الصفحة 1
الصفحة 1
Network Classification For Traffic Management : Anomaly detection, feature selection, clustering and classification
Investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks. Investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.
IP operations and management ; 8th IEEE International workshop, IPOM 2008, Samos Island, Greece, September 22-26, 2008. Proceedings
Constitutes the refereed proceedings of the 8th IEEE Workshop on IP Operations and Management, IPOM 2008, held on Samos Island, Greece, on September 22-26, 2008, as part of the 4th International Week on Management of Networks and Services, Manweek 2008. The 12 revised full papers presented in this volume were carefully reviewed and selected from 30 submissions. The papers are organized in topical sections on network anomaly detection; traffic engineering, protection, and recovery; network measurements and applications; and network management and security.
Intelligence and security informatics : Biosurveillance ; 2nd NSF Workshop, BioSurveillance 2007, New Brunswick, NJ, USA, May 22, 2007, Proceedings
The 2007 NSF BioSurveillance Workshop (BioSurveillance 2007) was built on the success of the first NSF BioSurveillance Workshop, hosted by the University of Arizona’s NSF BioPortal Center in March 2006.
Information theory and machine learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges.
Information security applications ; 21st International Conference, WISA 2020, Jeju Island, South Korea, August 26–28, 2020, Revised Selected Papers
This book constitutes the thoroughly refereed proceedings of the 21st International Conference on Information Security Applications, WISA 2020, held in Jeju Island, South Korea, in August 2020. The 30 full research papers included in this book were carefully reviewed and selected from 89 submissions. They are organized in the following topical sections: AI Security and Intrusion Detection; Steganography and Malware; Application, System, and Hardware Security; Cryptography; Advances in Network Security and Attack Defense; and Cyber Security.
Grouping Multidimensional Data : Recent Advances in Clustering
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.
Detection of intrusions and malware, and vulnerability assessment ; 5th International Conference, DIMVA 2008, Paris, France, July 10-11, 2008. Proceedings
This book constitutes the refereed proceedings of the 5th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2008, held in Paris, France in July 2008.
Detection of intrusions and malware, and vulnerability assessment ; 4th International Conference, DIMVA 2007 Lucerne, Switzerland, July 12-13, 2007 Proceedings
This book presented malware; network security, Web security; attacks; Security-Intrusion; Detection and Response
Data science on the Google cloud platform : Implementing end-to-end real-time data pipelines : From ingest to machine learning
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. You'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines
Cyber Security ; 18th China Annual Conference, CNCERT 2021, Beijing, China, July 20–21, 2021, revised selected papers
This book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification.
Managing Virtualization of Networks and Services ; 18th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management, DSOM 2007, San José, CA, USA, October 29-31, 2007, Proceedings
This volume of the Lecture Notes in Computer Science series contains all papers th accepted for presentation at the 18 IFIP/IEEE International Workshop on Distributed Systems: Operations and Management (DSOM 2007), which was held in the heart of Silicon Valley, San Jose, California, USA, on October 29–31, 2007.
Machine Learning Approaches in Cyber Security Analytics
Introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks.
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine Learning and Data Mining for Computer Security : Methods and Applications
Presents research conducted in academia and industry on methods and applications of machine learning and data mining for problems in computer security and will be of interest to researchers and practitioners, as well students.
Artificial immune systems ; 6th International Conference, ICARIS 2007, Santos, Brazil, August 26-29, 2007, Proceedings
This book contains sections on search and optimization, classification and clustering, anomaly detection and negative selection, robotics, control and electronics. Modeling papers, conceptual papers, and technical papers and general applications are also included.
Anti-fragile ICT Systems
Introduces a novel approach to the design and operation of large ICT systems. It views the technical solutions and their stakeholders as complex adaptive systems and argues that traditional risk analyses cannot predict all future incidents with major impacts. To avoid unacceptable events, it is necessary to establish and operate anti-fragile ICT systems that limit the impact of all incidents, and which learn from small-impact incidents how to function increasingly well in changing environments. The book applies four design principles and one operational principle to achieve anti-fragility for different classes of incidents. It discusses how systems can achieve high availability, prevent malware epidemics, and detect anomalies. Analyses of Netflix’s media streaming solution, Norwegian telecom infrastructures, e-government platforms, and Numenta’s anomaly detection software show that cloud computing is essential to achieving anti-fragility for classes of events with negative impacts.
Anomaly Detection : Techniques and Applications
When information in the data warehouse is processed, it follows a definite pattern. An unexpected deviation in the data pattern from the usual behavior is called an anomaly. The anomaly in the data is also referred to as noise, outlier, spammer, deviations, novelties and exceptions. Identification of the rare items, events, observations, patterns which raise suspension by differing significantly from the majority of data is called anomaly detection. With progress in the technologies and the widespread use of data for the purpose for business the increase in the spams faced by the individuals and the companies are increasing day by day. This noisy data has boomed as a major problem in various areas such as Internet of Things, web service, Machine Learning, Artificial Intelligence, Deep learning, Image Processing, Cloud Computing, Audio processing, Video Processing, VoIP, Data Science, Wireless Sensor etc. Identifying the anomaly data and filtering them before processing is a major challenge for the data analyst. This anomaly is unavoidable in all areas of research. This book covers the techniques and algorithms for detecting the deviated data.
An Introduction to Mathematics of Emerging Biomedical Imaging
Biomedical imaging is a fascinating research area to applied mathematicians. Challenging imaging problems arise and they often trigger the investigation of fundamental problems in various branches of mathematics. This is the first book to highlight the most recent mathematical developments in emerging biomedical imaging techniques. The main focus is on emerging multi-physics and multi-scales imaging approaches. For such promising techniques, it provides the basic mathematical concepts and tools for image reconstruction. Further improvements in these exciting imaging techniques require continued research in the mathematical sciences, a field that has contributed greatly to biomedical imaging and will continue to do so.
AI-Enabled Threat Detection and Security Analysis for Industrial IoT
Provides the state-of-the-art development on security and privacy for cyber-physical systems (CPS) and industrial Internet of Things (IIoT). More specifically, this book discusses the security challenges in CPS and IIoT systems as well as how Artificial Intelligence (AI) and Machine Learning (ML) can be used to address these challenges. Furthermore, this book proposes various defence strategies, including intelligent cyber-attack and anomaly detection algorithms for different IIoT applications.
Advances in Information and Computer Security ; 3rd International Workshop on Security, IWSEC 2008, Kagawa, Japan, November 25-27, 2008. Proceedings
This book constitutes the refereed proceedings of the Third International Workshop on Security, IWSEC 2008, held in Kagawa, Japan, in November 2008.
