Livros: "Attacks detection"

1

Dübendorfer, Thomas P. Impact analysis, early detection, and mitigation of large-scale Internet attacks. Aachen: Shaker, 2005.

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2

Li, Beibei, Rongxing Lu e Gaoxi Xiao. Detection of False Data Injection Attacks in Smart Grid Cyber-Physical Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58672-0.

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3

Raghavan, S. V., e E. Dawson, eds. An Investigation into the Detection and Mitigation of Denial of Service (DoS) Attacks. India: Springer India, 2011. http://dx.doi.org/10.1007/978-81-322-0277-6.

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4

K, Kokula Krishna Hari, ed. Early Detection and Prevention of Vampire Attacks in Wireless Sensor Networks: ICIEMS 2014. India: Association of Scientists, Developers and Faculties, 2014.

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5

Casola, Linda, e Dionna Ali, eds. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies. Washington, D.C.: National Academies Press, 2019. http://dx.doi.org/10.17226/25534.

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6

Raghavan, S. V. An Investigation into the Detection and Mitigation of Denial of Service (DoS) Attacks: Critical Information Infrastructure Protection. India: Springer India Pvt. Ltd., 2011.

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7

Nelson A. Rockefeller Institute of Government., ed. The role of "home" in homeland security: The prevention and detection of terrorist attacks : the challenge for state and local government. Albany, N.Y: The Institute, 2003.

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8

National Academy of Sciences (U.S.). Committee on Effectiveness of National Biosurveillance Systems, BioWatch and the Public Health System. BioWatch and public health surveillance: Evaluating systems for the early detection of biological threats. Washington, D.C: National Academies Press, 2011.

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9

Salem, Malek Ben. Towards Effective Masquerade Attack Detection. [New York, N.Y.?]: [publisher not identified], 2012.

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10

Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante e Stan Z. Li. Multi-Modal Face Presentation Attack Detection. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01824-4.

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11

Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante e Stan Z. Li. Advances in Face Presentation Attack Detection. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32906-7.

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12

Stewin, Patrick. Detecting Peripheral-based Attacks on the Host Memory. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13515-1.

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13

Pande, Sagar Dhanraj, e Aditya Khamparia. Networks Attack Detection on 5G Networks using Data Mining Techniques. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003470281.

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14

ill, Brunkus Denise, ed. The snack attack mystery. New York: Scholastic, 1995.

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15

Chevat, Richie. Shark attack. New York: Bantam Books, 1996.

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16

K, kokula Krishna Hari, ed. Detection of Replica Node Attack Based on Hybrid Artificial Immune System Technique: ICCS 2014. Bangkok, Thailand: Association of Scientists, Developers and Faculties, 2014.

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17

Brancik, Kenneth C. Insider computer fraud: An indepth framework for detecting and defending against insider it attacks. Boca Raton: Auerbach Publications, 2007.

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18

Joanne, White, Mountain Pine Beetle Initiative (Canada) e Pacific Forestry Centre, eds. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Victoria, B.C: Canadian Forest Service, Pacific Forestry Centre, 2005.

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19

Yasuda, Anita. The slime attack. North Mankato, Minn: Capstone Stone Arch Books, 2013.

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20

Haberlin, Richard J. Analysis of unattended ground sensors in theater Missile Defense Attack Operations. Monterey, Calif: Naval Postgraduate School, 1997.

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21

Niemann, K. O. Assessment of potential for remote sensing detection of bark beetle-infested areas during green attack: A literature review. Victoria, B.C: Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, 2004.

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22

Bhattacharyya, Dhruba Kumar, e Jugal Kumar Kalita. DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance. Taylor & Francis Group, 2016.

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23

Bhattacharyya, Dhruba Kumar, e Jugal Kumar Kalita. DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance. Taylor & Francis Group, 2016.

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24

Bhattacharyya, Dhruba Kumar, e Jugal Kumar Kalita. DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance. Taylor & Francis Group, 2016.

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25

Brooks, Richard, e Ilker Ozcelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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26

Brooks, Richard, e Ilker Ozcelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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27

Brooks, Richard, e İlker Özçelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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28

Brooks, Richard, e Ilker Ozcelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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29

Brooks, Richard, e Ilker Ozcelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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30

Li, Beibei, Rongxing Lu e Gaoxi Xiao. Detection of False Data Injection Attacks in Smart Grid Cyber-Physical Systems. Springer International Publishing AG, 2020.

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31

Busch, Christoph, Christian Rathgeb, Ruben Vera-Rodriguez e Ruben Tolosana. Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks. Springer International Publishing AG, 2021.

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32

Li, Beibei, Rongxing Lu e Gaoxi Xiao. Detection of False Data Injection Attacks in Smart Grid Cyber-Physical Systems. Springer International Publishing AG, 2021.

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33

Busch, Christoph, Christian Rathgeb, Ruben Vera-Rodriguez e Ruben Tolosana. Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks. Springer International Publishing AG, 2021.

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34

Gunasekhar, T., K. Thirupathi Rao, P. Sai Kiran, V. Krishna Reddy e B. Thirumala Rao. Detection and Mitigation of Insider Attacks in a Cloud Infrastructure: Emerging Research and Opportunities. IGI Global, 2018.

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35

Gunasekhar, T., K. Thirupathi Rao, P. Sai Kiran, V. Krishna Reddy e B. Thirumala Rao. Detection and Mitigation of Insider Attacks in a Cloud Infrastructure: Emerging Research and Opportunities. IGI Global, 2019.

