Relevant bibliographies by topics / Online Intrusion

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Online Intrusion'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Online Intrusion"

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Chueh, Hao-En, Shun-Chuan Ho, Shih-Peng Chang, and Ping-Yu Hsu. "Online Intrusion Behaviors: Sequences and Time Intervals." Social Behavior and Personality: an international journal 38, no. 10 (2010): 1307–12. http://dx.doi.org/10.2224/sbp.2010.38.10.1307.

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In this study we model the sequences and time intervals of online intrusion behaviors. To maintain network security, intrusion detection systems monitor network environments; however, most existing intrusion detection systems produce too many intrusion alerts, causing network managers to investigate many potential intrusions individually to determine their validity. To solve this problem, we combined a clustering analysis of the time intervals of online users' behaviors with a sequential pattern analysis to identify genuine intrusion behaviors. Knowledge of the patterns generated by intruder b
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Zhang, Kai, Shoushan Luo, Yang Xin, Hongliang Zhu, and Yuling Chen. "Online Mining Intrusion Patterns from IDS Alerts." Applied Sciences 10, no. 8 (2020): 2983. http://dx.doi.org/10.3390/app10082983.

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The intrusion detection system (IDS) which is used widely in enterprises, has produced a large number of logs named alerts, from which the intrusion patterns can be mined. These patterns can be used to construct the intrusion scenarios or discover the final objectives of the malicious actors, and even assist the forensic works of network crimes. In this paper, a novel algorithm for the intrusion pattern mining is proposed which aimsto solve the difficult problems of the intrusion action sequence such as the loss of important intrusion actions, the disorder of the action sequence and the random
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Zhang, Kai, Fei Zhao, Shoushan Luo, Yang Xin, Hongliang Zhu, and Yuling Chen. "Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)." Applied Sciences 10, no. 7 (2020): 2596. http://dx.doi.org/10.3390/app10072596.

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With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. Th
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Chaulk, Kasey, and Tim Jones. "Online Obsessive Relational Intrusion: Further Concerns About Facebook." Journal of Family Violence 26, no. 4 (2011): 245–54. http://dx.doi.org/10.1007/s10896-011-9360-x.

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ENGEL, Raquel, Maria Alice Altenburg de ASSIS, Adriana Soares LOBO, Danielle Biazzi LEAL, Francilene Gracieli Kunradi VIEIRA, and Patrícia Faria DI PIETRO. "Validation of the Online version of the Previous Day Food Questionnaire for schoolchildren." Revista de Nutrição 30, no. 5 (2017): 627–37. http://dx.doi.org/10.1590/1678-98652017000500008.

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ABSTRACT Objective To evaluate the validity of the web-based version of the Previous Day Food Questionnaire Online for schoolchildren from the 2nd to 5th grades of elementary school. Methods Participants were 312 schoolchildren aged 7 to 12 years of a public school from the city of Florianópolis, Santa Catarina, Brazil. Validity was assessed by sensitivity, specificity, as well as by agreement rates (match, omission, and intrusion rates) of food items reported by children on the Previous Day Food Questionnaire Online, using direct observation of foods/beverages eaten during school meals (mid-m
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Gaikwad, D. P., and R. C. Thool. "Online Anomaly Based Intrusion Detection System Using Machine Learning." i-manager's Journal on Cloud Computing 1, no. 1 (2014): 19–25. http://dx.doi.org/10.26634/jcc.1.1.2800.

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Salih Aref, Ismael, Ziyad Tariq Mustafa, Iraq Ali Hussain, and Samah Jalil Sabaa. "Online Intrusion Detection System using C4.5 Algorithm with Honeypot." Journal of Engineering and Applied Sciences 15, no. 5 (2019): 1127–32. http://dx.doi.org/10.36478/jeasci.2020.1127.1132.

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yesugade, Prof K. D. "Online Intrusion Detection System for Wide Range of Attack." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 3996–98. http://dx.doi.org/10.22214/ijraset.2018.4659.

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Hofmann, Alexander, and Bernhard Sick. "Online Intrusion Alert Aggregation with Generative Data Stream Modeling." IEEE Transactions on Dependable and Secure Computing 8, no. 2 (2011): 282–94. http://dx.doi.org/10.1109/tdsc.2009.36.

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Jumaa, Alaa, and Awezan Omar. "Online Database Intrusion Detection System Based on Query Signatures." Journal of University of Human Development 3, no. 1 (2017): 282–87. http://dx.doi.org/10.21928/juhd.20170315.14.

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Dissertations / Theses on the topic "Online Intrusion"

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Ahmed, Abdulbasit. "Online network intrusion detection system using temporal logic and stream data processing." Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/12153/.

