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Author : Wojciech Tylman,Politechnika Łódzka. Wydawnictwo
Publisher : Unknown
Page : 172 pages
File Size : 43,9 Mb
Release : 2013
Category : Electronic
ISBN : OCLC:857916373
Author : Obaid, Ahmed J.,Abdul-Majeed, Ghassan H.,Burlea-Schiopoiu, Adriana,Aggarwal, Parul
Publisher : IGI Global
Page : 409 pages
File Size : 41,9 Mb
Release : 2023-01-03
Category : Computers
ISBN : 9781668460627
In recent years, falsification and digital modification of video clips, images, as well as textual contents have become widespread and numerous, especially when deepfake technologies are adopted in many sources. Due to adopted deepfake techniques, a lot of content currently cannot be recognized from its original sources. As a result, the field of study previously devoted to general multimedia forensics has been revived. The Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications discusses the recent techniques and applications of illustration, generation, and detection of deepfake content in multimedia. It introduces the techniques and gives an overview of deepfake applications, types of deepfakes, the algorithms and applications used in deepfakes, recent challenges and problems, and practical applications to identify, generate, and detect deepfakes. Covering topics such as anomaly detection, intrusion detection, and security enhancement, this major reference work is a comprehensive resource for cyber security specialists, government officials, law enforcement, business leaders, students and faculty of higher education, librarians, researchers, and academicians.
Author : Tshilidzi Marwala,Monica Lagazio
Publisher : Springer Science & Business Media
Page : 254 pages
File Size : 42,5 Mb
Release : 2011-08-24
Category : Computers
ISBN : 0857297902
Militarized Conflict Modeling Using Computational Intelligence examines the application of computational intelligence methods to model conflict. Traditionally, conflict has been modeled using game theory. The inherent limitation of game theory when dealing with more than three players in a game is the main motivation for the application of computational intelligence in modeling conflict. Militarized interstate disputes (MIDs) are defined as a set of interactions between, or among, states that can result in the display, threat or actual use of military force in an explicit way. These interactions can result in either peace or conflict. This book models the relationship between key variables and the risk of conflict between two countries. The variables include Allies which measures the presence or absence of military alliance, Contiguity which measures whether the countries share a common boundary or not and Major Power which measures whether either or both states are a major power. Militarized Conflict Modeling Using Computational Intelligence implements various multi-layer perception neural networks, Bayesian networks, support vector machines, neuro-fuzzy models, rough sets models, neuro-rough sets models and optimized rough sets models to create models that estimate the risk of conflict given the variables. Secondly, these models are used to study the sensitivity of each variable to conflict. Furthermore, a framework on how these models can be used to control the possibility of peace is proposed. Finally, new and emerging topics on modelling conflict are identified and further work is proposed.
Author : Sanjai Veetil,Qigang Gao
Publisher : Elsevier Inc. Chapters
Page : 650 pages
File Size : 51,6 Mb
Release : 2013-11-06
Category : Computers
ISBN : 9780128070758
Over the years, many networks hosted by large companies or organizations have been crippled by intrusions launched with minimal effort. Such attacks have caused the loss of millions of dollars for the company and created serious security threats. As a result, network administrators and security experts across the globe have barricaded their networks with expensive Intrusion Detection Systems (IDS) to detect and take action in dealing with various network attacks. There is still a very challenging task to develop a cost-effective approach that can deal with network intrusions. Furthermore, large networks generate huge traffic data that serve as inputs for IDSes. In this chapter, we present a Network Intrusion Detection System (NIDS) built using Apache Hadoop and HStreaming, which can detect and alert administrators in real time. The system makes use of a simple yet versatile Naive Bayes classifier for predicting an attack. The experimental results show some promising outcomes.
