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A Machine Learning Approach To Phishing Detection And Defense
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Book Synopsis A Machine-Learning Approach to Phishing Detection and Defense by : Iraj Sadegh Amiri
Download or read book A Machine-Learning Approach to Phishing Detection and Defense written by Iraj Sadegh Amiri and published by Syngress. This book was released on 2014-12-05 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organizations who are seeking solutions to this long-standing threat. A Machine-Learning Approach to Phishing Detetion and Defense also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats. Discover novel research into the uses of machine-learning principles and algorithms to detect and prevent phishing attacks Help your business or organization avoid costly damage from phishing sources Gain insight into machine-learning strategies for facing a variety of information security threats
Book Synopsis Phishing Detection Using Content-Based Image Classification by : Shekhar Khandelwal
Download or read book Phishing Detection Using Content-Based Image Classification written by Shekhar Khandelwal and published by CRC Press. This book was released on 2022-06-01 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing Detection Using Content-Based Image Classification is an invaluable resource for any deep learning and cybersecurity professional and scholar trying to solve various cybersecurity tasks using new age technologies like Deep Learning and Computer Vision. With various rule-based phishing detection techniques at play which can be bypassed by phishers, this book provides a step-by-step approach to solve this problem using Computer Vision and Deep Learning techniques with significant accuracy. The book offers comprehensive coverage of the most essential topics, including: Programmatically reading and manipulating image data Extracting relevant features from images Building statistical models using image features Using state-of-the-art Deep Learning models for feature extraction Build a robust phishing detection tool even with less data Dimensionality reduction techniques Class imbalance treatment Feature Fusion techniques Building performance metrics for multi-class classification task Another unique aspect of this book is it comes with a completely reproducible code base developed by the author and shared via python notebooks for quick launch and running capabilities. They can be leveraged for further enhancing the provided models using new advancement in the field of computer vision and more advanced algorithms.
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :0309494508 Total Pages :99 pages Book Rating :4.3/5 (94 download)
Book Synopsis Implications of Artificial Intelligence for Cybersecurity by : National Academies of Sciences, Engineering, and Medicine
Download or read book Implications of Artificial Intelligence for Cybersecurity written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-01-27 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, interest and progress in the area of artificial intelligence (AI) and machine learning (ML) have boomed, with new applications vigorously pursued across many sectors. At the same time, the computing and communications technologies on which we have come to rely present serious security concerns: cyberattacks have escalated in number, frequency, and impact, drawing increased attention to the vulnerabilities of cyber systems and the need to increase their security. In the face of this changing landscape, there is significant concern and interest among policymakers, security practitioners, technologists, researchers, and the public about the potential implications of AI and ML for cybersecurity. The National Academies of Sciences, Engineering, and Medicine convened a workshop on March 12-13, 2019 to discuss and explore these concerns. This publication summarizes the presentations and discussions from the workshop.
Book Synopsis Effective Phishing Detection Using Machine Learning Approach by : Yang Yaokai
Download or read book Effective Phishing Detection Using Machine Learning Approach written by Yang Yaokai and published by . This book was released on 2019 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online phishing is one of the most epidemic crime schemes of the modern Internet. A common countermeasure involves checking URLs against blacklists of known phishing websites, which are traditionally compiled based on manual verification, and is inefficient. Thus, as the Internet scale grows, automatic URL detection is increasingly important to provide timely protection to end users. In this thesis, we propose an effective and flexible malicious URL detection system with a rich set of features reflecting diverse characteristics of phishing webpages and their hosting platforms, including features that are hard to forge by a miscreant. Using Random Forests algorithm, our system enjoys the benefit of both high detection power and low error rates. Based on our knowledge, this is the first study to conduct such a large-scale websites/URLs scanning and classification experiments taking advantage of distributed vantage points for feature collection. Experiment results demonstrate that our system can be utilized for automatic construction of blacklists by a blacklist provider.
Book Synopsis Phishing Detection with Modern NLP Approaches by : Christian Schmid
Download or read book Phishing Detection with Modern NLP Approaches written by Christian Schmid and published by GRIN Verlag. This book was released on 2021-05-31 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: Masterarbeit aus dem Jahr 2020 im Fachbereich Mathematik - Sonstiges, Note: 1,3, Universität Ulm, Sprache: Deutsch, Abstract: Phishing is a form of identity theft that combines social engineering techniques and sophisticated attack vectors to fraudulently gain confidential information of unsuspecting consumers. To prevent successful phishing attacks, there are several approaches to detect and block phishing emails. In this work, we apply a number of modern transformer based machine learning methods for phishing email detection. Typically, phishing messages imitate trustworthy sources and request information via some form of electronic communication. The most frequent attack route is via email where phishers often try to persuade the email recipients to perform an action. This action may involve revealing confidential information (e.g. passwords) or inadvertently providing access to their computers or networks (e.g. through the installation of malicious software).
