A Proactive Insider Threat Management Framework Using Explainable Machine Learning

Selma Shikonde; Mike Wa Nkongolo

Home/Archive/arXiv

arXiv · 2510.19883

A Proactive Insider Threat Management Framework Using Explainable Machine Learning

Selma Shikonde·Mike Wa Nkongolo

View original source ↗Read source document ↗Export metadata ↓

Abstract

Over the years, the technological landscape has evolved, reshaping the security posture of organisations and increasing their exposure to cybersecurity threats, many originating from within. Insider threats remain a major challenge, particularly in sectors where cybersecurity infrastructure, expertise, and regulations are still developing. This study proposes the Insider Threat Explainable Machine Learning (IT-XML) framework, which integrates the Cross-Industry Standard Process for Data Mining (CRISP-DM) with Hidden Markov Models (HMM) to enhance proactive insider threat management and decision-making. A quantitative approach is adopted using an online questionnaire to assess employees' knowledge of insider threat patterns, access control, privacy practices, and existing policies across three large data-sensitive organisations. The IT-XML framework provides assessment capabilities through survey-based data, HMM-driven pattern recognition for security maturity classification, and evidence-based recommendations for proactive threat mitigation. The framework classified all organisations at the developing security maturity level with 97-98% confidence and achieved a classification accuracy of 91.7%, identifying audit log access limits as the most critical control. Random Forest analysis highlighted vendor breach notifications (0.081) and regular audit log reviews (0.052) as key determinants of resilience. Explainability methods such as SHAP and LIME improved model transparency and interpretability, demonstrating the framework's potential to strengthen insider threat management practices.

Explore related subjects

cs.CR cs.CY

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Selma Shikonde, Mike Wa Nkongolo. 2025-10-22. A Proactive Insider Threat Management Framework Using Explainable Machine Learning. https://arxiv.org/abs/2510.19883

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

UPPRESSO: Untraceable and Unlinkable Privacy-PREserving Single Sign-On Services

Single sign-on (SSO) allows a user to maintain only the credential for an identity provider (IdP) to log into multiple relying parties (RPs). However, SSO introduces privacy threats, as (a) a curious IdP could track a user's all visits to RPs, and (b) colluding RPs could learn a user's online profile by linking her identities across these RPs. This paper presents a privacypreserving SSO scheme, called UPPRESSO, to protect an honest user's online profile against (a) an honest-but-curious IdP and (b) malicious RPs colluding with other users. UPPRESSO proposes an identity-transformation approach to generate untraceable ephemeral pseudo-identities for an RP and a user from which the target RP derives a permanent account for the user, while the transformations also provide unlinkability. This approach protects the identities of the user and the target RPs in a login flow, while working compatibly with widely-deployed SSO protocols and providing services accessed from a commercial-off-the-shelf browser without plug-ins or extensions. We built a prototype of UPPRESSO on top of MITREid Connect, an open-source SSO system. The extensive evaluations show that it fulfills the security and privacy requirements of SSO with reasonable overheads.

cs.CR↗

arXiv2026

Attack Tree Distance: a practical examination of tree difference measurement within cyber security

Attack trees are a popular threat modeling method. In practice, there is often a need to compare attack tree models produced by human experts, based on both the structure of the tree and the meaning of the node labels. In this work, we investigate the problem of comparing attack trees and measuring their similarity. We define five different measures for measuring the distance between two attack trees: Label Distance (LD), Tree Edit Distance (TED), Radical Distance (RD), Multiset Distance (MSD) and Weighted Sum Distance (WSD). We further propose a repeatable method of both theoretical and experimental attack tree distance measures validation. Our theoretical validation consists of a series of basic transformations to evaluate the behavior of distance measures with respect to specific types of transformations that may appear between two attack trees. To experimentally validate our distance measures, we designed and executed a human study ($n=39$) to collect a dataset of attack trees to be used for evaluation and comparison of the measures. From our theoretical and experimental results, we find that applying semantic similarity as a means of comparing node labels is a valid approach. Further, we find four of the five attack tree distance measures are valid approaches in certain, varying circumstances. Our results suggest that these methods can already be used to identify similar real-world attack trees. Overall, this work lays the groundwork for improved threat model analysis, validation of AI-generated attack trees, and future research into threat similarity measurement in cybersecurity.

cs.CR↗

arXiv2026

Probabilistic Modeling of Jailbreak on Multimodal LLMs: From Quantification to Application

Recently, Multimodal Large Language Models (MLLMs) have demonstrated their superior ability in understanding multimodal content. However, they remain vulnerable to jailbreak attacks, which exploit weaknesses in their safety alignment to generate harmful responses. Previous studies categorize jailbreaks as successful or failed based on whether responses contain malicious content. However, given the stochastic nature of MLLM responses, this binary classification of an input's ability to jailbreak MLLMs is inappropriate. Derived from this viewpoint, we introduce jailbreak probability to quantify the jailbreak potential of an input, which represents the likelihood that MLLMs generated a malicious response when prompted with this input. We approximate this probability through multiple queries to MLLMs. After modeling the relationship between input hidden states and their corresponding jailbreak probability using Jailbreak Probability Prediction Network (JPPN), we use continuous jailbreak probability for optimization. Specifically, we propose Jailbreak-Probability-based Attack (JPA) that optimizes adversarial perturbations on input image to maximize jailbreak probability, and further enhance it as Multimodal JPA (MJPA) by including monotonic text rephrasing. To counteract attacks, we also propose Jailbreak-Probability-based Finetuning (JPF), which minimizes jailbreak probability through MLLM parameter updates. Extensive experiments show that (1) (M)JPA yields significant improvements when attacking a wide range of models under both white and black box settings. (2) JPF vastly reduces jailbreaks by at most over 60\%. Both of the above results demonstrate the significance of introducing jailbreak probability to make nuanced distinctions among input jailbreak abilities.

cs.CR↗