Author : Balamurugan Rengeswaran
Publisher : GRIN Verlag
Page : 42 pages
File Size : 40,7 Mb
Release : 2019-10-21
Category : Computers
ISBN : 9783346039125
Research Paper (undergraduate) from the year 2019 in the subject Computer Science - Applied, VIT University, language: English, abstract: In these days of rising internet usage, almost everyone has access to the internet. It is available easily and readily. So along with increase in popularity and importance it also leads to an increase in risks and susceptibility to unwanted attacks. Networks and servers and more prone to malicious attacks than ever. Cyber security is vital in this age. Lots of organizations now interact and communicate with people via the internet. They store huge amounts of data in their computers or devices connected to the network. This data should only be accessed by authorized members of the organization. It is possible for hackers to gain unauthorized access to this data. A lot of sensitive information is present in the data which might lead to harm in the hands of hackers. It is important to protect the network from being attacked in such a way. Network security is an element of cyber security which aims to provide services so that the organizations are safe from such attacks. Intrusion detection systems are present in the network which work along with the firewalls to detect and prevent such attacks. For this project, we aim to identify the suitable machine learning technique to detect such attacks and which can be used in state of the art system.
Author : Olivier Pourret,Patrick Naïm,Bruce Marcot
Publisher : John Wiley & Sons
Page : 446 pages
File Size : 48,5 Mb
Release : 2008-04-30
Category : Mathematics
ISBN : 0470994541
Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
Author : Naval Postgraduate School,Carolyn A Schiesser
Publisher : Independently Published
Page : 78 pages
File Size : 46,6 Mb
Release : 2020-09-16
Category : Electronic
ISBN : 9798586818010
With the Navy's focus on efficient energy consumption, Naval Facilities Engineering Command deployed its own Smart Grid in 2019, allowing shore commands to modernize and meet energy consumption mandates set in place by the Secretary of the Navy. With the addition of new 'smart' technology comes additional risks in the form of cyber-attacks. This thesis implements a Bayesian classification and machine learning algorithm that explores how the data set, size of training data and number of features affect classification accuracy. Our experiment was performed using seven data sets, developed through the University of Montreal using a SCADA sandbox similar to that of the Navy Smart Grid. Three data sets contained nominal data, and four data sets contained malicious cyber-attacks. Our experiments, performed using MATLAB, showed that malicious packet distribution within the data set and size of the training data greatly affected classification accuracy. This thesis demonstrates machine learning operability for use in the Smart Grid environment and will provide data points to further research for Network Intrusion Detection Systems (NIDS).
Author : Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publisher : CRC Press
Page : 366 pages
File Size : 51,8 Mb
Release : 2013-06-18
Category : Computers
ISBN : 9781466582095
With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you’ll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.
Author : Subhendu Kumar Pani,Sanjay Kumar Singh,Lalit Garg,Ram Bilas Pachori,Xiaobo Zhang
Publisher : John Wiley & Sons
Page : 352 pages
File Size : 43,8 Mb
Release : 2020-12-31
Category : Computers
ISBN : 9781119711612
Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. This book provides innovative insights that will help obtain interventions to undertake emerging dynamic scenarios of criminal activities. Furthermore, it presents emerging issues, challenges and management strategies in public safety and crime control development across various domains. The book will play a vital role in improvising human life to a great extent. Researchers and practitioners working in the fields of data mining, machine learning and artificial intelligence will greatly benefit from this book, which will be a good addition to the state-of-the-art approaches collected for intelligent data analytics. It will also be very beneficial for those who are new to the field and need to quickly become acquainted with the best performing methods. With this book they will be able to compare different approaches and carry forward their research in the most important areas of this field, which has a direct impact on the betterment of human life by maintaining the security of our society. No other book is currently on the market which provides such a good collection of state-of-the-art methods for intelligent data analytics-based models for terror threat prediction, as intelligent data analytics is a newly emerging field and research in data mining and machine learning is still in the early stage of development.
