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Phishing Detection With Modern Nlp Approaches
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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 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.
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 Optimization, Learning Algorithms and Applications by : Ana I. Pereira
Download or read book Optimization, Learning Algorithms and Applications written by Ana I. Pereira and published by Springer Nature. This book was released on 2021-12-02 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected and revised papers presented at the First International Conference on Optimization, Learning Algorithms and Applications, OL2A 2021, held in Bragança, Portugal, in July 2021. Due to the COVID-19 pandemic the conference was held online. The 39 full papers and 13 short papers were thoroughly reviewed and selected from 134 submissions. They are organized in the topical sections on optimization theory; robotics; measurements with the internet of things; optimization in control systems design; deep learning; data visualization and virtual reality; health informatics; data analysis; trends in engineering education.
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 A New Real-time Approach for Website Phishing Detection Based on Visual Similarity by : Omid Asudeh
Download or read book A New Real-time Approach for Website Phishing Detection Based on Visual Similarity written by Omid Asudeh and published by . This book was released on 2016 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing attacks cause billions of dollars of loss every year worldwide. Among several solutions proposed for this type of attack, visual similarity detection methods have a good amount of accuracy. These methods exploit the fact that malicious pages mostly imitate some visual signals in the targeted websites. Visual similarity detection methods usually look for the imitations between the screen-shots of the web-pages and the image database of the most targeted websites. Despite their accuracy, the existing visual based approaches are not practical for the real-time purposes because of their image processing overhead. In this work, we use a pipeline framework in order to be reliable and fast at the same time. The goal of the framework is to quickly and confidently (without false negatives) rule out the bulk of the pages that are completely different with the database of targeted websites and to do more processing on the more similar pages. In our experiments, the very first module of the pipeline could rule out more than half of the test cases with zero false negatives. The mean and the median query time of each of the test cases is less than 5 milliseconds for the first module.
Book Synopsis Phishing Website Detection Using Intelligent Data Mining Techniques by : Maher Aburrous
Download or read book Phishing Website Detection Using Intelligent Data Mining Techniques written by Maher Aburrous and published by LAP Lambert Academic Publishing. This book was released on 2012 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing techniques have not only grown in number, but also in sophistication. Phishers might have a lot of approaches and tactics to conduct a well-designed phishing attack. The targets of the phishing attacks, which are mainly on-line banking consumers and payment service providers, are facing substantial financial loss and lack of trust in Internet-based services. In order to overcome these, there is an urgent need to find solutions to combat phishing attacks. Detecting phishing website is a complex task which requires significant expert knowledge and experience. So far, various solutions have been proposed and developed to address these problems. Most of these approaches are not able to make a decision dynamically on whether the site is in fact phished, giving rise to a large number of false positives. This is mainly due to limitation of the previously proposed approaches, for example depending only on fixed black and white listing database, missing of human intelligence and experts, poor scalability and theirtimeliness. In this research we investigated and developed the application of an intelligent fuzzy-based classification system for e-banking phishing website detection. The main aim of the proposed system is to provide protection to users from phishers deception tricks, giving them the ability to detect the legitimacy of the websites. The proposed intelligent phishing detection system employed Fuzzy Logic (FL) model with association classification mining algorithms. The approach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamic phishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deception behavior techniques have been conducted to cover all phishing concerns. A layered fuzzy structure has been constructed for all gathered and extracted phishing website features and patterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attack type. To reduce human knowledge intervention, different classification and association algorithms have been implemented to generate fuzzy phishing rules automatically, to be integrated inside the fuzzy inference engine for the final phishing detection. Experimental results demonstrated that the ability of the learning approach to identify all relevant fuzzy rules from the training data set. A comparative study and analysis showed that the proposed learning approach has a higher degree of predictive and detective capability than existing models. Experiments also showed significance of some important phishing criteria like URL & Domain Identity, Security & Encryption to the final phishing detection rate. Finally, our proposed intelligent phishing website detection system was developed, tested and validated by incorporating the scheme as a web based plug-ins phishing toolbar. The results obtained are promising and showed that our intelligent fuzzy based classification detectionsystem can provide an effective help for real-time phishing website detection. The toolbar successfully recognized and detected approximately 92% of the phishing websites selected from our test data set, avoiding many miss-classified websites and false phishing alarms.
