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MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-12942-2_19openalex-snapshot
Analysis and Evaluation of Dynamic Feature-Based Malware Detection Methods
inferredcites →https://doi.org/10.1007/978-3-030-16946-6_39openalex-snapshot
Malware Detection Based on Opcode Sequence and ResNet
inferredcites →https://doi.org/10.1007/978-3-319-19210-9_10openalex-snapshot
Malware Biodiversity Using Static Analysis
inferredcites →https://doi.org/10.1007/978-3-319-59439-2_4openalex-snapshot
Dynamic Analysis of Malware Using Run-Time Opcodes
inferredcites →https://doi.org/10.1007/978-3-319-59870-3_10openalex-snapshot
A New Malware Classification Approach Based on Malware Dynamic Analysis
inferredcites →https://doi.org/10.1007/978-3-540-89900-6_21openalex-snapshot
Unknown Malcode Detection Using OPCODE Representation
inferredcites →https://doi.org/10.1007/978-3-642-11747-3_3openalex-snapshot
Idea: Opcode-Sequence-Based Malware Detection
inferredcites →https://doi.org/10.1007/978-3-642-33018-6_28openalex-snapshot
OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection
inferredcites →https://doi.org/10.1007/978-981-10-0287-8_52openalex-snapshot
Case Studies on Intelligent Approaches for Static Malware Analysis
inferredcites →https://doi.org/10.1007/s10586-017-1117-8openalex-snapshot
A survey of deep learning-based network anomaly detection
inferredcites →https://doi.org/10.1007/s11416-016-0281-3openalex-snapshot
Impeding behavior-based malware analysis via replacement attacks to malware specifications
inferredcites →https://doi.org/10.1007/s11416-016-0283-1openalex-snapshot
An investigation of byte n-gram features for malware classification
inferredcites →https://doi.org/10.1007/s13369-017-2951-yopenalex-snapshot
Bio-inspired for Features Optimization and Malware Detection
inferredcites →https://doi.org/10.1016/j.cose.2014.10.011openalex-snapshot
A framework for metamorphic malware analysis and real-time detection
inferredcites →https://doi.org/10.1016/j.cose.2015.04.001openalex-snapshot
AMAL: High-fidelity, behavior-based automated malware analysis and classification
inferredcites →https://doi.org/10.1016/j.ins.2016.09.037openalex-snapshot
A three-way decision making approach to malware analysis using probabilistic rough sets
inferredcites →https://doi.org/10.1016/j.procs.2015.02.149openalex-snapshot
Integrated Static and Dynamic Analysis for Malware Detection
inferredcites →https://doi.org/10.1016/s1361-3723(14)70531-7openalex-snapshot
Using feature generation from API calls for malware detection
inferredcites →https://doi.org/10.1109/acsac.2007.21openalex-snapshot
Limits of Static Analysis for Malware Detection
inferredcites →https://doi.org/10.1109/aisp.2012.6313810openalex-snapshot
A miner for malware detection based on API function calls and their arguments
inferredcites →https://doi.org/10.1109/giis.2018.8635598openalex-snapshot
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis
inferredcites →https://doi.org/10.1109/icccc.2016.7496735openalex-snapshot
Proposed classification of malware, based on obfuscation
inferredcites →https://doi.org/10.1109/iciea.2013.6566472openalex-snapshot
Classification of malware families based on N-grams sequential pattern features
inferredcites →https://doi.org/10.1109/iciiecs.2017.8275880openalex-snapshot
Logistic regression for polymorphic malware detection using ANOVA F-test
inferredcites →https://doi.org/10.1109/iscon47742.2019.9036219openalex-snapshot
Malware Detection based on API Calls Frequency
inferredcites →https://doi.org/10.1109/malware.2010.5665796openalex-snapshot
Differentiating malware from cleanware using behavioural analysis
inferredcites →https://doi.org/10.1109/ssci.2017.8285426openalex-snapshot
Malware classification using static analysis based features
inferredcites →https://doi.org/10.1109/trustcom.2015.375openalex-snapshot
Efficient Detection of Zero-day Android Malware Using Normalized Bernoulli Naive Bayes
inferredcites →https://doi.org/10.1109/tse.2005.120openalex-snapshot
A method for detecting obfuscated calls in malicious binaries
inferredcites →https://doi.org/10.1145/1626195.1626251openalex-snapshot
Static CFG analyzer for metamorphic Malware code
inferredcites →https://doi.org/10.1145/1774088.1774303openalex-snapshot
Malware detection based on mining API calls
inferredcites →https://doi.org/10.1145/1858378.1858443openalex-snapshot
Malware detection using assembly code and control flow graph optimization
inferredcites →https://doi.org/10.1145/2089125.2089126openalex-snapshot
A survey on automated dynamic malware-analysis techniques and tools
inferredcites →https://doi.org/10.1145/2875475.2875476openalex-snapshot
Malware Detection Using Dynamic Birthmarks
inferredcites →https://doi.org/10.1145/3022227.3022306openalex-snapshot
Static analysis with paragraph vector for malware detection
inferredcites →https://doi.org/10.1145/3176258.3176335openalex-snapshot
Malware Analysis of Imaged Binary Samples by Convolutional Neural Network with Attention Mechanism
inferredcites →https://doi.org/10.1145/3329786openalex-snapshot
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey
inferredcites →https://doi.org/10.1145/3365001openalex-snapshot
Malware Dynamic Analysis Evasion Techniques
inferredcites →https://doi.org/10.1186/2190-8532-1-1openalex-snapshot
Detecting unknown malicious code by applying classification techniques on OpCode patterns
