Data Mining and Machine Learning in Cybersecurity


Author: Sumeet Dua,Xian Du
Publisher: CRC Press
ISBN: 9781439839430
Category: Computers
Page: 256
View: 6431
DOWNLOAD NOW »
With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need. From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges—detailing cutting-edge machine learning and data mining techniques. It also: Unveils cutting-edge techniques for detecting new attacks Contains in-depth discussions of machine learning solutions to detection problems Categorizes methods for detecting, scanning, and profiling intrusions and anomalies Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions Details privacy-preserving data mining methods This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.

Machine Learning for Computer and Cyber Security

Principle, Algorithms, and Practices
Author: Brij B. Gupta,Quan Z. Sheng
Publisher: CRC Press
ISBN: 0429995717
Category: Computers
Page: 352
View: 3870
DOWNLOAD NOW »
While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.

Network Intrusion Detection


Author: Stephen Northcutt,Judy Novak
Publisher: N.A
ISBN: 9783826650444
Category:
Page: 501
View: 2137
DOWNLOAD NOW »


Machine Learning for Computer and Cyber Security

Principles, Algorithms, and Practices
Author: Brij Bhooshian Gupta,Quan Z. Sheng
Publisher: CRC Press
ISBN: 9781138587304
Category: Artificial intelligence
Page: 275
View: 4942
DOWNLOAD NOW »
While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.

Hands-On Machine Learning for Cybersecurity

Safeguard your system by making your machines intelligent using the Python ecosystem
Author: Soma Halder,Sinan Ozdemir
Publisher: Packt Publishing Ltd
ISBN: 178899096X
Category: Computers
Page: 318
View: 2384
DOWNLOAD NOW »
Get into the world of smart data security using machine learning algorithms and Python libraries Key Features Learn machine learning algorithms and cybersecurity fundamentals Automate your daily workflow by applying use cases to many facets of security Implement smart machine learning solutions to detect various cybersecurity problems Book Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learn Use machine learning algorithms with complex datasets to implement cybersecurity concepts Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes Use TensorFlow in the cybersecurity domain and implement real-world examples Learn how machine learning and Python can be used in complex cyber issues Who this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

Data mining

praktische Werkzeuge und Techniken für das maschinelle Lernen
Author: Ian H. Witten,Eibe Frank
Publisher: N.A
ISBN: 9783446215337
Category:
Page: 386
View: 9765
DOWNLOAD NOW »


Cyber Security Cryptography and Machine Learning

First International Conference, CSCML 2017, Beer-Sheva, Israel, June 29-30, 2017, Proceedings
Author: Shlomi Dolev,Sachin Lodha
Publisher: Springer
ISBN: 331960080X
Category: Computers
Page: 307
View: 1608
DOWNLOAD NOW »
This book constitutes the proceedings of the first International Symposium on Cyber Security Cryptography and Machine Learning, held in Beer-Sheva, Israel, in June 2017. The 17 full and 4 short papers presented include cyber security; secure software development methodologies, formal methods semantics and verification of secure systems; fault tolerance, reliability, availability of distributed secure systems; game-theoretic approaches to secure computing; automatic recovery of self-stabilizing and self-organizing systems; communication, authentication and identification security; cyber security for mobile and Internet of things; cyber security of corporations; security and privacy for cloud, edge and fog computing; cryptography; cryptographic implementation analysis and construction; secure multi-party computation; privacy-enhancing technologies and anonymity; post-quantum cryptography and security; machine learning and big data; anomaly detection and malware identification; business intelligence and security; digital forensics; digital rights management; trust management and reputation systems; information retrieval, risk analysis, DoS.