Book Synopsis Phishing Website Detection Using Intelligent Data Mining Techniques. Design and Development of an Intelligent Association Classification Mining Fuzzy Based Scheme for Phishing Website Detection with an Emphasis on E-banking by : Maher Ragheb Mohammed Abur-rous
Download or read book Phishing Website Detection Using Intelligent Data Mining Techniques. Design and Development of an Intelligent Association Classification Mining Fuzzy Based Scheme for Phishing Website Detection with an Emphasis on E-banking written by Maher Ragheb Mohammed Abur-rous and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing techniques have not only grown in number, but also in sophistication. Phishers mighthave a lot of approaches and tactics to conduct a well-designed phishing attack. The targets ofthe phishing attacks, which are mainly on-line banking consumers and payment serviceproviders, are facing substantial financial loss and lack of trust in Internet-based services. Inorder to overcome these, there is an urgent need to find solutions to combat phishing attacks. Detecting phishing website is a complex task which requires significant expert knowledge andexperience. So far, various solutions have been proposed and developed to address theseproblems. Most of these approaches are not able to make a decision dynamically on whether thesite is in fact phished, giving rise to a large number of false positives. This is mainly due tolimitation of the previously proposed approaches, for example depending only on fixed blackand white listing database, missing of human intelligence and experts, poor scalability and theirtimeliness. In this research we investigated and developed the application of an intelligent fuzzy-basedclassification system for e-banking phishing website detection. The main aim of the proposedsystem is to provide protection to users from phishers deception tricks, giving them the abilityto detect the legitimacy of the websites. The proposed intelligent phishing detection systememployed Fuzzy Logic (FL) model with association classification mining algorithms. Theapproach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamicphishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deceptionbehaviour techniques have been conducted to cover all phishing concerns. A layered fuzzystructure has been constructed for all gathered and extracted phishing website features andpatterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attacktype. To reduce human knowledge intervention, Different classification and associationalgorithms have been implemented to generate fuzzy phishing rules automatically, to beintegrated inside the fuzzy inference engine for the final phishing detection. Experimental results demonstrated that the ability of the learning approach to identify allrelevant fuzzy rules from the training data set. A comparative study and analysis showed thatthe proposed learning approach has a higher degree of predictive and detective capability thanexisting models. Experiments also showed significance of some important phishing criteria likeURL & Domain Identity, Security & Encryption to the final phishing detection rate. Finally, our proposed intelligent phishing website detection system was developed, tested andvalidated by incorporating the scheme as a web based plug-ins phishing toolbar. The resultsobtained are promising and showed that our intelligent fuzzy based classification detectionsystem can provide an effective help for real-time phishing website detection. The toolbarsuccessfully recognized and detected approximately 92% of the phishing websites selected fromour test data set, avoiding many miss-classified websites and false phishing alarms.
