Effective Phishing Detection Using Machine Learning Approach

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

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.

This book is a collection of outstanding content written by experts working in the field of multimedia security. It provides an insight about various techniques used in multimedia security and identifies its progress in both technological and algorithmic perspectives. In the contemporary world, digitization offers an effective mechanism to process, preserve and transfer all types of information. The incredible progresses in computing and communication technologies augmented by economic feasibility have revolutionized the world. The availability of efficient algorithms together with inexpensive digital recording and storage peripherals have created a multimedia era bringing conveniences to people in sharing the digital data that includes images, audio and video. The ever-increasing pace, at which the multimedia and communication technology is growing, has also made it possible to combine, replicate and distribute the content faster and easier, thereby empowering mankind by having a wealth of information at their disposal. However, security of multimedia is giving tough time to the research community around the globe, due to ever-increasing and efficient attacks carried out on multimedia data by intruders, eves-droppers and hackers. Further, duplication, unauthorized use and mal-distribution of digital content have become a serious challenge as it leads to copyright violation and is considered to be the principal reason that refrains the information providers in freely sharing their proprietary digital content. The book is useful for students, researchers and professionals to advance their study.

The two volume set LNCS 5506 and LNCS 5507 constitutes the thoroughly refereed post-conference proceedings of the 15th International Conference on Neural Information Processing, ICONIP 2008, held in Auckland, New Zealand, in November 2008. The 260 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. 116 papers are published in the first volume and 112 in the second volume. The contributions deal with topics in the areas of data mining methods for cybersecurity, computational models and their applications to machine learning and pattern recognition, lifelong incremental learning for intelligent systems, application of intelligent methods in ecological informatics, pattern recognition from real-world information by svm and other sophisticated techniques, dynamics of neural networks, recent advances in brain-inspired technologies for robotics, neural information processing in cooperative multi-robot systems.

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.

The research domains like Computing, Communication, Control and Automation has led to exponential increase in the number of people using these technologies and also their interest in research and development activities To prepare ourselves for this global competition, Pimpri Chinchwad College of Engineering, Pune has conceptualized the 4th International Conference on Computing Communication Control and Automation (ICCUBEA) 2018 under IEEE Pune Section during 16th to 18th August, 2018 This three days International Conference ICCUBEA 2018 will focus on the latest research trends and applications in the domains of Computing, Communication, Control and Automation This conference is designed to provide a common platform to the academicians, research scholars, industry experts and students to spread knowledge on scientific research in Interdisciplinary areas Also the pre conference tutorials by the esteemed experts will enrich the technical takeaways for the delegates

This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.

This volume constitutes the thoroughly refereed conference proceedings of the 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligend Systems, IEA/AIE 2012, held in Dalian, China, in June 2012. The total of 82 papers selected for the proceedings were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on machine learning methods; cyber-physical system for intelligent transportation applications; AI applications; evolutionary algorithms, combinatorial optimization; modeling and support of cognitive and affective human processes; natural language processing and its applications; social network and its applications; mission-critical applications and case studies of intelligent systems; AI methods; sentiment analysis for asian languages; aspects on cognitive computing and intelligent interaction; spatio-temporal datamining, structured learning and their applications; decision making and knowledge based systems; pattern recognition; agent based systems; decision making techniques and innovative knowledge management; machine learning applications.

MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.

This book constitutes the refereed proceedings of the 4th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2007, held in Lucerne, Switzerland in July 2007. The 14 revised full papers presented were carefully reviewed and selected from 57 submissions. The papers are organized in topical sections on Web security, intrusion detection, traffic analysis, network security, and host security.