A Comparison of Malware Detection Techniques Based on Hidden Markov Model

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Journal of Information Security

ISSN Print: 2153-1234
ISSN Online: 2153-1242
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"A Comparison of Malware Detection Techniques Based on Hidden Markov Model"
written by Saja Alqurashi, Omar Batarfi,
published by Journal of Information Security, Vol.7 No.3, 2016

has been cited by the following article(s):

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CrossRef

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[4]	Ransomware Taxonomy and Detection Techniques Based on Machine Learning: A Review
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[10]	A Complete Study on Malware Types and Detecting Ransomware Using API Calls
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[11]	Markov Models for Malware and Intrusion Detection: A Survey
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[12]	Comparative Performance Analysis of Anti-virus Software.
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[13]	Malware detection with sequence-based machine learning and deep learning
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[14]	Comparative Performance Analysis of Anti-virus Software
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[15]	Malware Detection: Issues and Challenges
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[16]	The Use2Use Design Toolkit—Tools for User-Centred Circular Design
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[17]	Machine learning in cybersecurity: a comprehensive survey
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[18]	Compound Attack Prediction Method Based On Improved Algorithm Of Hidden Markov Model
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[19]	Stealthy Malware Detection Based on Deep Neural Network
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[20]	Detection of Anomalies in the Information Networks of Industrial Automation Systems Based on Artificial Immune Detectors
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[21]	Теорія та практика створення розподілених систем виявлення зловмисного програмного забезпечення в локальних комп'ютерних мережах
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[23]	An Improved Method for Packed Malware Detection using PE Header and Section Table Information.
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[24]	COMPARATIVE ANALYSIS OF MALWARE DETECTION TECHNIQUES USING SIGNATURE, BEHAVIOUR AND HEURISTICS
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[25]	An Improved Method for Packed Malware Detection using PE Header and Section Table Information
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[26]	An improved DDoS Detection Using Hybrid N-Gram Heuristic Technique
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[27]	A Two-stage Malware Detection Architecture Inspired by Human Immune System
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[28]	DDoS Detection on Network Protocol Using Cosine Similarity and N-Gram+ Method
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[29]	Crypto-Ransomware Detection through the Use of k-Nearest Neighbor Machine Learning Algorithm
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[30]	ОБНАРУЖЕНИЕ ВРЕДОНОСНОГО ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ С ИСПОЛЬЗОВАНИЕМ ИММУННЫХ ДЕТЕКТОРОВ
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[31]	Deep learning for malicious flow detection
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[32]	Proposing an efficient approach for malware clustering
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[33]	Multiple Sequential Data Modeling with Extended Hidden Semi-Markov Models and Its Data Aggregation and Management
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[34]	Malware detection vectors and analysis techniques: A brief survey
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[35]	Hidden markov models in the problem of detecting attacks on computer networks


[36]	Karma öznitelik kullanarak yazılım davranışlarının modellenmesi ve tespiti

[1]	Global Perspectives on the Applications of Computer Vision in Cybersecurity Advances in Information Security, Privacy, and Ethics, 2024 DOI:10.4018/978-1-6684-8127-1.ch007

[2]	A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security Concurrency and Computation: Practice and Experience, 2023 DOI:10.1002/cpe.7517

[3]	A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security Concurrency and Computation: Practice and Experience, 2023 DOI:10.1002/cpe.7517

[4]	Family Classification based on Tree Representations for Malware Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems, 2023 DOI:10.1145/3609510.3609818

[5]	Advanced Communication and Intelligent Systems Communications in Computer and Information Science, 2023 DOI:10.1007/978-3-031-45124-9_11

[6]	Familial Graph Classification of Malware based on Structured API Call Sequences 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C), 2023 DOI:10.1109/QRS-C60940.2023.00072

[7]	Machine learning in cybersecurity: a comprehensive survey The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 2022 DOI:10.1177/1548512920951275

[8]	A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security Concurrency and Computation: Practice and Experience, 2022 DOI:10.1002/cpe.7517

[9]	Machine learning in cybersecurity: a comprehensive survey The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 2022 DOI:10.1177/1548512920951275

[10]	Malware Analysis Using Artificial Intelligence and Deep Learning 2021 DOI:10.1007/978-3-030-62582-5_2

[11]	A Complete Study on Malware Types and Detecting Ransomware Using API Calls 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2021 DOI:10.1109/ICRITO51393.2021.9596085

[12]	Malware Detection: Issues and Challenges Journal of Physics: Conference Series, 2021 DOI:10.1088/1742-6596/1807/1/012011

[13]	Stealthy Malware Detection Based on Deep Neural Network Journal of Physics: Conference Series, 2020 DOI:10.1088/1742-6596/1437/1/012123

[14]	Detection of Anomalies in the Information Networks of Industrial Automation Systems Based on Artificial Immune Detectors 2019 International Russian Automation Conference (RusAutoCon), 2019 DOI:10.1109/RUSAUTOCON.2019.8867593

[15]	DDoS Detection on Network Protocol Using Cosine Similarity and N-Gram+ Method 2018 International Conference on Sustainable Information Engineering and Technology (SIET), 2018 DOI:10.1109/SIET.2018.8693215

[16]	A Two-stage Malware Detection Architecture Inspired by Human Immune System 2018 Cyber Resilience Conference (CRC), 2018 DOI:10.1109/CR.2018.8626867

[17]	Proposing an efficient approach for malware clustering 2017 Artificial Intelligence and Signal Processing Conference (AISP), 2017 DOI:10.1109/AISP.2017.8324094

[18]	Deep learning for malicious flow detection 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017 DOI:10.1109/PIMRC.2017.8292316

[19]	Malware detection vectors and analysis techniques: A brief survey 2016 International Conference on Accessibility to Digital World (ICADW), 2016 DOI:10.1109/ICADW.2016.7942517

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