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Incorporating Privileged Information to Unsupervised Anomaly Detection [article]

Shubhranshu Shekhar, Leman Akoglu
<span title="2018-05-24">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our ideas build on the Learning Using Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [19,17], which we extend to unsupervised learning and in particular to anomaly detection.  ...  Through extensive experiments on simulated and real datasets, we show that augmenting privileged information to anomaly detection significantly improves detection performance.  ...  While anomaly detection has been studied extensively [1] , we are unaware of any work other than [2] leveraging privileged information for unsupervised anomaly detection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.02269v2">arXiv:1805.02269v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6pucup5iazdc3l3k2wrzqfjgla">fatcat:6pucup5iazdc3l3k2wrzqfjgla</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191022022737/https://arxiv.org/pdf/1805.02269v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/39/2c/392cdbefc9e2f66bd724ca783fbd8c9268d39d77.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.02269v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

NIDS For Unsupervised Authentication Records of KDD Dataset in MATLAB

Bhawana Pillai, Uday Pratap
<span title="">2011</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
It is important problems to increase the detection rates and reduce false positive rates in Intrusion Detection System.  ...  Intrusions are detected by determining outliers related to the built patterns. We present the modification on the outlier detection algorithm.  ...  To overcome the limitations of supervised anomaly based systems, a number of IDS employ unsupervised approaches. Unsupervised anomaly detection does not need attack-free training data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/specialissue.2011.010209">doi:10.14569/specialissue.2011.010209</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pik3xrpn4fgfvdzm4jfh4fyzh4">fatcat:pik3xrpn4fgfvdzm4jfh4fyzh4</a> </span>
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Study of Data Mining Based Approaches For Network Intrusion Detection System

Neeraj Shukla, Nagendra Yadav
<span title="2015-02-11">2015</span> <i title="SMS Institute of Technology"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zqjuvwzkf5gohcflbkwgpxdxjm" style="color: black;">SAMRIDDHI A Journal of Physical Sciences Engineering and Technology</a> </i> &nbsp;
Intrusion can be defined as "a set of actions that aim to compromise the integrity, confidentiality or availability of a resource," for example, illegally obtain superuser privileges to attack and make  ...  The purpose of this document is to provide a study of some works that use data mining techniques to detect intrusions and answer some technical questions.  ...  Flowbased anomaly detection tends to rely on existing network elements, such as routers and switches, to make a flow of information available for analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18090/samriddhi.v7i2.8632">doi:10.18090/samriddhi.v7i2.8632</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uekc7fe5ljdobpmofnuz7clol4">fatcat:uekc7fe5ljdobpmofnuz7clol4</a> </span>
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Unsupervised Algorithms to Detect Zero-Day Attacks: Strategy and Application

Tommaso Zoppi, Andrea Ceccarelli, Andrea Bondavalli
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
UNSUPERVISED ANOMALY DETECTION A.  ...  A TOOL FOR UNSUPERVISED ANOMALY DETECTION The paramount importance of tools for the evaluation and comparison of unsupervised anomaly detection algorithms is intuitive.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3090957">doi:10.1109/access.2021.3090957</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zg3vagumlffvbei4g4rj3h7knu">fatcat:zg3vagumlffvbei4g4rj3h7knu</a> </span>
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A Survey of Intrusion Detection Systems Leveraging Host Data [article]

Tarrah R. Glass-Vanderlan and Michael D. Iannacone and Maria S. Vincent and Qian Chen and Robert A. Bridges
<span title="2018-05-17">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network.  ...  Similarly, a section surveying algorithmic developments that are applicable to HIDS but tested on network data sets is included, as this is a large and growing area of applicable literature.  ...  The authors also thank Kerry Long for fruitful conversations contributing to this work's scope and direction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.06070v2">arXiv:1805.06070v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jrihtcjmhjgdbl4kkqmbgyiqs4">fatcat:jrihtcjmhjgdbl4kkqmbgyiqs4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191024034454/https://arxiv.org/pdf/1805.06070v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e5/8b/e58b22470f96bde7bcca5387775da5256ea4d53b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.06070v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Insider threats and Insider Intrusion Detection

<span title="2019-08-29">2019</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3sfifsouvjgadp4gfj54u3z2ku" style="color: black;">International journal of recent technology and engineering</a> </i> &nbsp;
This paper pays particular attention to the categories of threats and different types of detection methods.  ...  Insider threats are emerging nowadays, it is important to identify these threats as they are generating critical problems to the system.  ...  Another important part of the unsupervised anomaly detection is feature selection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijrte.b1033.0782s519">doi:10.35940/ijrte.b1033.0782s519</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/unx2kk3asvcc3j5x4y46wsbgq4">fatcat:unx2kk3asvcc3j5x4y46wsbgq4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200209082028/https://www.ijrte.org/wp-content/uploads/papers/v8i2S5/B10330682S519.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/14/88/1488bc847fb7ced22ac0379f6e2f8f4de1faa63a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijrte.b1033.0782s519"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