Machine Learning and Security

Protecting Systems with Data and Algorithms
Author: Clarence Chio,David Freeman
Publisher: "O'Reilly Media, Inc."
ISBN: 1491979879
Category: Computers
Page: 386
View: 2908
DOWNLOAD NOW »
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself! With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions

Cyber Threat Intelligence


Author: Ali Dehghantanha,Mauro Conti,Tooska Dargahi
Publisher: Springer
ISBN: 3319739514
Category: Computers
Page: 334
View: 4030
DOWNLOAD NOW »
This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the researcher and digital forensics experts, in order to build intelligent automated systems to fight against advanced cybercrimes. The ever increasing number of cyber-attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time, and with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions – this in essence defines cyber threat intelligence notion. However, such intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyze, and interpret cyber-attack campaigns which is covered in this book. This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive reason, learn and act on a wide range of data collected from different cyber security and forensics solutions. This book introduces the notion of cyber threat intelligence and analytics and presents different attempts in utilizing machine learning and data mining techniques to create threat feeds for a range of consumers. Moreover, this book sheds light on existing and emerging trends in the field which could pave the way for future works. The inter-disciplinary nature of this book, makes it suitable for a wide range of audiences with backgrounds in artificial intelligence, cyber security, forensics, big data and data mining, distributed systems and computer networks. This would include industry professionals, advanced-level students and researchers that work within these related fields.

Data Science For Cyber-security


Author: Adams Niall M,Heard Nicholas A,Rubin-delanchy Patrick
Publisher: World Scientific
ISBN: 178634565X
Category: Computers
Page: 304
View: 5014
DOWNLOAD NOW »
Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.

Data Analytics and Decision Support for Cybersecurity

Trends, Methodologies and Applications
Author: Iván Palomares Carrascosa,Harsha Kumara Kalutarage,Yan Huang
Publisher: Springer
ISBN: 3319594397
Category: Computers
Page: 270
View: 7388
DOWNLOAD NOW »
The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.

Worm

Der erste digitale Weltkrieg
Author: Mark Bowden
Publisher: ebook Berlin Verlag
ISBN: 3827075203
Category: Science
Page: 288
View: 3377
DOWNLOAD NOW »
Dass Cyberverbrechen und Cyberwar keine bloß virtuellen Gefahren mehr sind, sickert erst allmählich ins öffentliche und politische Bewusstsein. Als der Computerwurm »Conficker« im November 2008 auf die Welt losgelassen wurde, infizierte er binnen weniger Wochen Millionen von Computern weltweit. War er in ein System eingedrungen, konnte er dieses mit anderen verbinden und so ein Netzwerk bilden, das sich von außen kontrollieren ließ. Ein solch großes Botnetz ist theoretisch in der Lage, sämtliche Computernetzwerke zu überwältigen, ohne die heute unsere Banken, Telefone, Kraftwerke oder Flughäfen, ja sogar das Internet selbst kollabieren würden - mit unabsehbaren Folgen. War »Conficker« nur das Werkzeug von Cyberkriminellen oder gar eine reale militärische Waffe mit so nie dagewesenem Zerstörungspotenzial? Mark Bowden erzählt, wie in einem dramatischen Wettlauf Computerexperten alles daransetzen, den brandgefährlichen Wurm auszuschalten. Packend beschreibt er einen nach wie vor völlig unterschätzten Krieg, der buchstäblich unter unseren Fingerspitzen auf der Tastatur ausgefochten wird.

Cyber Security Cryptography and Machine Learning

Second International Symposium, CSCML 2018, Beer Sheva, Israel, June 21–22, 2018, Proceedings
Author: Itai Dinur,Shlomi Dolev,Sachin Lodha
Publisher: Springer
ISBN: 331994147X
Category: Computers
Page: 287
View: 8022
DOWNLOAD NOW »
This book constitutes the refereed proceedings of the Second International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018, held in Beer-Sheva, Israel, in June 2018. The 16 full and 6 short papers presented in this volume were carefully reviewed and selected from 44 submissions. They deal with the theory, design, analysis, implementation, or application of cyber security, cryptography and machine learning systems and networks, and conceptually innovative topics in the scope.

