ThreatDetect

ThreatDetect ThreatDetect is a Streamlit-based insider threat detection prototype that analyzes employee activity data and flags potentially risky behavior. It uses a trained XGBoost classifier together with an Isolation Forest anomaly detector to produce organisation-level risk summaries and...

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...

A Three-Axis Stress Test of LLM Vs Classical ML for Network Intrusion Detection under Distribution Shift and Adversarial Evasion

Large language models are increasingly benchmarked against classical machine learning for network intrusion detection NIDS, almost always using same-dataset evaluation, and that protocol turns out to be incomplete. Evaluating XGBoost and RoBERTa-LoRA on two independently collected NetFlow v2...

Are We Shooting Flies with Cannons? Trade-Off Analysis for AI-Based 5G Intrusion Detection

The increasing adoption of Artificial Intelligence AI in network intrusion detection raises the question of whether complex and computationally expensive models are justified for this task. In this work, we investigate the trade-off between detection performance and computational cost for intrusi...

Explainable Adaptive Zero Trust Framework for AWS with Adversarial Robustness Evaluation

Cloud environments built on Amazon Web Services face a structural security vulnerability: once a credential passes authentication, the resulting session is often treated as trusted for its entire duration. This assumption fails when credentials are stolen. We introduce the Explainable Adaptive Ze...

Beyond Detection Accuracy: Measuring Explanation Cost, Stability, and Utility for Resource-Aware IoT Intrusion Detection

Machine-learning intrusion-detection studies commonly emphasize predictive accuracy while treating explanation generation as a computationally free post-processing step. This study jointly evaluates predictive effectiveness, explanation cost, local explanation stability, and selective explanation...

Performance Analysis of Machine Learning Algorithms for Predicting Malware

Malware poses a persistent and evolving threat to modern computing systems, making accurate and timely detection a critical cybersecurity challenge. Traditional signature-based antivirus solutions often fail to identify newly emerging malware, leaving systems vulnerable until updated signatures...

PYSEC-2026-1444 H2O Vulnerable to Denial of Service (DoS) and File Write

In h2oai/h2o-3 version 3.46.0.1, the runtool command exposes classes in the water.tools package through the ast parser. This includes the XGBoostLibExtractTool class, which can be exploited to shut down the server and write large files to arbitrary directories, leading to a denial of service...

H2O Vulnerable to Denial of Service (DoS) and File Write

In h2oai/h2o-3 version 3.46.0.1, the runtool command exposes classes in the water.tools package through the ast parser. This includes the XGBoostLibExtractTool class, which can be exploited to shut down the server and write large files to arbitrary directories, leading to a denial of service...

Detecting Data Exfiltration through I2P Anonymity Networks: A Two-Phase Machine Learning Approach

The Invisible Internet Project I2P provides strong anonymity through garlic routing and distributed network architecture, making it attractive for legitimate privacy needs. Nevertheless, the same properties can be exploited by malicious actors to steal sensitive information from corporate network...

Malicious code in @ml-toolkit-ts/xgboost (npm)

--- -= Per source details. Do not edit below this line.=- Source: google-open-source-security 5e1924464368f0c5816ee84e000cc47017f44045140feafbbc9e685d847ed5a5 This package was compromised as part of the "Mini Shai-Hulud is back" worm by the TeamPCP threat actor. The package will steal credentials...

MAL-2026-3602 Malicious code in @ml-toolkit-ts/xgboost (npm)

--- -= Per source details. Do not edit below this line.=- Source: google-open-source-security 5e1924464368f0c5816ee84e000cc47017f44045140feafbbc9e685d847ed5a5 This package was compromised as part of the "Mini Shai-Hulud is back" worm by the TeamPCP threat actor. The package will steal credentials...

Hybrid Ensemble Method for Detecting Cyber-Attacks in Water Distribution Systems Using the BATADAL Dataset

The cybersecurity of Industrial Control Systems that manage critical infrastructure such as Water Distribution Systems has become increasingly important as digital connectivity expands. BATADAL benchmark data is a good source of testing intrusion detection techniques, but it presents several...

A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data

Among the various types of cyberattacks, identifying zero-day attacks is problematic because they are unknown to security systems as their pattern and characteristics do not match known blacklisted attacks. There are many Machine Learning ML models designed to analyze and detect network attacks,...

Securing IoT Communications Via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method

The rapid growth of the Internet of Things IoT has transformed industries by enabling seamless data exchange among connected devices. However, IoT networks remain vulnerable to security threats such as denial of service DoS attacks, anomalous traffic, and data manipulation due to decentralized...

Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks

The increase of IoT devices, driven by advancements in hardware technologies, has led to widespread deployment in large-scale networks that process massive amounts of data daily. However, the reliance on Edge Computing to manage these devices has introduced significant security vulnerabilities, a...

On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0

Healthcare 5.0 integrates Artificial Intelligence AI, the Internet of Things IoT, real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on interconnected medical technologies exposes them to cyber threats...

Optimizing DDoS Detection in SDNs through Machine Learning Models

The emergence of Software-Defined Networking SDN has changed the network structure by separating the control plane from the data plane. However, this innovation has also increased susceptibility to DDoS attacks. Existing detection techniques are often ineffective due to data imbalance and accurac...

Machine Learning-Based Detection of DDoS Attacks in VANETs for Emergency Vehicle Communication

Vehicular Ad Hoc Networks VANETs play a key role in Intelligent Transportation Systems ITS, particularly in enabling real-time communication for emergency vehicles. However, Distributed Denial of Service DDoS attacks, which interfere with safety-critical communication channels, can severely impai...

Bilateral Differentially Private Vertical Federated Boosted Decision Trees

Federated learning is a distributed machine learning paradigm that enables collaborative training across multiple parties while ensuring data privacy. Gradient Boosting Decision Trees GBDT, such as XGBoost, have gained popularity due to their high performance and strong interpretability. Therefor...