Literatura académica sobre el tema "Light-based Intrusion classification system"
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Artículos de revistas sobre el tema "Light-based Intrusion classification system"
Jecheva, Veselina y Evgeniya Nikolova. "Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems". Serdica Journal of Computing 3, n.º 4 (11 de enero de 2010): 335–58. http://dx.doi.org/10.55630/sjc.2009.3.335-358.
Texto completoSandosh, S., Dr V. Govindasamy y Dr G. Akila. "Novel Pattern Matching based Alert Classification Approach For Intrusion Detection System". Journal of Advanced Research in Dynamical and Control Systems 11, n.º 11-SPECIAL ISSUE (29 de noviembre de 2019): 279–89. http://dx.doi.org/10.5373/jardcs/v11sp11/20193032.
Texto completoKamble, Arvind y Virendra S. Malemath. "Adam Improved Rider Optimization-Based Deep Recurrent Neural Network for the Intrusion Detection in Cyber Physical Systems". International Journal of Swarm Intelligence Research 13, n.º 3 (1 de julio de 2022): 1–22. http://dx.doi.org/10.4018/ijsir.304402.
Texto completoAhmad, Iftikhar, Qazi Emad Ul Haq, Muhammad Imran, Madini O. Alassafi y Rayed A. AlGhamdi. "An Efficient Network Intrusion Detection and Classification System". Mathematics 10, n.º 3 (8 de febrero de 2022): 530. http://dx.doi.org/10.3390/math10030530.
Texto completoMohammed, Bilal y Ekhlas K. Gbashi. "Intrusion Detection System for NSL-KDD Dataset Based on Deep Learning and Recursive Feature Elimination". Engineering and Technology Journal 39, n.º 7 (25 de julio de 2021): 1069–79. http://dx.doi.org/10.30684/etj.v39i7.1695.
Texto completoAli, Rashid y Supriya Kamthania. "A Comparative Study of Different Relevant Features Hybrid Neural Networks Based Intrusion Detection Systems". Advanced Materials Research 403-408 (noviembre de 2011): 4703–10. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4703.
Texto completoUgendhar, A., Babu Illuri, Sridhar Reddy Vulapula, Marepalli Radha, Sukanya K, Fayadh Alenezi, Sara A. Althubiti y Kemal Polat. "A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification". Mathematical Problems in Engineering 2022 (6 de mayo de 2022): 1–10. http://dx.doi.org/10.1155/2022/8030510.
Texto completoAfzal, Shehroz y Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas". STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, n.º 2 (31 de diciembre de 2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.
Texto completoAfzal, Shehroz y Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas". STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, n.º 2 (31 de diciembre de 2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.
Texto completoAlzahrani, Mohammed Saeed y Fawaz Waselallah Alsaade. "Computational Intelligence Approaches in Developing Cyberattack Detection System". Computational Intelligence and Neuroscience 2022 (18 de marzo de 2022): 1–16. http://dx.doi.org/10.1155/2022/4705325.
Texto completoTesis sobre el tema "Light-based Intrusion classification system"
Lee, Keum-Chang. "Design of an intrusion detection system based on a fuzzy classification and voting approach". Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506587.
Texto completoAl, Tobi Amjad Mohamed. "Anomaly-based network intrusion detection enhancement by prediction threshold adaptation of binary classification models". Thesis, University of St Andrews, 2018. http://hdl.handle.net/10023/17050.
Texto completoShafi, Kamran Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "An online and adaptive signature-based approach for intrusion detection using learning classifier systems". Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38991.
Texto completoSilva, Eduardo Germano da. "A one-class NIDS for SDN-based SCADA systems". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/164632.
Texto completoPower grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.
Noordhuis-Fairfax, Sarina. "Field | Guide: John Berger and the diagrammatic exploration of place". Phd thesis, Canberra, ACT : The Australian National University, 2018. http://hdl.handle.net/1885/154278.
Texto completoTseng, Hung-Lin y 曾鴻麟. "An Ensemble Based Classification Algorithm for Network Intrusion Detection System". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/16771777095571370354.
Texto completo國防大學理工學院
資訊科學碩士班
99
In the environment of changing information security threats, an intrusion detection system (IDS) is an important line of defense. With the continuous progress of information technology, the network speed and throughput are also increasing. There are hundreds of thousands of packets per second in the network. Taking both information security and network quality into account are a very important issue. In recent years, data mining technology becomes very popular and is applied in various fields successfully. Data mining can discover the useful information from a large volume of data. The current research tends to apply data mining technology in constructing the IDSs. However, many challenges still exist to be overcomed in the field of data mining-based IDSs, such as the imbalanced data sets, poor detection rate of the minority class, and low accuracy rate, etc. Therefore, by integrating the data selection, sampling, and feature selection methods, this thesis proposes an “Enhanced Integrated Learning” algorithm and an “EIL-Algorithm Based Ensemble System” to strengthen the classification model and its performance. This thesis uses KDD99 data set as the experiment data source. A series of experiments are conducted to show that the proposed algorithms can enhance the classification performance of the minority class. For U2R attack class, Recall and F-measure are 57.01% and 38.98%, respectively, which shows the classification performance for U2R attack class is effectively improved. Meanwhile, the overall classification performance of anomaly network-based IDS is enhanced.
HUANG, HUI-YING y 黃蕙嫈. "Classification of Intrusion Detection System Based on Machine Learning Technology". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/pz9b2z.
Texto completoChoubisa, Tarun. "Design, Development, Deployment and Performance Evaluation of Pyroelectric Infra-Red and Optical Camera based Intrusion Detection Systems in an Outdoor Setting". Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5306.