A Hybrid Intrusion Detection Model for Identification of Threats in Internet of Things Environment

Nsikak Pius Owoh, Manmeet Mahinderjit Singh, Zarul Fitril Zaaba
<span title="">2021</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
Intrusion Detection Systems (IDS) that employ unsupervised learning techniques are used to secure sensitive data transmitted on IoT networks and preserve privacy.  ...  This paper proposes a hybrid model for intrusion detection that relies on a dimension reduction algorithm, an unsupervised learning algorithm, and a classifier.  ...  Considering this, we propose an anomaly detection system that employs an unsupervised learning technique with a classifier capable of detecting up to four classes of attacks present in the NSL-KDD dataset  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2021.0120976">doi:10.14569/ijacsa.2021.0120976</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t7xsi33pabcnxoeo7agyqnubli">fatcat:t7xsi33pabcnxoeo7agyqnubli</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211020053215/https://thesai.org/Downloads/Volume12No9/Paper_76-A_Hybrid_Intrusion_Detection_Model_for_Identification_of_Threats.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/5a/855a5c810d474214cde5654051639809434a49d0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2021.0120976"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Some Clustering-Based Methodology Applications to Anomaly Intrusion Detection Systems

Veselina Jecheva, Evgeniya Nikolova
<span title="2016-01-31">2016</span> <i title="Science and Engineering Research Support Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4h7flaxsjnh6he3qbiwvxbekxe" style="color: black;">International Journal of Security and Its Applications</a> </i> &nbsp;
Various approaches have been applied to the anomaly-based intrusion detection during the recent years.  ...  The present paper introduces some clustering-based methodology applications to the anomaly and host-based intrusion detection.  ...  Intrusion Detection Systems (IDS) are essential parts of the security systems, which purpose is to strengthen the security of communication and information systems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14257/ijsia.2016.10.1.20">doi:10.14257/ijsia.2016.10.1.20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dkx2s6oy5ndaznb5wezgmrihma">fatcat:dkx2s6oy5ndaznb5wezgmrihma</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180603051510/http://www.sersc.org/journals/IJSIA/vol10_no1_2016/20.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/98/f5/98f5a4654412c9e637f08c04a10d83fa8f6abcfe.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14257/ijsia.2016.10.1.20"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Adaptive anomaly detection with evolving connectionist systems

Yihua Liao, V. Rao Vemuri, Alejandro Pasos
<span title="">2007</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zpj7dqnpznduhha4bqzx3te2um" style="color: black;">Journal of Network and Computer Applications</a> </i> &nbsp;
In this paper, a new adaptive anomaly detection framework, based on the use of unsupervised evolving connectionist systems, is proposed to address these issues.  ...  In order to be effective in a practical environment, anomaly detection systems have to be capable of online learning and handling concept drift.  ...  Acknowledgments The authors wish to thank Dr. Tom Goldring of NSA for providing us the Windows NT user profiling data set.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jnca.2005.08.005">doi:10.1016/j.jnca.2005.08.005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4woc5j3ssbat7gksaa7mqpldhe">fatcat:4woc5j3ssbat7gksaa7mqpldhe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20100727084101/http://techlab.bu.edu/files/resources/articles_tt/Adaptive%20anomaly%20detection%20with%20evolving%20connectionist%20systems.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9f/c4/9fc441548b981316d9f42cfd27051a09b819dab7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jnca.2005.08.005"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

Randa Aljably, Yuan Tian, Mznah Al-Rodhaan
<span title="2020-07-20">2020</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sdme5pnua5auzcsjgqmqefb66m" style="color: black;">Security and Communication Networks</a> </i> &nbsp;
In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models.  ...  The models could use further information derived from the user's profiles to detect anomalous users.  ...  Also, it integrates the access control model with machine learning supervised and unsupervised anomaly detection, thereby employing multiple stages to detect a user's anomalous behavior. is scheme contributed  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2020/5874935">doi:10.1155/2020/5874935</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zjuxk6t7pnbtpgcorsoaigiwam">fatcat:zjuxk6t7pnbtpgcorsoaigiwam</a> </span>
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An Efficient Hybrid Clustering-PSO Algorithm for Anomaly Intrusion Detection