Book Synopsis Detection and Analysis of Phishing Attacks by : Qian Cui
Download or read book Detection and Analysis of Phishing Attacks written by Qian Cui and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The so-called "phishing attacks" are attacks in which a legitimate website is impersonated, in order to steal sensitive information from end-users. Phishing attacks represent one of the important threats to individuals and corporations in today's Internet. This problem has been actively researched by both academia and the industry over the past few years. Attempts to provide effective anti-phishing solutions have followed two main approaches: The first one is to identify a phishing attack by comparing its similarity to the target site. The second approach is to look at intrinsic characteristics of the attacks. In this thesis, we first look at this problem from a new angle. Instead of using the intrinsic characteristics of an attack or of comparing the similarity between attacks and target sites, we go back to the source of the problem. We perform an in-depth analysis of how phishing attacks are being built by the attackers. We show that most phishing attacks are duplicates or quasi-duplicates of former attacks. Given that phishing attacks are not built from scratch, we propose two clustering-based methods to evaluate the similarity between attacks. When comparing a newly reported attack against our database of known ones, our method achieves an accuracy of at least 90%, with a false-positive rate of 0.65%. We then explore the evolution of phishing attacks and track variations over time. Our aim is to better understand what attackers do change, and why, across iterations of the attack. We propose a graph-based model in order to monitor and analyze theses changes and their relations. In addition to the detection and analysis of phishing attacks on the client-side, we also explore the server-side aspect of phishing. We conduct a static analysis of the source code of "phishing kits" and propose an approach to track stolen information. Since most phishing attacks use email as the means to exfiltrate stolen information, we propose a deep learning model to detect these messages in network traffic. This approach can be used to easily detect that a phishing attack is hosted inside a large network for example. The third and final contribution of this thesis is a "blind" phishing scanning system, which is used to search for and identify unreported phishing attacks at large scale. The only input of that system is a very large list of domain names. In order to efficiently handle the list, we propose a ranking algorithm which combines natural language processing and machine learning techniques to prioritize the domains that are most likely to be harmful. We then mine our extensive, real-time phishing attack database to guess possible URLs of attacks on these domains and use our own detection algorithm for eventual detection.
Book Synopsis Risk Detection and Cyber Security for the Success of Contemporary Computing by : Kumar, Raghvendra
Download or read book Risk Detection and Cyber Security for the Success of Contemporary Computing written by Kumar, Raghvendra and published by IGI Global. This book was released on 2023-11-09 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid evolution of technology, identifying new risks is a constantly moving target. The metaverse is a virtual space that is interconnected with cloud computing and with companies, organizations, and even countries investing in virtual real estate. The questions of what new risks will become evident in these virtual worlds and in augmented reality and what real-world impacts they will have in an ever-expanding internet of things (IoT) need to be answered. Within continually connected societies that require uninterrupted functionality, cyber security is vital, and the ability to detect potential risks and ensure the security of computing systems is crucial to their effective use and success. Proper utilization of the latest technological advancements can help in developing more efficient techniques to prevent cyber threats and enhance cybersecurity. Risk Detection and Cyber Security for the Success of Contemporary Computing presents the newest findings with technological advances that can be utilized for more effective prevention techniques to protect against cyber threats. This book is led by editors of best-selling and highly indexed publications, and together they have over two decades of experience in computer science and engineering. Featuring extensive coverage on authentication techniques, cloud security, and mobile robotics, this book is ideally designed for students, researchers, scientists, and engineers seeking current research on methods, models, and implementation of optimized security in digital contexts.
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.
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 A Handbook of Computational Linguistics: Artificial Intelligence in Natural Language Processing by : Youddha Beer Singh
Download or read book A Handbook of Computational Linguistics: Artificial Intelligence in Natural Language Processing written by Youddha Beer Singh and published by Bentham Science Publishers. This book was released on 2024-08-12 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides a comprehensive understanding of computational linguistics, focusing on the integration of deep learning in natural language processing (NLP). 18 edited chapters cover the state-of-the-art theoretical and experimental research on NLP, offering insights into advanced models and recent applications. Highlights: - Foundations of NLP: Provides an in-depth study of natural language processing, including basics, challenges, and applications. - Advanced NLP Techniques: Explores recent advancements in text summarization, machine translation, and deep learning applications in NLP. - Practical Applications: Demonstrates use cases on text identification from hazy images, speech-to-sign language translation, and word sense disambiguation using deep learning. - Future Directions: Includes discussions on the future of NLP, including transfer learning, beyond syntax and semantics, and emerging challenges. Key Features: - Comprehensive coverage of NLP and deep learning integration. - Practical insights into real-world applications - Detailed exploration of recent research and advancements through 16 easy to read chapters - References and notes on experimental methods used for advanced readers Ideal for researchers, students, and professionals, this book offers a thorough understanding of computational linguistics by equipping readers with the knowledge to understand how computational techniques are applied to understand text, language and speech.
