260243]
The field of red teaming is experiencing fast-paced growth, which highlights the need for a comprehensive organization covering the entire pipeline.
Our extensive survey, which examines over 120 papers, introduces a taxonomy of fine-grained attack strategies grounded in the inherent capabilities of language models.
We have developed the searcher framework that unifies various automatic red teaming approaches.
arXiv Detail & Related papers (2024-03-31T09:50:39Z) - The current state of security -- Insights from the German software industry [0.0]
This paper outlines the main ideas of secure software development that have been discussed in the literature.
A dataset on implementation in practice is gathered through a qualitative interview research involving 20 companies.
arXiv Detail & Related papers (2024-02-13T13:05:10Z) - "We are a startup to the core": A qualitative interview study on the
security and privacy development practices in Turkish software startups [7.222052188523043]
Security and privacy are neglected in software development, and rarely a priority for developers.
To close this research gap, we conducted a semi-structured interview study with 16 developers working in Turkish software startups.
Our main finding is that developers rarely prioritize security and privacy, due to a lack of awareness, skills, and resources.
arXiv Detail & Related papers (2022-12-16T10:40:43Z) - SafeText: A Benchmark for Exploring Physical Safety in Language Models [62.810902375154136]
We study commonsense physical safety across various models designed for text generation and commonsense reasoning tasks.
We find that state-of-the-art large language models are susceptible to the generation of unsafe text and have difficulty rejecting unsafe advice.
arXiv Detail & Related papers (2022-10-18T17:59:31Z) - Inspect, Understand, Overcome: A Survey of Practical Methods for AI
Safety [54.478842696269304]
The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings.
In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged.
Our paper addresses both machine learning experts and safety engineers.
arXiv Detail & Related papers (2021-04-29T09:54:54Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - An Empirical Study on Developing Secure Mobile Health Apps: The
Developers Perspective [0.0]
MHealth apps (mHealth apps for short) are becoming integral part of mobile and pervasive computing to improve the availability and quality of healthcare services.
Despite the offered benefits, mHealth apps face a critical challenge, i.e., security of health critical data that is produced and consumed by the app.
Several studies have revealed that security specific issues of mHealth apps have not been adequately addressed.
arXiv Detail & Related papers (2020-08-07T08:23:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.