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These days, the world is becoming more interconnected, and the Internet has dominated the ways to communicate or to do business. Network security measures must be taken to protect the organization environment. Among these security measures are the intrusion detection systems. These systems aim to detect the actions that attempt to compromise the confidentiality, availability, and integrity of a resource by monitoring the events occurring in computer systems and/or networks. The increasing amounts of data that are transmitted at higher and higher speed networks created a challenging problem for
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Shafi, Kamran Information Technology &amp 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.

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This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. The two major criticisms of the signature based intrusion detection systems are their i) reliance on domain experts to handcraft intrusion signatures and ii) inability to detect previously unknown attacks or the attacks for which no signatures are available at the time. In this thesis, we present a biologically-inspired computational approach to address these two issues. This is done by adaptively learning maximally general rules, whic
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Al, Rawashdeh Khaled. "Toward a Hardware-assisted Online Intrusion Detection System Based on Deep Learning Algorithms for Resource-Limited Embedded Systems." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535464571843315.

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Norton, Aaron Michael. "Technology mediated communication in intimate relationships." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18126.

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Doctor of Philosophy<br>Department of Family Studies and Human Services<br>Joyce Baptist<br>Very little research has been conducted to understand how the technology revolution has changed and impacted couple relationships. The proposed study examined the impact of technology on couples in committed relationships through the lens of the couple and technology framework. Specifically, this study used data from 2,826 European couples to examine associations between online boundary crossing, online intrusion, relationship satisfaction, and partner responsiveness. The results suggest that when parti
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Chen, Bo. "Byzantine Fault Tolerance for Nondeterministic Applications." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1231600826.

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Damour, Gabriel. "Information-Theoretic Framework for Network Anomaly Detection: Enabling online application of statistical learning models to high-speed traffic." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252560.

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With the current proliferation of cyber attacks, safeguarding internet facing assets from network intrusions, is becoming a vital task in our increasingly digitalised economies. Although recent successes of machine learning (ML) models bode the dawn of a new generation of intrusion detection systems (IDS); current solutions struggle to implement these in an efficient manner, leaving many IDSs to rely on rule-based techniques. In this paper we begin by reviewing the different approaches to feature construction and attack source identification employed in such applications. We refer to these ste
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Kim, Dae Wook. "Data-Driven Network-Centric Threat Assessment." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495191891086814.

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Wallin, Clas, and Sara Wisseng. "Konsten att förmedla online-annonser : En studie om vilken typ av online-annonser som används för att nå ut till internetanvändare på ett mindre påträngande sätt." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-31873.

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Påträngande och störande annonser har blivit alltmer vanligt på internet vilket har medfört att konsumenter fått en alltmer negativ inställning till online-annonser. Därmed har användningen av mjukvaror för blockering av annonser på senare tid blivit alltmer vanligt. Detta har medfört minskningar i reklamintäkter för företag som använder sig av webbaserad marknadsföring, vilket har lagt för handen att de måste anpassa sina online-annonser till konsumenten för att kunna hålla verksamheten lönsam. Syftet med denna studie är att undersöka vilken typ av online-annonser som används för att nå ut ti
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Tokunaga, Robert S. "Cyber-intrusions : strategies of coping with online obsessive relational intrusion." Thesis, 2007. http://hdl.handle.net/10125/20905.

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Ribeiro, Tiago Miguel Pereira. "Online Machine Learning-enabled Network Intrusion Detection." Dissertação, 2021. https://hdl.handle.net/10216/134763.

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Machine Learning (ML) is seeing growing usage on Network Intrusion Detection Systems (NIDS) and allowing for an early detection of novel attacks, a prime example of how this technology may be applied to the field of cybersecurity. However, computational requirements associated with ML might make it difficult to process traffic in real time, particularly when dealing with a large volume of data or a high throughput connection, undermining the NIDS' usefulness or its detection capabilities. The goal of this dissertation is to implement an IDS capable of detecting malware command and control traf
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Books on the topic "Online Intrusion"

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Wacks, Raymond. 1. Privacy in peril. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780198725947.003.0001.

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Electronic and aerial surveillance, biometrics, closed circuit TV (CCTV), identity cards, radio frequency identification (RFID) codes, online security, encryption, the Google ‘right to be forgotten’ controversy, interception of email, the monitoring of employees, DNA, cloning, stem cell research, the ‘war on terror’—to mention only a few—all raise fundamental questions about privacy. Reports of the fragility of ‘privacy’ have, of course, been sounded for at least a century. In respect of the future of ‘privacy‘, there can be little doubt that the questions are changing before our eyes. And if,
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Book chapters on the topic "Online Intrusion"

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DeCusatis, C., P. Liengtiraphan, and A. Sager. "Advanced Intrusion Prevention for Geographically Dispersed Higher Education Cloud Networks." In Online Engineering & Internet of Things. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64352-6_13.