Book Synopsis Categorization of Phishing Detection Features and Using the Feature Vectors to Classify Phishing Websites by : Bhuvana Namasivayam
Download or read book Categorization of Phishing Detection Features and Using the Feature Vectors to Classify Phishing Websites written by Bhuvana Namasivayam and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing is a form of online fraud where a spoofed website tries to gain access to user's sensitive information by tricking the user into believing that it is a benign website. There are several solutions to detect phishing attacks such as educating users, using blacklists or extracting phishing characteristics found to exist in phishing attacks. In this thesis, we analyze approaches that extract features from phishing websites and train classification models with extracted feature set to classify phishing websites. We create an exhaustive list of all features used in these approaches and categorize them into 6 broader categories and 33 finer categories. We extract 59 features from the URL, URL redirects, hosting domain (WHOIS and DNS records) and popularity of the website and analyze their robustness in classifying a phishing website. Our emphasis is on determining the predictive performance of robust features. We evaluate the classification accuracy when using the entire feature set and when URL features or site popularity features are excluded from the feature set and show how our approach can be used to effectively predict specific types of phishing attacks such as shortened URLs and randomized URLs. Using both decision table classifiers and neural network classifiers, our results indicate that robust features seem to have enough predictive power to be used in practice.
Book Synopsis Advanced Practical Approaches to Web Mining Techniques and Application by : Obaid, Ahmed J.
Download or read book Advanced Practical Approaches to Web Mining Techniques and Application written by Obaid, Ahmed J. and published by IGI Global. This book was released on 2022-03-18 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid increase of web pages has introduced new challenges for many organizations as they attempt to extract information from a massive corpus of web pages. Finding relevant information, eliminating irregular content, and retrieving accurate results has become extremely difficult in today’s world where there is a surplus of information available. It is crucial to further understand and study web mining in order to discover the best ways to connect users with appropriate information in a timely manner. Advanced Practical Approaches to Web Mining Techniques and Application aims to illustrate all the concepts of web mining and fosters transformative, multidisciplinary, and novel approaches that introduce the practical method of analyzing various web data sources and extracting knowledge by taking into consideration the unique challenges present in the environment. Covering a range of topics such as data science and security threats, this reference work is ideal for industry professionals, researchers, academicians, practitioners, scholars, instructors, and students.
Download or read book Pelican written by Wern Sen Wong and published by . This book was released on 2019 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: An increasing number of people are using social media services and with it comes a more attractive outlet for phishing attacks. Our initial focus is to analyze Twitter as it is one of the most popular social media services. Phishers on Twitter curate tweets that lead users to websites that download malware. This is a major issue as phishers can then gain access to the user’s digital identity and perform malicious acts. Phishing attacks have the potential to be similar in different regions, perhaps at different times. We use these characteristics to help identify attacks and investigate the use of transfer learning to detect phishing models learned in one region to detect phishing in other regions. We have made three major contributions. Firstly, we have developed a novel semisupervised machine learning algorithm, which we call Pelican, that detects potential phishing attacks in real-time on Twitter. Pelican can be used for early detection of potential phishing attacks and is able to detect potential new attacks without pre-existing assumptions about the type of data or understanding of the characteristics of the attacks. The technique uses ensembles and sampling methods to handle class imbalances in real-world applications. Secondly, the technique automatically detects unusual behaviour or changes in Twitter. We have investigated changes in trends across Twitter to detect changes in online behaviour of potential phishing links. The technique uses a change detector that enables automatic retraining when there is unusual behaviour detected. Pelican is a novel technique that adapts to changes within phishing attacks in real-time. The technique detects 93.94% of the phishing tweets in real-world data that we collected over a 9 month period, which is higher than benchmark algorithms. Finally, we have adapted our system to detect phishing in small populations where data is scarce such as New Zealand. We used inductive instance transfer learning from the United States dataset to build the New Zealand model, by leveraging similar instances of phishing in the US. As a result, we were able to build a more accurate model for NZ. We have also contrasted the types of phishing attacks internationally versus phishing attacks on New Zealand. We have discovered that New Zealand has the lowest rate of phishing among Singapore, Australia and the United States over a 9 month period.
