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Статті в журналах з теми "Light-based Intrusion classification system"
Jecheva, Veselina, and Evgeniya Nikolova. "Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems." Serdica Journal of Computing 3, no. 4 (January 11, 2010): 335–58. http://dx.doi.org/10.55630/sjc.2009.3.335-358.
Повний текст джерелаSandosh, S., Dr V. Govindasamy, and Dr G. Akila. "Novel Pattern Matching based Alert Classification Approach For Intrusion Detection System." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (November 29, 2019): 279–89. http://dx.doi.org/10.5373/jardcs/v11sp11/20193032.
Повний текст джерелаKamble, Arvind, and 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, no. 3 (July 1, 2022): 1–22. http://dx.doi.org/10.4018/ijsir.304402.
Повний текст джерелаAhmad, Iftikhar, Qazi Emad Ul Haq, Muhammad Imran, Madini O. Alassafi, and Rayed A. AlGhamdi. "An Efficient Network Intrusion Detection and Classification System." Mathematics 10, no. 3 (February 8, 2022): 530. http://dx.doi.org/10.3390/math10030530.
Повний текст джерелаMohammed, Bilal, and Ekhlas K. Gbashi. "Intrusion Detection System for NSL-KDD Dataset Based on Deep Learning and Recursive Feature Elimination." Engineering and Technology Journal 39, no. 7 (July 25, 2021): 1069–79. http://dx.doi.org/10.30684/etj.v39i7.1695.
Повний текст джерелаAli, Rashid, and Supriya Kamthania. "A Comparative Study of Different Relevant Features Hybrid Neural Networks Based Intrusion Detection Systems." Advanced Materials Research 403-408 (November 2011): 4703–10. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4703.
Повний текст джерелаUgendhar, A., Babu Illuri, Sridhar Reddy Vulapula, Marepalli Radha, Sukanya K, Fayadh Alenezi, Sara A. Althubiti, and Kemal Polat. "A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification." Mathematical Problems in Engineering 2022 (May 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/8030510.
Повний текст джерелаAfzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (December 31, 2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.
Повний текст джерелаAfzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (December 31, 2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.
Повний текст джерелаAlzahrani, Mohammed Saeed, and Fawaz Waselallah Alsaade. "Computational Intelligence Approaches in Developing Cyberattack Detection System." Computational Intelligence and Neuroscience 2022 (March 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/4705325.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаAl, 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.
Повний текст джерелаShafi, 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.
Повний текст джерелаSilva, 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.
Повний текст джерелаPower 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.
Повний текст джерелаTseng, Hung-Lin, and 曾鴻麟. "An Ensemble Based Classification Algorithm for Network Intrusion Detection System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/16771777095571370354.
Повний текст джерела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, and 黃蕙嫈. "Classification of Intrusion Detection System Based on Machine Learning Technology." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/pz9b2z.
Повний текст джерелаChoubisa, 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.
Повний текст джерелаWeigert, Stefan. "Community-Based Intrusion Detection." Doctoral thesis, 2015. https://tud.qucosa.de/id/qucosa%3A30127.
Повний текст джерелаSaradha, 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.
Повний текст джерелаКниги з теми "Light-based Intrusion classification system"
Sabri, Omar, and Martin Bircher. Management of limb and pelvic injuries. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0336.
Повний текст джерелаЧастини книг з теми "Light-based Intrusion classification system"
Chuang, Hsiu-Min, Hui-Ying Huang, Fanpyn Liu, and Chung-Hsien Tsai. "Classification of Intrusion Detection System Based on Machine Learning." In Communications in Computer and Information Science, 492–98. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6113-9_55.
Повний текст джерелаShin, Moon Sun, Eun Hee Kim, and Keun Ho Ryu. "False Alarm Classification Model for Network-Based Intrusion Detection System." In 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.
Повний текст джерелаNadiammai, G. V., and M. Hemalatha. "Performance Analysis of Tree Based Classification Algorithms for Intrusion Detection System." In Mining Intelligence and Knowledge Exploration, 82–89. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03844-5_9.