Texto completoWeigert, Stefan. "Community-Based Intrusion Detection". Doctoral thesis, 2015. https://tud.qucosa.de/id/qucosa%3A30127.
Texto completoSaradha, R. "Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting". Thesis, 2014. http://etd.iisc.ac.in/handle/2005/3129.
Texto completoLibros sobre el tema "Light-based Intrusion classification system"
Sabri, Omar y Martin Bircher. Management of limb and pelvic injuries. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0336.
Texto completoCapítulos de libros sobre el tema "Light-based Intrusion classification system"
Chuang, Hsiu-Min, Hui-Ying Huang, Fanpyn Liu y Chung-Hsien Tsai. "Classification of Intrusion Detection System Based on Machine Learning". En Communications in Computer and Information Science, 492–98. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6113-9_55.
Texto completoShin, Moon Sun, Eun Hee Kim y Keun Ho Ryu. "False Alarm Classification Model for Network-Based Intrusion Detection System". En Lecture Notes in Computer Science, 259–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_38.
Texto completoNadiammai, G. V. y M. Hemalatha. "Performance Analysis of Tree Based Classification Algorithms for Intrusion Detection System". En Mining Intelligence and Knowledge Exploration, 82–89. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03844-5_9.
Texto completoWang, Yunpeng, Yuzhou Li, Daxin Tian, Congyu Wang, Wenyang Wang, Rong Hui, Peng Guo y Haijun Zhang. "A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification". En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 581–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72823-0_53.
Texto completoMehrotra, Latika, Prashant Sahai Saxena y Nitika Vats Doohan. "A Data Classification Model: For Effective Classification of Intrusion in an Intrusion Detection System Based on Decision Tree Learning Algorithm". En Information and Communication Technology for Sustainable Development, 61–66. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3932-4_7.
Texto completoShyu, Mei-Ling y Varsha Sainani. "A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification". En Data Mining and Multi-agent Integration, 127–42. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2_8.
Texto completoVitorino, João, Rui Andrade, Isabel Praça, Orlando Sousa y Eva Maia. "A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection". En Foundations and Practice of Security, 191–207. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08147-7_13.
Texto completoAlasad, Qutaiba, Maytham M. Hammood y Shahad Alahmed. "Performance and Complexity Tradeoffs of Feature Selection on Intrusion Detection System-Based Neural Network Classification with High-Dimensional Dataset". En Lecture Notes in Networks and Systems, 533–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25274-7_45.
Texto completoLiu, Fang y Yun Tian. "Intrusion Detection Based on Clustering Organizational Co-Evolutionary Classification". En Fuzzy Systems and Knowledge Discovery, 1113–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_139.
Texto completoNanda, Manas Kumar y Manas Ranjan Patra. "Intrusion Detection and Classification Using Decision Tree-Based Feature Selection Classifiers". En Smart Innovation, Systems and Technologies, 157–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6202-0_17.
Texto completoActas de conferencias sobre el tema "Light-based Intrusion classification system"
Shang-fu, Gong y Zhao Chun-lan. "Intrusion detection system based on classification". En 2012 IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment (ICADE). IEEE, 2012. http://dx.doi.org/10.1109/icade.2012.6330103.
Texto completoBelhor, Mariem y Farah Jemili. "Intrusion detection based on genetic fuzzy classification system". En 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016. http://dx.doi.org/10.1109/aiccsa.2016.7945690.
Texto completoJabbar, M. A., Rajanikanth Aluvalu y S. Sai Satyanarayana Reddy. "Cluster Based Ensemble Classification for Intrusion Detection System". En ICMLC 2017: 2017 the 9th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3055635.3056595.
Texto completoSharma, Rachana, Priyanka Sharma, Preeti Mishra y Emmanuel S. Pilli. "Towards MapReduce based classification approaches for Intrusion Detection". En 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence). IEEE, 2016. http://dx.doi.org/10.1109/confluence.2016.7508144.
Texto completoGupta, Prabhav, Yash Ghatole y Nihal Reddy. "Stacked Autoencoder based Intrusion Detection System using One-Class Classification". En 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377069.
Texto completoOgundokun, Roseline Oluwaseun, Sanjay Misra, Akinbowale Nathaniel Babatunde y Sabarathinam Chockalingam. "Cyber Intrusion Detection System based on Machine Learning Classification Approaches". En 2022 International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2022. http://dx.doi.org/10.1109/icapai55158.2022.9801566.
Texto completoSingh, Abhay Pratap, Sanjeev Kumar, Amit Kumar y Mohd Usama. "Machine Learning based Intrusion Detection System for Minority Attacks Classification". En 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES). IEEE, 2022. http://dx.doi.org/10.1109/cises54857.2022.9844381.
Texto completoEffendy, David Ahmad, Kusrini Kusrini y Sudarmawan Sudarmawan. "Classification of intrusion detection system (IDS) based on computer network". En 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2017. http://dx.doi.org/10.1109/icitisee.2017.8285566.
Texto completoKumar, Sanjay, Ari Viinikainen y Timo Hamalainen. "Machine learning classification model for Network based Intrusion Detection System". En 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 2016. http://dx.doi.org/10.1109/icitst.2016.7856705.
Texto completoSubba, Basant, Santosh Biswas y Sushanta Karmakar. "A Neural Network based system for Intrusion Detection and attack classification". En 2016 Twenty Second National Conference on Communication (NCC). IEEE, 2016. http://dx.doi.org/10.1109/ncc.2016.7561088.
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