An Introduction to Adaptive Software Security
- URL: http://arxiv.org/abs/2312.17358v1
- Date: Thu, 28 Dec 2023 20:53:11 GMT
- Title: An Introduction to Adaptive Software Security
- Authors: Mehran Alidoost Nia
- Abstract summary: This paper presents an innovative approach integrating the MAPE-K loop and the Software Development Life Cycle (SDLC)
It proactively embeds security policies throughout development, reducing vulnerabilities from different levels of software engineering.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents the adaptive software security model, an innovative
approach integrating the MAPE-K loop and the Software Development Life Cycle
(SDLC). It proactively embeds security policies throughout development,
reducing vulnerabilities from different levels of software engineering. Three
primary contributions-MAPE-K integration, SDLC embedding, and analytical
insights-converge to create a comprehensive approach for strengthening software
systems against security threats. This research represents a paradigm shift,
adapting security measures with agile software development and ensuring
continuous improvement in the face of evolving threats. The model emerges as a
robust solution, addressing the crucial need for adaptive software security
strategies in modern software development. We analytically discuss the
advantages of the proposed model.
Related papers
- Model Developmental Safety: A Safety-Centric Method and Applications in Vision-Language Models [75.8161094916476]
We study how to develop a pretrained vision-language model (aka the CLIP model) for acquiring new capabilities or improving existing capabilities of image classification.
Our experiments on improving vision perception capabilities on autonomous driving and scene recognition datasets demonstrate the efficacy of the proposed approach.
arXiv Detail & Related papers (2024-10-04T22:34:58Z) - Continuous risk assessment in secure DevOps [0.24475591916185502]
We argue how secure DevOps could profit from engaging with risk related activities within organisations.
We focus on combining Risk Assessment (RA), particularly Threat Modelling (TM) and apply security considerations early in the software life-cycle.
arXiv Detail & Related papers (2024-09-05T10:42:27Z) - Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - Threat Modelling and Risk Analysis for Large Language Model (LLM)-Powered Applications [0.0]
Large Language Models (LLMs) have revolutionized various applications by providing advanced natural language processing capabilities.
This paper explores the threat modeling and risk analysis specifically tailored for LLM-powered applications.
arXiv Detail & Related papers (2024-06-16T16:43:58Z) - SoK: A Defense-Oriented Evaluation of Software Supply Chain Security [3.165193382160046]
We argue that the next stage of software supply chain security research and development will benefit greatly from a defense-oriented approach.
This paper introduces the AStRA model, a framework for representing fundamental software supply chain elements and their causal relationships.
arXiv Detail & Related papers (2024-05-23T18:53:48Z) - Assessing the Threat Level of Software Supply Chains with the Log Model [4.1920378271058425]
The use of free and open source software (FOSS) components in all software systems is estimated to be above 90%.
This work presents a novel approach of assessing threat levels in FOSS supply chains with the log model.
arXiv Detail & Related papers (2023-11-20T12:44:37Z) - Software Repositories and Machine Learning Research in Cyber Security [0.0]
The integration of robust cyber security defenses has become essential across all phases of software development.
Attempts have been made to leverage topic modeling and machine learning for the detection of these early-stage vulnerabilities in the software requirements process.
arXiv Detail & Related papers (2023-11-01T17:46:07Z) - Leveraging Traceability to Integrate Safety Analysis Artifacts into the
Software Development Process [51.42800587382228]
Safety assurance cases (SACs) can be challenging to maintain during system evolution.
We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models.
We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
arXiv Detail & Related papers (2023-07-14T16:03:27Z) - Towards Safer Generative Language Models: A Survey on Safety Risks,
Evaluations, and Improvements [76.80453043969209]
This survey presents a framework for safety research pertaining to large models.
We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models.
We explore the strategies for enhancing large model safety from training to deployment.
arXiv Detail & Related papers (2023-02-18T09:32:55Z) - Evaluating Model-free Reinforcement Learning toward Safety-critical
Tasks [70.76757529955577]
This paper revisits prior work in this scope from the perspective of state-wise safe RL.
We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection.
To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
arXiv Detail & Related papers (2022-12-12T06:30:17Z) - Practical Machine Learning Safety: A Survey and Primer [81.73857913779534]
Open-world deployment of Machine Learning algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities.
New models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks.
Our organization maps state-of-the-art ML techniques to safety strategies in order to enhance the dependability of the ML algorithm from different aspects.
arXiv Detail & Related papers (2021-06-09T05:56:42Z)
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.