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36

Medicine, Institute of, Board on Chemical Sciences and Technology, Board on Health Sciences Policy, National Research Council e Board on Life Sciences. BioWatch and Public Health Surveillance : Evaluating Systems for the Early Detection of Biological Threats: Abbreviated Version. National Academies Press, 2011.

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37

Raghavan, S. V., e E. Dawson. An Investigation into the Detection and Mitigation of Denial of Service Attacks: Critical Information Infrastructure Protection. Springer, 2011.

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38

Raghavan, S. V., e E. Dawson. An Investigation into the Detection and Mitigation of Denial of Service Attacks: Critical Information Infrastructure Protection. Springer, 2013.

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39

Computer Science and Telecommunications Board, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Intelligence Community Studies Board e Board on Mathematical Sciences and Analytics. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. National Academies Press, 2019.

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40

Computer Science and Telecommunications Board, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Intelligence Community Studies Board e Board on Mathematical Sciences and Analytics. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. National Academies Press, 2019.

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41

Computer Science and Telecommunications Board, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Intelligence Community Studies Board e Board on Mathematical Sciences and Analytics. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. National Academies Press, 2019.

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42

Ruxton, Graeme D., William L. Allen, Thomas N. Sherratt e Michael P. Speed. Avoiding Attack. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199688678.001.0001.

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Avoiding Attack discusses the diversity of mechanisms by which prey avoid predator attacks and explores how such defensive mechanisms have evolved through natural selection. It considers how potential prey avoid detection, how they make themselves unprofitable to attack, how they communicate this status, and how other species have exploited these signals. Using carefully selected examples of camouflage, mimicry, and warning signals drawn from a wide range of species and ecosystems, the authors summarize the latest research into these fascinating adaptations, developing mathematical models where appropriate and making recommendations for future study.This second edition has been extensively rewritten, particularly in the application of modern genetic research techniques which have transformed our recent understanding of adaptations in evolutionary genomics and phylogenetics. The book also employs a more integrated and systematic approach, ensuring that each chapter has a broader focus on the evolutionary and ecological consequences of anti-predator adaptation. The field has grown and developed considerably over the last decade with an explosion of new research literature, making this new edition timely.

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43

Ruxton, Graeme D., William L. Allen, Thomas N. Sherratt e Michael P. Speed. Advertising elusiveness. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199688678.003.0009.

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Elusiveness signals are given by prey during the close approach of a predator, acting to inform the predator that the prey would be difficult to catch and subdue, and, therefore, that an attempt to catch the prey is likely to be unsuccessful. These signals will be restricted to mobile prey that can mount an active response to impending attack and they will not normally be displayed continuously, but instead be triggered by the perception by the prey that they are under imminent risk of attack. There are two different types of such elusiveness signals: 1) pursuit deterrent signals, communicating fleetness or strength to coursing predators, and 2) perception advertisement, communicating detection of a stalking or ambushing predator. Elusiveness signals can only be effective if mounting attacks is expensive to predators in some way. There is also an element of generalization required for elusiveness signals to be effective but, by signalling fleetness, strength, or awareness of the predator, the prey also seek to differentiate themselves from other potential prey. We first document current empirical evidence for elusiveness signalling, before discussing evolutionary considerations—starting with theory on how such signalling might evolve and be maintained—ecological aspects, and co-evolutionary considerations.

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44

Pell, Stephanie K. Systematic Government Access to Private-Sector Data in the United States I. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190685515.003.0008.

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After the September 11 attacks, law enforcement's mission expanded to include, at times even prioritize, the general “prevention, deterrence and disruption” of terrorist attacks, which presumed a new emphasis upon threat detection and identification by analyzing patterns in larger, less specific bodies of information. Indeed, the unprecedented level of “third-party” possession of information inevitably makes the private sector the most reliable and comprehensive source of information available to law enforcement and intelligence agencies alike. This chapter explores the potential applications of systematic government access to data held by third-party private-sector intermediaries that would not be considered public information sources but, rather, data generated based on the role these intermediaries play in facilitating economic and business transactions (including personal business, such as buying groceries or staying at a hotel on vacation).

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45

Li, Stan Z., Escalera, Jun Wan, Guodong Guo e Hugo Jair Escalante. Multi-Modal Face Presentation Attack Detection. Morgan & Claypool Publishers, 2020.

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46

Li, Stan Z., Escalera, Jun Wan, Guodong Guo e Hugo Jair Escalante. Multi-Modal Face Presentation Attack Detection. Morgan & Claypool Publishers, 2020.

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47

Li, Stan Z., Jun Wan, Sergio Escalera, Guodong Guo e Hugo Jair Escalante. Multi-Modal Face Presentation Attack Detection. Morgan & Claypool Publishers, 2020.

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48

Li, Stan Z., Jun Wan, Sergio Escalera, Guodong Guo e Hugo Jair Escalante. Multi-Modal Face Presentation Attack Detection. Springer International Publishing AG, 2020.

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49

Cassiman, David, e Wouter Meersseman. Tyrosinemia Type I. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199972135.003.0013.

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Tyrosinemia type 1 (HT-1) is a rare metabolic disorder affecting degradation pathways of the amino acid tyrosine. HT-1 presents with liver, kidney and/or bone disease and can cause acute porphyria attacks. Biochemical diagnosis is made by measuring raised plasma tyrosine and detection of succinylacetone in urine. Long-term management with diet and nitisinone leads to excellent short term results, but since long term effects are largely unknown, life-long treatment and follow-up for liver malignancy, bone disease and kidney disease seem necessary. HT-1 is treatable by liver transplantation.

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50

Saxe, Joshua, e Hillary Sanders. Malware Data Science: Attack Detection and Attribution. No Starch Press, 2018.

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Livros sobre o tema "Attacks detection"

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