ThreatDetect

ThreatDetect ThreatDetect는 Streamlit 기반의 내부 위협 탐지 프로토타입으로, 직원 활동 데이터를 분석하여 잠재적인 위험 행동을 식별합니다. 학습된 XGBoost 분류기와 Isolation Forest 이상 탐지기를 함께 사용하여 조직 수준의 위험 요약과 설명 가능한 직원 수준의 인사이트를 제공합니다. 데모 이 앱은 Streamlit을 사용하여 로컬에서 실행할 수 있습니다. 배포된 데모는 다음에서 확인할 수 있었습니다: https://threatdetectcos720.streamlit.app/ 주요 ...

Systematically Optimized CNN-Transformer with Focal Loss for Imbalanced Intrusion Detection on NSL-KDD

Intrusion Detection Systems IDS struggle with imbalanced datasets like NSL-KDD, especially in detecting rare R2L and U2R attacks. This work describes a systematically optimized and explainable framework using a CNN-Transformer architecture to improve performance on highly imbalanced data. We...

CVE-2026-92786

A flaw was found in LightGBM. The software does not properly validate data within text models, which can be exploited by an attacker. By crafting a malicious model file, an attacker can trigger an out-of-bounds write during the SHAP prediction process. This vulnerability could lead to arbitrary...

Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection

Large language models LLMs are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation through prompt injection and jailbreak attacks. Classifier-based guardrails, such as Prompt Guard 2, are widely used as a first line of defense against suc...

CVE-2026-92786

LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...

CVE-2026-92786 LightGBM through 4.7.0 Out-of-Bounds Write via Crafted Model

LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...

CVE-2026-92786 LightGBM through 4.7.0 Out-of-Bounds Write via Crafted Model

LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...

CVE-2026-92786

LightGBM versions through 4.7.0 are affected by an out-of-bounds write vulnerability triggered during SHAP prediction (feature contribution computation). The root cause is a failure to validate child and split array values when parsing text model files. An attacker can craft a malicious model fil...

EUVD-2026-81049

LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...

CVE-2026-92786 LightGBM through 4.7.0 Out-of-Bounds Write via Crafted Model

LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...

CVE-2026-92786: Out-of-bounds Write

LightGBM through 4.7.0 fails to validate child and split array values when parsing text models, allowing attackers to write out-of-bounds memory during SHAP prediction. Attackers can craft malicious model files with invalid node references that trigger out-of-bounds writes at attacker-chosen...

PT-2026-94063

Name of the Vulnerable Software and Affected Versions LightGBM versions prior to 4.7.1 Description Failure to validate child and split array values when parsing text models allows for out-of-bounds memory writes during SHAP SHapley Additive exPlanations, a method used to explain the output of...

Self-Verifying Anomaly Detection Using Explainable AI for Cybersecurity of DER Networks

The rapid growth of Distributed Energy Resources DERs has significantly expanded the cyber attack surface of modern power grids. Furthermore, increasing sophistication in attack techniques demands anomaly detection systems ADS that are accurate, interpretable, and reliable to support DER...

A Calibrated and Explainable Bimodal Machine Learning Framework for Hybrid Intrusion Detection

Modern communication systems face critical gaps in detecting unknown attacks and rare threat classes due to extreme data imbalance and black-box decision logic. We propose a bimodal framework of calibrated and explainable machine learning ML for network security, unifying known-class precision wi...

Dueling Deep Q-Learning for Intrusion Detection

Intrusion detection systems IDS and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods. Though effective, these models struggle to effectively adapt to new attack types. This study proposes a novel approach by employing a...

How Jailbreak Attacks Inform Safety Alignment: A Defender-Centric, Shapley-Based Evaluation of Jailbreak Contributions

Jailbreak attacks on large language models are usually evaluated by attacker-centric metrics such as attack success rate ASR, yet an attack that breaks a model is not necessarily useful for improving its safety. We propose a defender-centric view of jailbreak evaluation, where attacks are evaluat...

Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers

Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to understand and triage alerts. We extend our prior MLP conference study to Random Forest and XGBoost across four tabular securi...

Multi-Level Distributional Entropy for Explainable Network Intrusion Detection

Machine learning network intrusion detection systems IDS rely on aggregate flow statistics that discard distributional structure, while established entropy measures require raw packet sequences unavailable in pre-aggregated flow datasets. We propose Multi-Level Distributional Entropy MDE, an...

NLLog: Lightweight, Explainable SOC Anomaly Detection Via Log-To-Language Rewriting

System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We present NLLog Natural-Language Log, a lightweight pipeline that deterministically rewrites parsed templates into WHO-WHAT-SEVERITY sentences, pools...

Explainable Machine Learning for Phishing Detection on Heterogeneous Datasets with MCP-Enabled Deployment

With the growth in digital transformation and Internet usage, the Social Engineering techniques such as Phishing have become a major concern for the users and the organizations. Phishing attacks involve deceptive techniques to trick users into revealing confidential information that causes...