Maschinelles Lernen


Author: Ethem Alpaydin
Publisher: De Gruyter Oldenbourg
ISBN: 9783486581140
Category: Machine learning
Page: 440
View: 6822
DOWNLOAD NOW »
Maschinelles Lernen heißt, Computer so zu programmieren, dass ein bestimmtes Leistungskriterium anhand von Beispieldaten und Erfahrungswerten aus der Vergangenheit optimiert wird. Das vorliegende Buch diskutiert diverse Methoden, die ihre Grundlagen in verschiedenen Themenfeldern haben: Statistik, Mustererkennung, neuronale Netze, Künstliche Intelligenz, Signalverarbeitung, Steuerung und Data Mining. In der Vergangenheit verfolgten Forscher verschiedene Wege mit unterschiedlichen Schwerpunkten. Das Anliegen dieses Buches ist es, all diese unterschiedlichen Ansätze zu kombinieren, um eine allumfassende Behandlung der Probleme und ihrer vorgeschlagenen Lösungen zu geben.

Social Network Forensics, Cyber Security, and Machine Learning


Author: P. Venkata Krishna,Sasikumar Gurumoorthy,Mohammad S. Obaidat
Publisher: Springer
ISBN: 981131456X
Category: Computers
Page: 116
View: 9798
DOWNLOAD NOW »
This book discusses the issues and challenges in Online Social Networks (OSNs). It highlights various aspects of OSNs consisting of novel social network strategies and the development of services using different computing models. Moreover, the book investigates how OSNs are impacted by cutting-edge innovations.

Leben 3.0

Mensch sein im Zeitalter Künstlicher Intelligenz
Author: Max Tegmark
Publisher: Ullstein Buchverlage
ISBN: 3843716706
Category: Social Science
Page: 528
View: 8414
DOWNLOAD NOW »
Die Nobelpreis-Schmiede Massachusetts Institute of Technology ist der bedeutendste technologische Think Tank der USA. Dort arbeitet Professor Max Tegmark mit den weltweit führenden Entwicklern künstlicher Intelligenz zusammen, die ihm exklusive Einblicke in ihre Labors gewähren. Die Erkenntnisse, die er daraus zieht, sind atemberaubend und zutiefst verstörend zugleich. Neigt sich die Ära der Menschen dem Ende zu? Der Physikprofessor Max Tegmark zeigt anhand der neusten Forschung, was die Menschheit erwartet. Hier eine Auswahl möglicher Szenarien: - Eroberer: Künstliche Intelligenz übernimmt die Macht und entledigt sich der Menschheit mit Methoden, die wir noch nicht einmal verstehen. - Der versklavte Gott: Die Menschen bemächtigen sich einer superintelligenten künstlichen Intelligenz und nutzen sie, um Hochtechnologien herzustellen. - Umkehr: Der technologische Fortschritt wird radikal unterbunden und wir kehren zu einer prä-technologischen Gesellschaft im Stil der Amish zurück. - Selbstzerstörung: Superintelligenz wird nicht erreicht, weil sich die Menschheit vorher nuklear oder anders selbst vernichtet. - Egalitäres Utopia: Es gibt weder Superintelligenz noch Besitz, Menschen und kybernetische Organismen existieren friedlich nebeneinander. Max Tegmark bietet kluge und fundierte Zukunftsszenarien basierend auf seinen exklusiven Einblicken in die aktuelle Forschung zur künstlichen Intelligenz.