Hongying Zheng, Meiju Hou, Yu Wang
<span title="2011-12-01">2011</span> <i title="International Academy Publishing (IAP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jr7366lnajgfrhmmgwgrpt3o7u" style="color: black;">Journal of Software</a> </i> &nbsp;
Generally speaking, in anomaly intrusion detection, modeling the normal behavior of activities performed by a user or a program is an important issue.  ...  algorithm is used to optimize the clustering results and obtain the optimal detection result.  ...  Furthermore, the clusters data can be analyzed for more information, i.e. the signature of new attacks. Most unsupervised anomaly detection methods are based on two basic assumptions about data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4304/jsw.6.12.2350-2360">doi:10.4304/jsw.6.12.2350-2360</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o47uwgtmhrgipmlyvcn7ztpv5y">fatcat:o47uwgtmhrgipmlyvcn7ztpv5y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20141228004436/http://ojs.academypublisher.com:80/index.php/jsw/article/viewFile/jsw061223502360/4035" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0e/d9/0ed9f75db0e16b8f136827cfb78d45f7f1bd237e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4304/jsw.6.12.2350-2360"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

A Hierarchical Approach for Advanced Persistent Threat Detection with Attention-Based Graph Neural Networks

Zitong Li, Xiang Cheng, Lixiao Sun, Ji Zhang, Bing Chen
<span title="">2021</span> <i title="Hindawi-Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sdme5pnua5auzcsjgqmqefb66m" style="color: black;">Security and Communication Networks</a> </i> &nbsp;
Currently, more and more researchers begin to focus on graph-based anomaly detection methods that leverage graph data to model normal behaviors and detect outliers for defending against APTs.  ...  Advanced Persistent Threats (APTs) are the most sophisticated attacks for modern information systems.  ...  As such, anomaly detection models incorporating multiple benign patterns are more likely to identify outliers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/9961342">doi:10.1155/2021/9961342</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/b70e89445a8d43de9f51f7c93734a4d8">doaj:b70e89445a8d43de9f51f7c93734a4d8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fmltezip6bf2jiwwr47q4puynq">fatcat:fmltezip6bf2jiwwr47q4puynq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210610040125/https://downloads.hindawi.com/journals/scn/2021/9961342.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/09/2d/092d50f802dcdce76391596cef72186f53ec0e1e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/9961342"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Detecting Unknown Insider Threat Scenarios

William T. Young, Alex Memory, Henry G. Goldberg, Ted E. Senator
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/inod5dqjxfdj3bnjykkklcg7d4" style="color: black;">2014 IEEE Security and Privacy Workshops</a> </i> &nbsp;
This paper reports results from a set of experiments that evaluate an insider threat detection prototype on its ability to detect scenarios that have not previously been seen or contemplated by the developers  ...  We explore factors that contribute to the success of the ensemble method, such as the number and variety of unsupervised detectors and the use of prior knowledge encoded in scenario-based detectors designed  ...  UNSUPERVISED ENSEMBLE-BASED ANOMALY DETECTION A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/spw.2014.42">doi:10.1109/spw.2014.42</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sp/YoungMGS14.html">dblp:conf/sp/YoungMGS14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2d6sfuzx5fa3tc4wttqbw6lb34">fatcat:2d6sfuzx5fa3tc4wttqbw6lb34</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160805214904/http://www.ieee-security.org/TC/SPW2014/papers/5103a277.PDF" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e9/78/e978be0f7a7f78b232094726aff9c64fdcd49a7e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/spw.2014.42"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities [article]

Shuhan Yuan, Xintao Wu
<span title="2020-05-25">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, applying deep learning to further advance the insider threat detection task still faces several limitations, such as lack of labeled data, adaptive attacks.  ...  We then discuss such challenges and suggest future research directions that have the potential to address challenges and further boost the performance of deep learning for insider threat detection.  ...  When no labeled data are available, unsupervised deep anomaly detection is applied to detect anomalies based on intrinsic properties of data samples [31, 68, 69] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.12433v1">arXiv:2005.12433v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bmmog7g47vfmpmzdvd4tqd5v7u">fatcat:bmmog7g47vfmpmzdvd4tqd5v7u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200529024959/https://arxiv.org/pdf/2005.12433v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.12433v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Mahdi Rabbani, Yongli Wang, Reza Khoshkangini, Hamed Jelodar, Ruxin Zhao, Sajjad Bagheri Baba Ahmadi, Seyedvalyallah Ayobi
<span title="2021-04-25">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a> </i> &nbsp;
In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively  ...  Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities.  ...  Acknowledgments: The authors would like to appreciate the anonymous reviewers for their advantageous comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e23050529">doi:10.3390/e23050529</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33923125">pmid:33923125</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d7sfiqhbkzhtre3vtl74eh67qy">fatcat:d7sfiqhbkzhtre3vtl74eh67qy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210503233703/https://res.mdpi.com/d_attachment/entropy/entropy-23-00529/article_deploy/entropy-23-00529-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a4/37/a437404d3bfa749a5bca795198c9722ac734f0da.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e23050529"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>
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