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We could extend the work in the following directions. First, we will build a full fledge system to capture user input as stream using apache flume and store it on the Hadoop distributed file system (HDFS) and then apply our approaches. Next, we will apply MapReduce to calculate edit distance between patterns for a particular user's command sequence data.
Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.
Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.
Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.
The book presents selected papers from the Fifteenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, in conjunction with the Twelfth International Conference on Frontiers of Information Technology, Applications and Tools, held on July 18–20, 2019 in Jilin, China. Featuring the latest research, it provides valuable information on problem solving and applications for engineers in computer science-related fields, and is a valuable reference resource for academics, industry practitioners and students.
This book covers different aspects of real-world applications of optimization algorithms. It provides insights from the Sixth International Conference on Harmony Search, Soft Computing and Applications held at Istanbul University, Turkey, in July 2020. Harmony Search (HS) is one of the most popular metaheuristic algorithms, developed in 2001 by Prof. Joong Hoon Kim and Prof. Zong Woo Geem, that mimics the improvisation process of jazz musicians to seek the best harmony. The book consists of research articles on novel and newly proposed optimization algorithms; the theoretical study of nature-inspired optimization algorithms; numerically established results of nature-inspired optimization algorithms; and real-world applications of optimization algorithms and synthetic benchmarking of optimization algorithms.
This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts, and consists of selected presentations at the 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses.
These proceedings represent the work of researchers participating in the 15th European Conference on Cyber Warfare and Security (ECCWS 2016) which is being hosted this year by the Universitat der Bundeswehr, Munich, Germany on the 7-8 July 2016. ECCWS is a recognised event on the International research conferences calendar and provides a valuable plat-form for individuals to present their research findings, display their work in progress and discuss conceptual and empirical advances in the area of Cyberwar and Cyber Security. It provides an important opportunity for researchers and managers to come together with peers to share their experiences of using the varied and ex-panding range of Cyberwar and Cyber Security research available to them. With an initial submission of 110 abstracts, after the double blind, peer review process there are 37 Academic research papers and 11 PhD research papers, 1 Master's research paper, 2 Work In Progress papers and 2 non-academic papers published in these Conference Proceedings. These papers come from many different coun-tries including Austria, Belgium, Canada, Czech Republic, Finland, France, Germany, Greece, Hungary, Ireland, Kenya, Luxembourg, Netherlands, Norway, Portugal, Romania, Russia, Slovenia, South Africa, Sweden, Turkey, UK and USA. This is not only highlighting the international character of the conference, but is also promising very interesting discussions based on the broad treasure trove of experience of our community and partici-pants."
This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
Insider Threat: Detection, Mitigation, Deterrence and Prevention presents a set of solutions to address the increase in cases of insider threat. This includes espionage, embezzlement, sabotage, fraud, intellectual property theft, and research and development theft from current or former employees. This book outlines a step-by-step path for developing an insider threat program within any organization, focusing on management and employee engagement, as well as ethical, legal, and privacy concerns. In addition, it includes tactics on how to collect, correlate, and visualize potential risk indicators into a seamless system for protecting an organization’s critical assets from malicious, complacent, and ignorant insiders. Insider Threat presents robust mitigation strategies that will interrupt the forward motion of a potential insider who intends to do harm to a company or its employees, as well as an understanding of supply chain risk and cyber security, as they relate to insider threat. Offers an ideal resource for executives and managers who want the latest information available on protecting their organization’s assets from this growing threat Shows how departments across an entire organization can bring disparate, but related, information together to promote the early identification of insider threats Provides an in-depth explanation of mitigating supply chain risk Outlines progressive approaches to cyber security