Efficient Cybersecurity Assessment Using SVM and Fuzzy Evidential Reasoning for Resilient Infrastructure

• URL: http://arxiv.org/abs/2506.22938v1
• Date: Sat, 28 Jun 2025 16:08:34 GMT
• Title: Efficient Cybersecurity Assessment Using SVM and Fuzzy Evidential Reasoning for Resilient Infrastructure
• Authors: Zaydon L. Ali, Wassan Saad Abduljabbar Hayale, Israa Ibraheem Al_Barazanchi, Ravi Sekhar, Pritesh Shah, Sushma Parihar,
• Abstract summary: This paper proposes an assessment model for security issues using fuzzy evidential reasoning (ER) approaches.<n>To overcome with such complications, this paper proposes an assessment model for security issues using fuzzy evidential reasoning (ER) approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With current advancement in hybermedia knowledges, the privacy of digital information has developed a critical problem. To overawed the susceptibilities of present security protocols, scholars tend to focus mainly on efforts on alternation of current protocols. Over past decade, various proposed encoding models have been shown insecurity, leading to main threats against significant data. Utilizing the suitable encryption model is very vital means of guard against various such, but algorithm is selected based on the dependency of data which need to be secured. Moreover, testing potentiality of the security assessment one by one to identify the best choice can take a vital time for processing. For faster and precisive identification of assessment algorithm, we suggest a security phase exposure model for cipher encryption technique by invoking Support Vector Machine (SVM). In this work, we form a dataset using usual security components like contrast, homogeneity. To overcome the uncertainty in analysing the security and lack of ability of processing data to a risk assessment mechanism. To overcome with such complications, this paper proposes an assessment model for security issues using fuzzy evidential reasoning (ER) approaches. Significantly, the model can be utilised to process and assemble risk assessment data on various aspects in systematic ways. To estimate the performance of our framework, we have various analyses like, recall, F1 score and accuracy.

Related papers

• DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective [59.66984417026933]
  We introduce a novel taxonomy, classifying existing methods based on their reliance on internal features (IF) (inherent to the data) versus external features (EF) (artificially introduced for auditing)<n>We formulate two primary attack types: evasion attacks, designed to conceal the use of a dataset, and forgery attacks, intending to falsely implicate an unused dataset.<n>Building on the understanding of existing methods and attack objectives, we further propose systematic attack strategies: decoupling, removal, and detection for evasion; adversarial example-based methods for forgery.<n>Our benchmark, DATABench, comprises 17 evasion attacks, 5 forgery attacks, and 9
  arXiv  Detail & Related papers  (2025-07-08T03:07:15Z)
• Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation [52.83870601473094]
  Embodied agents exhibit immense potential across a multitude of domains.<n>Existing research predominantly concentrates on the security of general large language models.<n>This paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents.
  arXiv  Detail & Related papers  (2025-04-22T08:34:35Z)
• A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning [2.942616054218564]
  Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare.<n>Current security implementations for these systems face a fundamental trade-off: rigorous cryptographic protections impose prohibitive computational overhead.<n>We present Fast and Secure Federated Learning, a novel approach that strategically combines selective homomorphic encryption, differential privacy, and bitwise scrambling to achieve robust security.
  arXiv  Detail & Related papers  (2025-01-22T14:37:44Z)
• Towards Precise Observations of Neural Model Robustness in Classification [2.127049691404299]
  In deep learning applications, robustness measures the ability of neural models that handle slight changes in input data. Our approach contributes to a deeper understanding of model robustness in safety-critical applications.
  arXiv  Detail & Related papers  (2024-04-25T09:37:44Z)
• ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models [65.79770974145983]
  ASSERT, Automated Safety Scenario Red Teaming, consists of three methods -- semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection. We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance. We find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings.
  arXiv  Detail & Related papers  (2023-10-14T17:10:28Z)
• Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing [1.2183405753834557]
  Current AI algorithms are unable to identify common causes for failure detection. Additional techniques are required to quantify the quality of predictions. This thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering.
  arXiv  Detail & Related papers  (2023-08-06T18:05:59Z)
• Meta-Learning Priors for Safe Bayesian Optimization [72.8349503901712]
  We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
  arXiv  Detail & Related papers  (2022-10-03T08:38:38Z)
• Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning [72.97229770329214]
  We introduce a general approach for seeking high dimensional non-linear optimization problems in which maintaining safety during learning is crucial. Our approach called LBSGD is based on applying a logarithmic barrier approximation with a carefully chosen step size. We demonstrate the effectiveness of our approach on minimizing violation in policy tasks in safe reinforcement learning.
  arXiv  Detail & Related papers  (2022-07-21T11:14:47Z)
• 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)
• Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation [23.43175124406634]
  Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks. We propose a flow-based multimodal safety-critical scenario generator for evaluating decisionmaking algorithms. We evaluate six Reinforcement Learning algorithms with our generated traffic scenarios and provide empirical conclusions about their robustness.
  arXiv  Detail & Related papers  (2020-09-16T15:16:43Z)
• A Safety Framework for Critical Systems Utilising Deep Neural Networks [13.763070043077633]
  This paper presents a principled novel safety argument framework for critical systems that utilise deep neural networks. The approach allows various forms of predictions, e.g., future reliability of passing some demands, or confidence on a required reliability level. It is supported by a Bayesian analysis using operational data and the recent verification and validation techniques for deep learning.
  arXiv  Detail & Related papers  (2020-03-07T23:35:05Z)

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.