Book Synopsis Computer Security -- ESORICS 2012 by : Sara Foresti
Download or read book Computer Security -- ESORICS 2012 written by Sara Foresti and published by Springer. This book was released on 2012-08-19 with total page 911 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th European Symposium on Computer Security, ESORICS 2012, held in Pisa, Italy, in September 2012. The 50 papers included in the book were carefully reviewed and selected from 248 papers. The articles are organized in topical sections on security and data protection in real systems; formal models for cryptography and access control; security and privacy in mobile and wireless networks; counteracting man-in-the-middle attacks; network security; users privacy and anonymity; location privacy; voting protocols and anonymous communication; private computation in cloud systems; formal security models; identity based encryption and group signature; authentication; encryption key and password security; malware and phishing; and software security.
Book Synopsis Design and Development of a Machine Learning-based Framework for Phishing Website Detection by : Lizhen Tang
Download or read book Design and Development of a Machine Learning-based Framework for Phishing Website Detection written by Lizhen Tang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing is a social engineering cyber attack to steal personal information from users. Attackers solicit individuals to click phishing links by sending them emails or social media text messages with deceptive content. With the development and applications of machine learning technology, solutions for detecting phishing links have emerged. Subsequently, performance optimization achieved by machine learning-based approaches were predominantly limited to the datasets used to train the model, such as few open source datasets, poorly characterized data points, and outdated datasets. This thesis introduces a framework based on multiple phishing detection strategies, which are whitelist, blacklist, heuristic rules, and machine learning models, to improve accuracy and flexibility. In the machine learning-based method, three traditional models and three deep learning models are trained and compared the performance of their test results, and concluded that the Gated Recurrent Units (GRU) model achieved the highest accuracy of 99.18%. Furthermore, in the expert-driven heuristic rule-based strategy, seven new HTML-based features are proposed. Finally, a prototype has been developed, with a browser extension to display detection results in real-time.
Book Synopsis Algorithms and Architectures for Parallel Processing, Part II by : Yang Xiang
Download or read book Algorithms and Architectures for Parallel Processing, Part II written by Yang Xiang and published by Springer Science & Business Media. This book was released on 2011-10-07 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set LNCS 7016 and LNCS 7017 constitutes the refereed proceedings of the 11th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2011, held in Melbourne, Australia, in October 2011. The second volume includes 37 papers from one symposium and three workshops held together with ICA3PP 2011 main conference. These are 16 papers from the 2011 International Symposium on Advances of Distributed Computing and Networking (ADCN 2011), 10 papers of the 4th IEEE International Workshop on Internet and Distributed Computing Systems (IDCS 2011), 7 papers belonging to the III International Workshop on Multicore and Multithreaded Architectures and Algorithms (M2A2 2011), as well as 4 papers of the 1st IEEE International Workshop on Parallel Architectures for Bioinformatics Systems (HardBio 2011).
Book Synopsis Data and Applications Security and Privacy XXXIII by : Simon N. Foley
Download or read book Data and Applications Security and Privacy XXXIII written by Simon N. Foley and published by Springer. This book was released on 2019-07-04 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 33rd Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2019, held in Charleston, SC, USA, in July 2018. The 21 full papers presented were carefully reviewed and selected from 52 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections on attacks, mobile and Web security, privacy, security protocol practices, distributed systems, source code security, and malware.
Book Synopsis Deep Learning Applications for Cyber Security by : Mamoun Alazab
Download or read book Deep Learning Applications for Cyber Security written by Mamoun Alazab and published by Springer. This book was released on 2019-08-14 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.
Book Synopsis Malware Analysis Using Artificial Intelligence and Deep Learning by : Mark Stamp
Download or read book Malware Analysis Using Artificial Intelligence and Deep Learning written by Mark Stamp and published by Springer Nature. This book was released on 2020-12-20 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
Book Synopsis Mastering Machine Learning for Penetration Testing by : Chiheb Chebbi
Download or read book Mastering Machine Learning for Penetration Testing written by Chiheb Chebbi and published by Packt Publishing Ltd. This book was released on 2018-06-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
Book Synopsis Game Theory and Machine Learning for Cyber Security by : Charles A. Kamhoua
Download or read book Game Theory and Machine Learning for Cyber Security written by Charles A. Kamhoua and published by John Wiley & Sons. This book was released on 2021-09-08 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.
