Detection of phishing websites

This document summarizes Shreya Gopal Sundari's project on detecting phishing websites using machine learning techniques. The objectives are to collect a dataset of phishing and legitimate URLs, extract relevant features from the URLs, train machine learning models on the dataset, and evaluate the models' performance in classifying URLs as phishing or legitimate. Key steps include collecting 5000 phishing and 5000 legitimate URLs, extracting 17 features related to the URLs and websites, training models like decision trees, random forests, neural networks and support vector machines, and finding that XGBoost achieved the best accuracy. Potential next steps are developing a browser extension or GUI to classify new URLs.

Web security involves protecting information transmitted over the internet from attacks like viruses, worms, trojans, ransomware, and keyloggers. Users can help secure themselves by using antivirus software, avoiding phishing scams, and reporting spam. Larger attacks often involve botnets, which are networks of infected computers that can overwhelm websites and services with traffic through distributed denial of service attacks.

Web spoofing involves masquerading as another user or program to gain an illegitimate advantage. There are several types of spoofing, including IP spoofing where the sender address is falsified, and web spoofing where an adversary can observe and modify web pages sent to the victim. Defenses include ingress/egress filtering to block spoofed IP addresses, and users verifying URLs and SSL certificates when entering sensitive information online.

The document outlines Prajakta Shinde's seminar on phishing attacks. It defines phishing as attempting to acquire personal information through electronic communication by posing as a trustworthy entity. It discusses common phishing techniques like link manipulation and phone phishing. It also covers types of phishing like deceptive and man-in-the-middle phishing, causes of phishing like user awareness and website vulnerabilities, methods to defend against attacks, and concludes that a combination of user education and security improvements can help reduce phishing.

A man-in-the-middle (MITM) attack intercepts communications between two parties by relaying and controlling messages between them. The attacker eavesdrops and potentially modifies the communication by replacing the keys for their own. This allows them to intercept sensitive transmissions like passwords or financial transactions. A MITM works by spoofing the MAC address of the target to intercept and manipulate traffic between the target and other devices on the network, such as a router. Encrypted connections and careful certificate verification can help prevent MITM attacks.

This document discusses hacking and phishing. It defines hacking and lists common hacking methods like inside jobs, rogue access points, and viruses. It then focuses on phishing, explaining that phishers masquerade as trustworthy entities to steal personal information. The document describes how phishers use software and free web hosting to create fake websites, upload phishing pages, and steal email credentials. It warns readers to think twice before clicking links to avoid falling for phishing scams.

Phishing is a form of hacking that involves using deceptive emails or fake websites to steal user data like login credentials. It works by tricking users into believing they are on legitimate websites by using authentic looking designs. Phishing attacks come in different forms like deceptive, spear, and whaling phishing. Users can protect themselves by being educated on how to identify phishing scams and using security technologies like email filters and firewalls. Organizations should implement layered security and train employees to reduce the impact of phishing attacks.

Seminar on Phishing Attack. This is a cyber crime presentation for Computer science Engineering and Information Technology college students.

This document discusses social engineering techniques used by attackers to trick people into divulging sensitive information or performing actions. It defines key terms and explains why social engineering is a threat even for organizations with strong technical security controls. Common social engineering attack methods are described in detail, including phishing emails, phone calls, dropping infected USB drives, and impersonation. The document emphasizes that education is needed to help people recognize and avoid social engineering tactics.

This document summarizes a face recognition attendance system project. The project uses face recognition technology to take attendance by comparing captured images to stored student records. It has a completed status. The methodology follows a waterfall model. System diagrams include context, data flow, and architecture diagrams. The database stores student data like name, roll number, attendance, and captured images. The system allows for student registration by capturing images, training the model, and recognizing faces to mark attendance. Developing this project provided experience with real-world software development processes.

This document discusses using machine learning and deep learning for malware detection. It notes that over 350,000 new malware are created daily, posing a significant threat. Traditional signature-based detection has limitations in detecting new malware. The document reviews research applying machine learning and deep learning techniques to malware detection using static and dynamic analysis of features. It then describes the authors' approach of using opcode frequency models with random forest and neural networks to classify files, achieving 97-98% precision and recall on a test set. The conclusion is that machine learning and deep learning can help address limitations of traditional approaches by enabling detection of new malware.

Phishing involves using deceptive messages, usually via email or malicious websites, to trick users into providing sensitive personal information. It works by pretending to be from legitimate organizations like banks or retailers. Common goals of phishing are to steal usernames, passwords, credit card numbers, and other financial information. Phishing succeeds due to human vulnerabilities like clicking links without verifying the source, lack of awareness about threats, and weak security practices of organizations. Its negative impacts include identity theft, financial losses, and erosion of trust in online services. Users can help prevent phishing by verifying sources of communications, avoiding providing sensitive details via email, and being wary of unsolicited messages. A combination of user education and improved security technologies is

The document is a presentation on fake news detection. It discusses what fake news detection is, how to identify fake news through both manual and automated methods, and the machine learning approaches used in automated detection. It describes the tools and technologies used to develop their fake news detection platform, including Python programming language, NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn libraries. Their platform was developed using Jupyter Notebook for its interactive and shareable environment. The presentation concludes with demonstrating how their platform works and its high accuracy rate.

This presentation contains Introduction of Phishing attack, its types and Various techniques, their impact with real live example, after that its Avoidance, Prevention and Solution. Also it contains brief introduction of SSL and HTTPS with their working.

Slides for a college course at City College San Francisco. Based on "Hands-On Ethical Hacking and Network Defense, Third Edition" by Michael T. Simpson, Kent Backman, and James Corley -- ISBN: 9781285454610. Instructor: Sam Bowne Class website: https://samsclass.info/123/123_S17.shtml