Evaluation of Modified Vector Space Representation Using ADFA-LD and ADFA-WD Datasets

Journal of Information Security

ISSN Print: 2153-1234
ISSN Online: 2153-1242
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"Evaluation of Modified Vector Space Representation Using ADFA-LD and ADFA-WD Datasets"
written by Bhavesh Borisaniya, Dhiren Patel,
published by Journal of Information Security, Vol.6 No.3, 2015

has been cited by the following article(s):

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[1]	Stacking ensemble-based HIDS framework for detecting anomalous system processes in Windows based operating systems using multiple word embedding
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[2]	Machine Learning Methods leveraging ADFA-LD Dataset for Anomaly Detection in Linux Host Systems
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[12]	GAN based Augmentation for Improving Anomaly Detection Accuracy in Host-based Intrusion Detection Systems
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[13]	A new perspective towards the development of robust data-driven intrusion detection for industrial control systems
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[14]	Comprehensive Review of Cybercrime Detection Techniques
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[15]	procmonML: Generating evasion resilient host-based behavioral analytics from tree ensembles
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[16]	A TfidfVectorizer and Singular Value Decomposition based host intrusion detection system framework for detecting anomalous system processes
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[17]	A Novel Deep Intrusion Detection Model Based On a Convolutional Neural Network.
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[18]	A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
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[19]	Data security analysis based on Blockchain Recurrence Qualitative Analysis (BRQA)
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[20]	Deep Learning Approach for Intelligent Intrusion Detection System
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[21]	SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ
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[22]	EVALUATION OF ANOMALY DETECTION APPROACHES USING SYSTEM CALL TRACES ON EMBEDDED LINUX SYSTEMS AND COMPARISON WITH …
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[23]	COMBINATION OF MULTI-CHANNEL CNN AND BiLSTM FOR HOST-BASED INTRUSION DETECTION
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[24]	EagleEye: A Novel Visual Anomaly Detection Method
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[25]	Host-based misuse intrusion detection using PCA feature extraction and kNN classification algorithms
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[26]	Sequence Covering for Efficient Host-Based Intrusion Detection
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[27]	Machine Learning for Host-based Misuse and Anomaly Detection in UNIX Environment
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[28]	Ensemble classifier for misuse detection using N-gram feature vectors through operating system call traces
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[29]	Host based intrusion detection system using frequency analysis of n-gram terms
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[30]	EVALUATING HOST-BASED INTRUSION DETECTION ON THE ADFA-WD AND ADFA-WD: SAA DATASETS
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[31]	Creation of a Dataset Modeling the System Calls of Spectrum Sensors Affected by Malware

[1]	Stacking ensemble-based HIDS framework for detecting anomalous system processes in Windows based operating systems using multiple word embedding Computers & Security, 2023 DOI:10.1016/j.cose.2022.102961

[2]	API-based features representation fusion for malware classification 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), 2023 DOI:10.1109/COMPSAC57700.2023.00256

[3]	Cybersecurity attacks: Which dataset should be used to evaluate an intrusion detection system? Vojnotehnicki glasnik, 2023 DOI:10.5937/vojtehg71-46524

[4]	A tfidfvectorizer and singular value decomposition based host intrusion detection system framework for detecting anomalous system processes Computers & Security, 2021 DOI:10.1016/j.cose.2020.102084

[5]	Intrusion Detection based on Sequential Information preserving Log Embedding Methods and Anomaly Detection Algorithms IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3071763

[6]	Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security Electronics, 2021 DOI:10.3390/electronics10141633

[7]	An Intrusion Detection System on Fog Architecture 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2021 DOI:10.1109/MASS52906.2021.00084

[8]	Comprehensive Review of Cybercrime Detection Techniques IEEE Access, 2020 DOI:10.1109/ACCESS.2020.3011259

[9]	procmonML: Generating evasion resilient host-based behavioral analytics from tree ensembles Computers & Security, 2020 DOI:10.1016/j.cose.2020.102002

[10]	Deep Learning Approach for Intelligent Intrusion Detection System IEEE Access, 2019 DOI:10.1109/ACCESS.2019.2895334

[11]	SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 2019 DOI:10.31796/ogummf.560747

[12]	Sequence Covering for Efficient Host-Based Intrusion Detection IEEE Transactions on Information Forensics and Security, 2019 DOI:10.1109/TIFS.2018.2868614

[13]	A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing Applied Sciences, 2019 DOI:10.3390/app9010178

[14]	SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 2019 DOI:10.31796/ogummf.560747

[15]	EagleEye: A Novel Visual Anomaly Detection Method 2018 16th Annual Conference on Privacy, Security and Trust (PST), 2018 DOI:10.1109/PST.2018.8514179

[16]	Host based intrusion detection system using frequency analysis of n-gram terms TENCON 2017 - 2017 IEEE Region 10 Conference, 2017 DOI:10.1109/TENCON.2017.8228190

[17]	Ensemble classifier for misuse detection using N-gram feature vectors through operating system call traces International Journal of Hybrid Intelligent Systems, 2017 DOI:10.3233/HIS-170247

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