inferredcites →https://doi.org/10.1587/transinf.e94.d.2150openalex-snapshot
A Novel Malware Clustering Method Using Frequency of Function Call Traces in Parallel Threads
inferredcites →https://doi.org/10.1631/fitee.1601325openalex-snapshot
Automatic malware classification and new malware detection using machine learning
inferredcites →https://doi.org/10.17706/jcp.13.5.545-554openalex-snapshot
A Bit Vector Based Binary Code Comparison Method for Static Malware Analysis
inferredcites →https://doi.org/10.48550/arxiv.1904.02100openalex-snapshot
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis
inferredcites →https://doi.org/10.5072/zenodo.8558openalex-snapshot
Proposal of n-gram Based Algorithm for Malware Classification
inferredcites →https://doi.org/10.5220/0001863603170320openalex-snapshot
N-GRAMS-BASED FILE SIGNATURES FOR MALWARE DETECTION
inferredcites →https://doi.org/10.5220/0002895203950399openalex-snapshot
AUTOMATIC BEHAVIOUR-BASED ANALYSIS AND CLASSIFICATION SYSTEM FOR MALWARE DETECTION
inferredcites →https://doi.org/10.5555/1248547.1248646openalex-snapshot
Learning to Detect and Classify Malicious Executables in the Wild
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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MUDROID: Android malware detection and classification based on permission and behavior for autonomous vehicles
inferred← citeshttps://doi.org/10.1007/978-3-031-23095-0_13openalex-snapshot
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Novel Malware Similarity Analysis: Regional Longest Common Substring Algorithm with Elastic Suffix Tree
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Impact of Machine Learning in Cybersecurity: A Critical Review with Insights on the Internet of Things
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“I see models being a whole other thing”: an empirical study of pre-trained model naming conventions and a tool for enhancing naming consistency
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Antivirus solution to IoT malware detection with authorial next-generation sandbox
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GBADroid: an Android malware detection method based on multi-view feature fusion
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Antimalware applied to IoT malware detection based on softcore processor endowed with authorial sandbox
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Optimized feature representation and selection for malware detection using portable executable headers and machine learning
inferred← citeshttps://doi.org/10.1007/s13748-026-00473-5openalex-snapshot
Machine intelligence approaches for preventing adversarial malware attacks in intrusion detection systems: A systematic review
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Malware Detection in Embedded Devices Using Artificial Hardware Immunity
inferred← citeshttps://doi.org/10.1007/s41870-024-02175-4openalex-snapshot
Statistical cryptanalysis of seven classical lightweight ciphers
inferred← citeshttps://doi.org/10.1007/s42979-024-03454-4openalex-snapshot
Feature Selection Using Particle Swarm Optimization and Ensemble-Based Machine Learning Models for Ransomware Detection
inferred← citeshttps://doi.org/10.1007/s44379-024-00010-yopenalex-snapshot
Elucidation on the performance of various machine learning models for real-time malware detection, malware classification and network packet screening
inferred← citeshttps://doi.org/10.1016/j.comnet.2021.108008openalex-snapshot
SPEAR SIEM: A Security Information and Event Management system for the Smart Grid
inferred← citeshttps://doi.org/10.1016/j.comnet.2021.108595openalex-snapshot
Distinguishing malicious programs based on visualization and hybrid learning algorithms
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2022.107703openalex-snapshot
Sequential opcode embedding-based malware detection method
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109414openalex-snapshot
CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118073openalex-snapshot
Identification of malware families using stacking of textural features and machine learning
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Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms
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Radon transform based malware classification in cyber-physical system using deep learning
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Deep Learning and Regularization Algorithms for Malicious Code Classification
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Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions
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Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning
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From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques
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Increasing the Accuracy of Malware Detection in PE Files Using Advanced Machine-Learning Techniques
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Intelligent Android Malware Detection in Real-World Environments Using Machine and Deep Learning Models
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Malware Feature Extraction of the Alibaba Cloud Malware Dataset via Machine Learning Algorithms
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Toward an Ensemble Behavioral-based Early Evasive Malware Detection Framework
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