Author : Anonim
Publisher : Unknown
Page : 1110 pages
File Size : 53,6 Mb
Release : 1986
Category : Engineering
ISBN : UVA:X030605759
Author : Daniel Barbará,Sushil Jajodia
Publisher : Springer Science & Business Media
Page : 266 pages
File Size : 40,8 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461509530
Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign, looking for patterns in financial transactions to discover illegal activities or analyzing genome sequences. From this perspective, it was just a matter of time for the discipline to reach the important area of computer security. Applications Of Data Mining In Computer Security presents a collection of research efforts on the use of data mining in computer security. Applications Of Data Mining In Computer Security concentrates heavily on the use of data mining in the area of intrusion detection. The reason for this is twofold. First, the volume of data dealing with both network and host activity is so large that it makes it an ideal candidate for using data mining techniques. Second, intrusion detection is an extremely critical activity. This book also addresses the application of data mining to computer forensics. This is a crucial area that seeks to address the needs of law enforcement in analyzing the digital evidence.
Author : Engin Kirda,Somesh Jha,Davide Balzarotti
Publisher : Springer Science & Business Media
Page : 395 pages
File Size : 45,5 Mb
Release : 2009-09-11
Category : Business & Economics
ISBN : 9783642043413
On behalf of the Program Committee, it is our pleasure to present the p- ceedings of the 12th International Symposium on Recent Advances in Intrusion Detection systems (RAID 2009),which took place in Saint-Malo,France, during September 23–25. As in the past, the symposium brought together leading - searchers and practitioners from academia, government, and industry to discuss intrusion detection research and practice. There were six main sessions prese- ingfullresearchpapersonanomalyandspeci?cation-basedapproaches,malware detection and prevention, network and host intrusion detection and prevention, intrusion detection for mobile devices, and high-performance intrusion det- tion. Furthermore, there was a poster session on emerging research areas and case studies. The RAID 2009ProgramCommittee received59 full paper submissionsfrom all over the world. All submissions were carefully reviewed by independent - viewers on the basis of space, topic, technical assessment, and overall balance. The ?nal selection took place at the Program Committee meeting on May 21 in Oakland, California. In all, 17 papers were selected for presentation and p- lication in the conference proceedings. As a continued feature, the symposium accepted submissions for poster presentations which have been published as - tended abstracts, reporting early-stage research, demonstration of applications, or case studies. Thirty posters were submitted for a numerical review by an independent, three-person sub-committee of the Program Committee based on novelty, description, and evaluation. The sub-committee recommended the - ceptance of 16 of these posters for presentation and publication. The success of RAID 2009 depended on the joint e?ort of many people.
Author : Mirosław Kutyłowski,Jun Zhang,Chao Chen
Publisher : Springer Nature
Page : 458 pages
File Size : 51,9 Mb
Release : 2020-12-18
Category : Computers
ISBN : 9783030657451
This book constitutes the refereed proceedings of the 14th International Conference on Network and System Security, NSS 2020, held in Melbourne, VIC, Australia, in November 2020. The 17 full and 9 short papers were carefully reviewed and selected from 60 submissions. The selected papers are devoted to topics such as secure operating system architectures, applications programming and security testing, intrusion and attack detection, cybersecurity intelligence, access control, cryptographic techniques, cryptocurrencies, ransomware, anonymity, trust, recommendation systems, as well machine learning problems. Due to the Corona pandemic the event was held virtually.
Author : Vasilios Siris,Sotiris Ioannidis,Kostas Anagnostakis,Panagiotis Trimintzios
Publisher : Springer Science & Business Media
Page : 165 pages
File Size : 47,6 Mb
Release : 2010-06-09
Category : Computers
ISBN : 9780387855554
The European Conference on Computer and Network Defense draws contributions and participation both from academia and industry, and addresses security from multiple perspectives, including state-of-the-art research in computer network security, intrusion detection, denial-of-service, privacy protection, security policies, and incident response & management. The conference is organized jointly by the Institute of Computer Science of the Foundation for Research and Technology – Hellas (FORTH) and the European Network and Information Security Agency (ENISA).
Author : Kwangjo Kim,Muhamad Erza Aminanto,Harry Chandra Tanuwidjaja
Publisher : Springer
Page : 79 pages
File Size : 49,7 Mb
Release : 2018-10-02
Category : Computers
ISBN : 9811314438
This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.