Machine Learning Forensics for Law Enforcement, Security, and Intelligence


Author: Jesus Mena
Publisher: CRC Press
ISBN: 143986070X
Category: Computers
Page: 349
View: 7260
DOWNLOAD NOW »
Increasingly, crimes and fraud are digital in nature, occurring at breakneck speed and encompassing large volumes of data. To combat this unlawful activity, knowledge about the use of machine learning technology and software is critical. Machine Learning Forensics for Law Enforcement, Security, and Intelligence integrates an assortment of deductive and instructive tools, techniques, and technologies to arm professionals with the tools they need to be prepared and stay ahead of the game. Step-by-step instructions The book is a practical guide on how to conduct forensic investigations using self-organizing clustering map (SOM) neural networks, text extraction, and rule generating software to "interrogate the evidence." This powerful data is indispensable for fraud detection, cybersecurity, competitive counterintelligence, and corporate and litigation investigations. The book also provides step-by-step instructions on how to construct adaptive criminal and fraud detection systems for organizations. Prediction is the key Internet activity, email, and wireless communications can be captured, modeled, and deployed in order to anticipate potential cyber attacks and other types of crimes. The successful prediction of human reactions and server actions by quantifying their behaviors is invaluable for pre-empting criminal activity. This volume assists chief information officers, law enforcement personnel, legal and IT professionals, investigators, and competitive intelligence analysts in the strategic planning needed to recognize the patterns of criminal activities in order to predict when and where crimes and intrusions are likely to take place.

Granular Computing Based Machine Learning

A Big Data Processing Approach
Author: Han Liu,Mihaela Cocea
Publisher: Springer
ISBN: 3319700588
Category: Computers
Page: 113
View: 5545
DOWNLOAD NOW »
This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.

Hacking mit Security Onion

Sicherheit im Netzwerk überwachen: Daten erfassen und sammeln, analysieren und Angriffe rechtzeitig erkennen
Author: Chris Sanders,Jason Smith
Publisher: Franzis Verlag
ISBN: 3645204962
Category: Computers
Page: 560
View: 3081
DOWNLOAD NOW »
Sie können noch so viel in Hardware, Software und Abwehrmechanismen investieren, absolute Sicherheit für Ihre IT-Infrastruktur wird es nicht geben. Wenn Hacker sich wirklich anstrengen, werden sie auch in Ihr System gelangen. Sollte das geschehen, müssen Sie sowohl technisch als auch organisatorisch so aufgestellt sein, dass Sie die Gegenwart eines Hackers erkennen und darauf reagieren können. Sie müssen in der Lage sein, einen Zwischenfall zu deklarieren und die Angreifer aus Ihrem Netzwerk zu vertreiben, bevor sie erheblichen Schaden anrichten. Das ist Network Security Monitoring (NSM). Lernen Sie von dem leitenden Sicherheitsanalytiker Sanders die Feinheiten des Network Security Monitoring kennen. Konzepte verstehen und Network Security Monitoring mit Open-Source-Tools durchführen: Lernen Sie die drei NSM-Phasen kennen, um diese in der Praxis anzuwenden. Die praktische Umsetzung der NSM erfolgt mit vielen Open-Source-Werkzeugen wie z. B. Bro, Daemonlogger, Dumpcap, Justniffer, Honeyd, Httpry, Netsniff-NG, Sguil, SiLK, Snorby Snort, Squert, Suricata, TShark und Wireshark. Anhand von ausführlichen Beispielen lernen Sie, die Tools effizient in Ihrem Netzwerk einzusetzen.

Data Science für Dummies


Author: Lillian Pierson
Publisher: John Wiley & Sons
ISBN: 352780675X
Category: Mathematics
Page: 382
View: 4964
DOWNLOAD NOW »
Daten, Daten, Daten? Sie haben schon Kenntnisse in Excel und Statistik, wissen aber noch nicht, wie all die Datensätze helfen sollen, bessere Entscheidungen zu treffen? Von Lillian Pierson bekommen Sie das dafür notwendige Handwerkszeug: Bauen Sie Ihre Kenntnisse in Statistik, Programmierung und Visualisierung aus. Nutzen Sie Python, R, SQL, Excel und KNIME. Zahlreiche Beispiele veranschaulichen die vorgestellten Methoden und Techniken. So können Sie die Erkenntnisse dieses Buches auf Ihre Daten übertragen und aus deren Analyse unmittelbare Schlüsse und Konsequenzen ziehen.