001443042 000__ 06164cam\a2200613\i\4500
001443042 001__ 1443042
001443042 003__ OCoLC
001443042 005__ 20230310003523.0
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001443042 008__ 211206s2021\\\\sz\a\\\\ob\\\\000\0\eng\d
001443042 019__ $$a1287674440$$a1287893369$$a1287923153
001443042 020__ $$a9783030747534$$q(electronic book)
001443042 020__ $$a3030747530$$q(electronic book)
001443042 020__ $$z3030747522
001443042 020__ $$z9783030747527
001443042 0247_ $$a10.1007/978-3-030-74753-4$$2doi
001443042 035__ $$aSP(OCoLC)1287616116
001443042 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dAAA$$dN$T$$dGW5XE$$dYDXIT$$dOCLCF$$dEBLCP$$dOCLCO$$dOCLCQ$$dUKAHL$$dOCLCQ
001443042 049__ $$aISEA
001443042 050_4 $$aQA76.9.A25$$bH348 2021
001443042 08204 $$a005.8$$223
001443042 24500 $$aHandbook of big data analytics and forensics /$$cKim-Kwang Raymond Choo, Ali Dehghantanha, editors.
001443042 264_1 $$aCham :$$bSpringer,$$c[2021]
001443042 300__ $$a1 online resource (viii, 287 pages) :$$billustrations
001443042 336__ $$atext$$btxt$$2rdacontent
001443042 337__ $$acomputer$$bc$$2rdamedia
001443042 338__ $$aonline resource$$bcr$$2rdacarrier
001443042 504__ $$aIncludes bibliographical references.
001443042 5050_ $$aBig Data Analytics and Forensics: An Overview -- IoT Privacy, Security and Forensics Challenges: An Unmanned Aerial Vehicle (UAV) Case Study -- Detection of Enumeration Attacks in Cloud Environments Using Infrastructure Log Data -- Cyber Threat Attribution with Multi-View Heuristic Analysis -- Security of Industrial Cyberspace: Fair Clustering with Linear Time Approximation -- Adaptive Neural Trees for Attack Detection in Cyber Physical Systems -- Evaluating Performance of Scalable Fair Clustering Machine Learning Techniques in Detecting Cyber Attacks in Industrial Control Systems -- Fuzzy Bayesian Learning for Cyber Threat Hunting in Industrial Control Systems -- Cyber-Attack Detection in Cyber-Physical Systems Using Supervised Machine Learning -- Evaluation of Scalable Fair Clustering Machine Learning Methods for Threat Hunting in Cyber-Physical Systems -- Evaluation of Supervised and Unsupervised Machine Learning Classifiers for Mac OS Malware Detection -- Evaluation of Machine Learning Algorithms on Internet of Things (IoT) Malware Opcodes -- Mac OS X Malware Detection with Supervised Machine Learning Algorithms -- Machine Learning for OSX Malware Detection -- Hybrid Analysis on Credit Card Fraud Detection Using Machine Learning Techniques -- Mapping CKC Model Through NLP Modelling for APT Groups Reports -- Ransomware Threat Detection: A Deep Learning Approach -- Scalable Fair Clustering Algorithm for Internet of Things Malware Classification.
001443042 506__ $$aAccess limited to authorized users.
001443042 520__ $$aThis handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud's log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter. The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS's cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS's cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated. This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters. This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.
001443042 588__ $$aOnline resource; title from digital title page (viewed on January 07, 2022).
001443042 650_0 $$aComputer security.
001443042 650_0 $$aBig data.
001443042 650_0 $$aDigital forensic science.
001443042 650_0 $$aMachine learning.
001443042 650_0 $$aInternet of things.
001443042 650_0 $$aArtificial intelligence.
001443042 650_6 $$aSécurité informatique.
001443042 650_6 $$aDonnées volumineuses.
001443042 650_6 $$aApprentissage automatique.
001443042 650_6 $$aInternet des objets.
001443042 650_6 $$aIntelligence artificielle.
001443042 655_0 $$aElectronic books.
001443042 7001_ $$aChoo, Kim-Kwang Raymond,$$eeditor.
001443042 7001_ $$aDehghantanha, Ali,$$eeditor.
001443042 77608 $$iPrint version:$$z3030747522$$z9783030747527$$w(OCoLC)1243350113
001443042 852__ $$bebk
001443042 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-74753-4$$zOnline Access$$91397441.1
001443042 909CO $$ooai:library.usi.edu:1443042$$pGLOBAL_SET
001443042 980__ $$aBIB
001443042 980__ $$aEBOOK
001443042 982__ $$aEbook
001443042 983__ $$aOnline
001443042 994__ $$a92$$bISE