Book Synopsis Handbook of Research on Cyber Approaches to Public Administration and Social Policy by : Özsungur, Fahri
Download or read book Handbook of Research on Cyber Approaches to Public Administration and Social Policy written by Özsungur, Fahri and published by IGI Global. This book was released on 2022-06-10 with total page 692 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the COVID-19 era, the functions of social policy and public administration have undergone a meaningful change, especially with the advancement of digital elements and online and virtual functions. Cyber developments, cyber threats, and the effects of cyberwar on the public administrations of countries have become critical research subjects, and it is important to have resources that can introduce and guide users through the current best practices, laboratory methods, policies, protocols, and more within cyber public administration and social policy. The Handbook of Research on Cyber Approaches to Public Administration and Social Policy focuses on the post-pandemic changes in the functions of social policy and public administration. It also examines the implications of the cyber cosmos on public and social policies and practices from a broad perspective. Covering topics such as intersectional racism, cloud computing applications, and public policies, this major reference work is an essential resource for scientists, laboratory technicians, professionals, technologists, computer scientists, policymakers, students, educators, researchers, and academicians.
Book Synopsis Big Data and Edge Intelligence for Enhanced Cyber Defense by : Chhabi Rani Panigrahi
Download or read book Big Data and Edge Intelligence for Enhanced Cyber Defense written by Chhabi Rani Panigrahi and published by CRC Press. This book was released on 2024-07-31 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: An unfortunate outcome of the growth of the Internet and mobile technologies has been the challenge of countering cybercrime. This book introduces and explains the latest trends and techniques of edge artificial intelligence (EdgeAI) intended to help cyber security experts design robust cyber defense systems (CDS), including host-based and network-based intrusion detection system and digital forensic intelligence. This book discusses the direct confluence of EdgeAI with big data, as well as demonstrating detailed reviews of recent cyber threats and their countermeasure. It provides computational intelligence techniques and automated reasoning models capable of fast training and timely data processing of cyber security big data, in addition to other basic information related to network security. In addition, it provides a brief overview of modern cyber security threats and outlines the advantages of using EdgeAI to counter these threats, as well as exploring various cyber defense mechanisms (CDM) based on detection type and approaches. Specific challenging areas pertaining to cyber defense through EdgeAI, such as improving digital forensic intelligence, proactive and adaptive defense of network infrastructure, and bio-inspired CDM, are also discussed. This book is intended as a reference for academics and students in the field of network and cybersecurity, particularly on the topics of intrusion detection systems, smart grid, EdgeAI, and bio-inspired cyber defense principles. The front-line EdgeAI techniques discussed will also be of use to cybersecurity engineers in their work enhancing cyber defense systems.
Book Synopsis Swarm, Evolutionary, and Memetic Computing by : Bijaya Ketan Panigrahi
Download or read book Swarm, Evolutionary, and Memetic Computing written by Bijaya Ketan Panigrahi and published by Springer. This book was released on 2013-12-02 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 8297 and LNCS 8298 constitutes the proceedings of the 4th International Conference on Swarm, Evolutionary and Memetic Computing, SEMCCO 2013, held in Chennai, India, in December 2013. The total of 123 papers presented in this volume set was carefully reviewed and selected for inclusion in the proceedings. They cover cutting-edge research on swarm, evolutionary and memetic computing, neural and fuzzy computing and its application.
Book Synopsis Exploring Phishing Detection Using Search Engine Optimization and Uniform Resource Locator Based Information by : Kewei Ma
Download or read book Exploring Phishing Detection Using Search Engine Optimization and Uniform Resource Locator Based Information written by Kewei Ma and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing attacks are the work of social engineering. They are used to trick users to obtain their sensitive/private information using malicious links, websites, and electronic messages. In this thesis, phishing attack detection is explored using information based on uniform resource locators (URLs) and third-party search engine optimization (SEO) tools. A supervised learning approach is used to detect phishing websites. Evaluations are performed using real-world data and a Decision Tree model, which optimized using the Tree-based Pipeline Optimization Tool (TPOT) via Automated Machine Learning (AutoML). The results obtained are not only better than the state-of-the-art models in the literature, but also achieve a 97% detection rate. To utilize the proposed model, the best-performing pipeline from TPOT is embedded to a web API for future remote access.
