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Message classification as a basis for studying command and control communications—an evaluation of machine learning approaches
2011
Journal of Intelligent Information Systems
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Message classification as a basis for studying command and control communication: an evaluation of machine learning approaches, 2011, Journal of Intelligent Information Systems. http://dx.Abstract In military ...
command and control, success relies on being able to perform key functions such as communicating intent. ...
In the following sections, we describe command and control research and the rationale for investigating machine learning approaches for supporting it in Section 2. ...
doi:10.1007/s10844-011-0156-5
fatcat:fb5rei4mv5fcvjl35sxbt3zxly
SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications
2016
PLoS ONE
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As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. ...
The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i. e 99.49% accuracy is achieved. ...
A machine learning based hybrid detection and classification approach is proposed by [35] . The authors built their software using an open-source framework known as CuckooDroid. ...
doi:10.1371/journal.pone.0150077
pmid:26978523
pmcid:PMC4792466
fatcat:f26o4di5tfgstktz2fvdyjblna
An AI-powered Network Threat Detection System
2022
IEEE Access
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The mutual dependencies of features and network threats are evaluated. Results of a performance analysis reveal that the proposed AI@NTDS system has an accuracy of 99.20% and an F1-score of 99.80%. ...
The features contain message-based features for all kinds of Linux operating instructions, host-based features for all types of information in the network connection process, and geography-based features ...
This study presents 14 features that are used in the real-time classification of attacks using machine learning algorithms. Tomas et al. ...
doi:10.1109/access.2022.3175886
fatcat:nz7gkhfawbdrvihaowhrubxmte
A Novel Method for Recognizing Vietnamese Voice Commands on Smartphones with Support Vector Machine and Convolutional Neural Networks
2020
Wireless Communications and Mobile Computing
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We propose a supervised machine-learning approach to address cases in which Google incorrectly identifies voice commands. ...
First, we build a voice command dataset that includes hypotheses of GSR for each corresponding voice command. ...
SVM is a supervised machine-learning method used for classification and regression problems [16] . ...
doi:10.1155/2020/2312908
fatcat:ph244qk5pfgjhakjspzkbsw45a
A Review of Research Work on Network-Based SCADA Intrusion Detection Systems
2020
IEEE Access
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This article aims to contribute to assess the state-ofthe-art, identify the open issues and provide an insight for future study areas. ...
Results of our analysis indicate considerable progress regarding the development of machine learning-based detection methods, implementation platforms, and to some extent, sophisticated testbeds. ...
Machine learning-based techniques establish an explicit or implicit model that allows classification of analyzed patterns. ...
doi:10.1109/access.2020.2994961
fatcat:pid6aq5t7be7hkf4moaym2wqti
CyberPulse: A Machine Learning based Link Flooding Attack Mitigation System for Software Defined Networks
2019
IEEE Access
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CyberPulse was evaluated for its accuracy, false positive rate, and effectiveness as compared to competing approaches on realistic networks generated using Mininet. ...
CyberPulse performs network surveillance by classifying network traffic using deep learning techniques and is implemented as an extension module in the Floodlight controller. ...
Machine Learning (ML) allows machines to learn about the features of a problem using statistical techniques and automate the solution for an arbitrary dataset. ...
doi:10.1109/access.2019.2904236
fatcat:m2p3y4ttbngtxffra6vua4nhnq
Peer to Peer Botnet Detection Based on Flow Intervals
[chapter]
2012
IFIP Advances in Information and Communication Technology
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In this paper we propose an approach to detect botnet activity by classifying network traffic behavior using machine learning classification techniques. ...
We study the feasibility of detecting botnet activity without having seen a complete network flow by classifying behavior based on time intervals and we examine the performance of two popular classification ...
Evaluation results We implemented our framework in Java and utilized the popular Weka machine learning framework and libraries for our classification algorithms [20] . ...
doi:10.1007/978-3-642-30436-1_8
fatcat:puuzvflvcrg7bke67o3wwlw3sa
Automated text-based analysis for decision-making research
2011
Cognition, Technology & Work
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Specifically, we devised and evaluated an analysis tool for C 2 researchers who study simulated decision-making scenarios for command teams. ...
