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Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review

https://doi.org/10.3390/app132111908

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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GAN-Based Anomaly Detection In Imbalance Problems

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RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods

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A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning methods

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Swarm intelligence in intrusion detection: A survey

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Real-time anomaly detection systems for Denial-of-Service attacks by weighted k-nearest-neighbor classifiers

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OCPAD: One class Naive Bayes classifier for payload based anomaly detection

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Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic

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QoS-based dissemination of content in Grids

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Fault Detection using Random Forest Similarity Distance

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Method for conducting systematic literature review and meta-analysis for environmental science research

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A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects

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Efficient anomaly detection by modeling privilege flows using hidden Markov model

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Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier

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Firefly Algorithm for Structural Search

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A digital evidence fusion method in network forensics systems with Dempster-shafer theory

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Fuzzy logic based anomaly detection for embedded network security cyber sensor

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Secure Offloading of Intrusion Detection Systems from VMs with Intel SGX

inferredcites →https://doi.org/10.1109/glocomw.2015.7414141openalex-snapshot

Network State Awareness and Proactive Anomaly Detection in Self-Organizing Networks

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A machine learning framework for network anomaly detection using SVM and GA

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Flow-Based Anomaly Detection Using Neural Network Optimized with GSA Algorithm

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Adversarially Learned Anomaly Detection

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Anomaly detection using random forest: A performance revisited

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An anomaly detection in smart cities modeled as wireless sensor network

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Anomaly Detection in Hybrid Electric Vehicles Using ANN Based Support Vector Data Description

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Using Random Forests for Network-based Anomaly detection at Active routers

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Fence GAN: Towards Better Anomaly Detection

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An intelligent question answering conversational agent using Naïve Bayesian classifier

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Anomaly detection based on indicators aggregation

inferredcites →https://doi.org/10.1109/mines.2012.146openalex-snapshot

Network Anomaly Detection Using Random Forests and Entropy of Traffic Features

inferredcites →https://doi.org/10.1109/sai.2014.6918242openalex-snapshot

Intrusion detection system using genetic algorithm

inferredcites →https://doi.org/10.1109/spaces.2015.7058223openalex-snapshot

Performance analysis of NSL-KDD dataset using ANN

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Mobile agent-based cross-layer anomaly detection in smart home sensor networks using fuzzy logic

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Anomaly Detection on EtherCAT Based Water Level Control Automation

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Provably Secure Lattice-Based Self-Certified Signature Scheme

inferredcites →https://doi.org/10.22364/bjmc.2018.6.3.01openalex-snapshot

Towards a Robust Method of Dataset Generation of Malicious Activity for Anomaly-Based HIDS Training and Presentation of AWSCTD Dataset

inferredcites →https://doi.org/10.36001/phme.2014.v2i1.1554openalex-snapshot

Anomaly Detection Using Self-Organizing Maps-Based K-Nearest Neighbor Algorithm

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A Survey of Anomaly Detection Techniques and Hidden Markov Model

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Transfer Learning and Ensemble Models for Robust Malware Detection

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The Implementation of Quantum Annealing for Ensemble Pruning

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From code to security: machine learning approaches in android vulnerability detection

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A Self-Adaptive Curriculum and Rare-Class-Aware Training (SA-CRT) Framework for Reliable Modbus/TCP Intrusion Detection

inferred← citeshttps://doi.org/10.1051/e3sconf/202455601008openalex-snapshot

A Comprehensive Review of Android Malware Detection Techniques

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Hashed Nebula: Self-attention with Feature Engineering for Dynamic Malware Detection

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A Machine Learning Framework For Efficient Malware Detection and Classification

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Android Malware Detection: Necessity, Applications and Future Direction

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Hybrid Machine Learning-Based Intrusion Detection System for Cybersecurity in Autonomous Vehicles

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An Interpretable Malware Detection Framework Based on Gradient Boosting and Explainable AI

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Enhancement of Cybersecurity with Machine Learning: Applying Ensemble Classification and Anomaly Detection Approaches to Find Zero-Day Attacks in Network Traffic

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Vision Transformer for Detecting Encrypted Malicious Payloads in Packet Captures

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Developing an Advanced Cross layer Distributed Model for Enhanced Attack Detection Model

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SAMA: A Comprehensive Smart Automated Malware Analyzer Empowered by ChatGPT Integration

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Large Language Model-Driven Automated Malware Detection and Reverse Engineering for Secure Software Systems

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Enhancing malware detection and classification in network traffic using deep learning techniques

inferred← citeshttps://doi.org/10.1145/3736654openalex-snapshot

Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning

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Enhancing Security in Social Networks: A Hybrid Anomaly Detection Approach Using Rf-Svmnb

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AI-Driven Forensics Framework for Reliable Detection of Ransomware

inferred← citeshttps://doi.org/10.3390/app14114645openalex-snapshot

Securing Edge Devices: Malware Classification with Dual-Attention Deep Network

inferred← citeshttps://doi.org/10.3390/app15179830openalex-snapshot

Static and Dynamic Malware Analysis Using CycleGAN Data Augmentation and Deep Learning Techniques

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LLM-Generated Samples for Android Malware Detection

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Explainable Machine Learning for Malware Detection on Android Applications

inferred← citeshttps://doi.org/10.3390/info16060458openalex-snapshot

A Transductive Zero-Shot Learning Framework for Ransomware Detection Using Malware Knowledge Graphs

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Amalgamating Ensemble Machine Learning Soft Voting Classifier, SMOTE, and Pearson's Correlation Coefficient for Enhanced Malware Detection

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Effectiveness of predictive neural network models in improving cyberattack detection and vulnerability assessment in critical infrastructure systems

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