Повний текст джерелаWang, Yunpeng, Yuzhou Li, Daxin Tian, Congyu Wang, Wenyang Wang, Rong Hui, Peng Guo, and Haijun Zhang. "A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification." In 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.
Повний текст джерелаMehrotra, Latika, Prashant Sahai Saxena, and Nitika Vats Doohan. "A Data Classification Model: For Effective Classification of Intrusion in an Intrusion Detection System Based on Decision Tree Learning Algorithm." In 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.
Повний текст джерелаShyu, Mei-Ling, and Varsha Sainani. "A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification." In 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.
Повний текст джерелаVitorino, João, Rui Andrade, Isabel Praça, Orlando Sousa, and Eva Maia. "A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection." In Foundations and Practice of Security, 191–207. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08147-7_13.
Повний текст джерелаAlasad, Qutaiba, Maytham M. Hammood, and Shahad Alahmed. "Performance and Complexity Tradeoffs of Feature Selection on Intrusion Detection System-Based Neural Network Classification with High-Dimensional Dataset." In 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.
Повний текст джерелаLiu, Fang, and Yun Tian. "Intrusion Detection Based on Clustering Organizational Co-Evolutionary Classification." In Fuzzy Systems and Knowledge Discovery, 1113–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_139.
Повний текст джерелаNanda, Manas Kumar, and Manas Ranjan Patra. "Intrusion Detection and Classification Using Decision Tree-Based Feature Selection Classifiers." In Smart Innovation, Systems and Technologies, 157–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6202-0_17.
Повний текст джерелаТези доповідей конференцій з теми "Light-based Intrusion classification system"
Shang-fu, Gong, and Zhao Chun-lan. "Intrusion detection system based on classification." In 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.
Повний текст джерелаBelhor, Mariem, and Farah Jemili. "Intrusion detection based on genetic fuzzy classification system." In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016. http://dx.doi.org/10.1109/aiccsa.2016.7945690.
Повний текст джерелаJabbar, M. A., Rajanikanth Aluvalu, and S. Sai Satyanarayana Reddy. "Cluster Based Ensemble Classification for Intrusion Detection System." In 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.
Повний текст джерелаSharma, Rachana, Priyanka Sharma, Preeti Mishra, and Emmanuel S. Pilli. "Towards MapReduce based classification approaches for Intrusion Detection." In 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence). IEEE, 2016. http://dx.doi.org/10.1109/confluence.2016.7508144.
Повний текст джерелаGupta, Prabhav, Yash Ghatole, and Nihal Reddy. "Stacked Autoencoder based Intrusion Detection System using One-Class Classification." In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377069.
Повний текст джерелаOgundokun, Roseline Oluwaseun, Sanjay Misra, Akinbowale Nathaniel Babatunde, and Sabarathinam Chockalingam. "Cyber Intrusion Detection System based on Machine Learning Classification Approaches." In 2022 International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2022. http://dx.doi.org/10.1109/icapai55158.2022.9801566.
Повний текст джерелаSingh, Abhay Pratap, Sanjeev Kumar, Amit Kumar, and Mohd Usama. "Machine Learning based Intrusion Detection System for Minority Attacks Classification." In 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES). IEEE, 2022. http://dx.doi.org/10.1109/cises54857.2022.9844381.
Повний текст джерелаEffendy, David Ahmad, Kusrini Kusrini, and Sudarmawan Sudarmawan. "Classification of intrusion detection system (IDS) based on computer network." In 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2017. http://dx.doi.org/10.1109/icitisee.2017.8285566.
Повний текст джерелаKumar, Sanjay, Ari Viinikainen, and Timo Hamalainen. "Machine learning classification model for Network based Intrusion Detection System." In 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 2016. http://dx.doi.org/10.1109/icitst.2016.7856705.
Повний текст джерелаSubba, Basant, Santosh Biswas, and Sushanta Karmakar. "A Neural Network based system for Intrusion Detection and attack classification." In 2016 Twenty Second National Conference on Communication (NCC). IEEE, 2016. http://dx.doi.org/10.1109/ncc.2016.7561088.
Повний текст джерела