We present results from a study on constructing and evaluating a support tool for the extraction of patterns in distributed decision making processes, based on design criteria elicited from a study on ...
We would like to thank the participants at the Swedish Defense Research Agency and VSL Systems AB for participating in this study and generously providing material used for the scenarios in this article ...
doi:10.1007/s10111-010-0170-3
fatcat:denltud36nha7nduqgdxj6xopq
Collaborative Framework for Early Detection of RAT-Bots Attacks
2019
IEEE Access
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Attackers tend to use Remote Access Trojans (RATs) to compromise and control a targeted computer, which makes the RAT detection as an active research field. ...
This paper introduces a machine learning-based framework for detecting compromised hosts and networks that are infected by the RAT-Bots. ...
ACKNOWLEDGMENT The authors would like to thank the Malware Analysis and Reverse Engineering team in the EG-CERT for providing helpful ideas and the malicious database used in this research. ...
doi:10.1109/access.2019.2919680
fatcat:5lwgpmhmpfcqjd6sshcox4xbje
An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers
2019
Applied Sciences
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To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. ...
Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app9112375
fatcat:s7rkdoabjzbjnpsqlzdpptnuyq
A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms
2022
Discrete Dynamics in Nature and Society
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In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. ...
In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory. ...
See Figure 8 for a comparison of convergence under different training sets. e traffic matrix reflects the traffic load of the whole network and can provide a decision basis for studies such as network ...
doi:10.1155/2022/8998352
fatcat:nzjoighvwnhmveq5fr74krk6ru
Distributed Supervised Sentiment Analysis of Tweets: Integrating Machine Learning and Streaming Analytics for Big Data Challenges in Communication and Audience Research
2019
EMPIRIA: Revista de Metodología de Ciencias Sociales
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Bringing together machine learning and streaming analytics approaches in a distributed environment might help scholars to obtain valuable data from Twitter in order to immediately classify messages depending ...
The large-scale analysis of tweets in real-time using supervised sentiment analysis depicts a unique opportunity for communication and audience research. ...
machine learning. the transition from and the dialogue with conventional Media Studies and Communication research are still pending, as progress in these research approaches is not being leaded by Communication ...
doi:10.5944/empiria.42.2019.23254
fatcat:yft66k3fpbdwlblg6uvfhee7qe
Modeling of Multi-Agent Oriented learning System for Impaired Students with JADE
2012
International Journal of Intelligent Systems and Applications
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The learning system is modeled on the basis of both centralized as well as distributed multi-agent planning. ...
In this research paper we presented a model of multi-agent system based learning environment for physically impaired students. ...
As we stated earlier, agents of this learning system work on the basis of both centralized as well as distributed multi-agent planning for communication, cooperation and negotiation between themselves ...
doi:10.5815/ijisa.2012.12.07
fatcat:gvbtvxzpnvafpc5mbf7ukhygc4
A REVIEW OF PEER-TO-PEER BOTNET DETECTION TECHNIQUES
2014
Journal of Computer Science
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In this study we discuss various P2P botnet detection approaches and evaluate their effectiveness. We identify the advantages and shortcomings of each of the discussed techniques. ...
Botnets have exploited this technology efficiently and introduced the P2P botnet, which uses P2P network for remote control of its bots and become one of the most significant threats to computer networks ...
This research is supported by National Advanced IPv6 Centre of Excellence (NAV6), Universiti Sains Malaysia (USM).Grant title: "A comprehensive botnet mitigation Ecosystem".Acc.No:1001/PNAV/857001. ...
doi:10.3844/jcssp.2014.169.177
fatcat:4quoft4bnrdttosfalen46igmy
Using classifier cascades for scalable e-mail classification
2011
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference on - CEAS '11
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Using this method, we learn a relationship between feature costs and label hierarchies, for granular classification and cost budgets, for load-sensitive classification. ...
In load-sensitive classification, we classify a set of instances within an arbitrary total budget for acquiring features. ...
Acknowledgments This work was partially supported by NSF Grant # IIS-0746930 and AFRL contract # FA8750-10-C-0191. ...
doi:10.1145/2030376.2030383
dblp:conf/ceas/PujaraDG11
fatcat:3whipobtbzge7dcoomsbqyjr4e
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