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Gu, Chengjie, Shunyi Zhang, and Hanhua Lu. "Online Internet Intrusion Detection Based on Flow Statistical Characteristics." In Knowledge Science, Engineering and Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25975-3_15.

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Rahman, Ahmedur, C. I. Ezeife, and A. K. Aggarwal. "WiFi Miner: An Online Apriori-Infrequent Based Wireless Intrusion System." In Knowledge Discovery from Sensor Data. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12519-5_5.

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Mu, C. P., X. J. Li, H. K. Huang, and S. F. Tian. "Online Risk Assessment of Intrusion Scenarios Using D-S Evidence Theory." In Computer Security - ESORICS 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88313-5_3.

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Yazdani, Hossein, and Kazimierz Choroś. "Intrusion Detection and Risk Evaluation in Online Transactions Using Partitioning Methods." In Cryptology and Network Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98678-4_21.

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Zonouz, Saman Aliari, Kaustubh R. Joshi, and William H. Sanders. "FloGuard: Cost-Aware Systemwide Intrusion Defense via Online Forensics and On-Demand IDS Deployment." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24270-0_25.

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Xu, Haitao, Haining Wang, and Angelos Stavrou. "Privacy Risk Assessment on Online Photos." In Research in Attacks, Intrusions, and Defenses. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26362-5_20.

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Mahmood, Shah, and Yvo Desmedt. "Online Social Networks, a Criminals Multipurpose Toolbox (Poster Abstract)." In Research in Attacks, Intrusions, and Defenses. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33338-5_19.

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Ren, Hanli, Natalia Stakhanova, and Ali A. Ghorbani. "An Online Adaptive Approach to Alert Correlation." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14215-4_9.

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Chen, Yizheng, Panagiotis Kintis, Manos Antonakakis, et al. "Financial Lower Bounds of Online Advertising Abuse." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40667-1_12.

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Conference papers on the topic "Online Intrusion"

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Villarin, Jan, Xu Chu, and Yi Zou. "METAMORPHIC P-T CONDITION OF A MAFIC META-INTRUSION IN HUNTSVILLE, ONTARIO." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-356756.

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Stanic, Sofija, Aaron A. Mohammed, Anner Paldor, Barret Kurylyk, and Holly Michael. "CLIMATE-DRIVEN SALTWATER INTRUSION DYNAMICS IN A CONFINED SMALL-ISLAND AQUIFER IN ATLANTIC CANADA." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-352038.

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Korin, Ashley, Shafiul Chowdhury, and Donald Hodder. "AQUIFER CHARACTERIZATION AND EVALUATION OF SEA WATER INTRUSION USING HYDROGEOCHEMICAL ANALYSES FOR MALEKULA, VANUATU." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-354185.

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Wachino, Ian, and Samuel J. Hampton. "EXPLORING THE GEOMETRY AND GEOCHEMISTRY OF THE VOLCANIC INTRUSION UNDERLYING URUMAU RIDGE: LYTTELTON, NEW ZEALAND." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-356785.

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Gumus, Fatma, C. Okan Sakar, Zeki Erdem, and Olcay Kursun. "Online Naive Bayes classification for network intrusion detection." In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2014. http://dx.doi.org/10.1109/asonam.2014.6921657.

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Nguyen, Hai Thanh, and Katrin Franke. "Adaptive Intrusion Detection System via online machine learning." In 2012 12th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2012. http://dx.doi.org/10.1109/his.2012.6421346.

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Constantinides, Christos, Stavros Shiaeles, Bogdan Ghita, and Nicholas Kolokotronis. "A Novel Online Incremental Learning Intrusion Prevention System." In 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE, 2019. http://dx.doi.org/10.1109/ntms.2019.8763842.

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Cannady, J. "Applying CMAC-based online learning to intrusion detection." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.861503.

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Castanhel, Gabriel Ruschel, Tiago Heinrich, Fabrício Ceschin, and Carlos A. Maziero. "Sliding Window: The Impact of Trace Size in Anomaly Detection System for Containers Through Machine Learning." In XVIII Escola Regional de Redes de Computadores. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/errc.2020.15203.

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Anomaly intrusion detection in Host-based Intrusion Detection System (HIDS) is a process intended to monitor operations on a host to identify behaviors that differ from a “normal ” system behavior. System call based HIDS uses traces of calls to represent the behavior of a system. Due to the volume of data generated by applications and the operating system, sliding windows are applied in order to asses an online environment, allowing intrusions to be detected in real time while being still executed. The respective study explores the impact that the size of the observation window has on Machine
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Milidragovic, Dejan, Graham T. Nixon, Graham T. Nixon, et al. "REDOX CONTROLS ON CU-PGE-AU MINERALIZATION IN THE POLARIS ALASKAN-TYPE INTRUSION, NORTH-CENTRAL BRITISH COLUMBIA." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-357971.

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