Advanced SIP-based caller identification and voicemail analysis system for fraud prevention in telecommunications

An artificial intelligence (AI) system for phishing detection and call screening, comprising a storage device storing:

• a web crawler configured to continuously scan an internet and retrieve web pages potentially associated with phishing activities by matching predefined criteria indicative of phishing attempts;
• a web scraping agent operatively connected to the web crawler, configured to extract content from the retrieved web pages, including text, images, and metadata;
• a feature engineering module configured to process the extracted content to generate a set of features indicative of phishing characteristics, the phishing characteristics comprising suspicious Uniform Resource Locators (URLs), unusual domain names, and specific keywords commonly found in phishing attacks;
• a machine learning model comprising a random forest algorithm trained on a dataset of known phishing and legitimate web pages, configured to classify the web pages as phishing or legitimate based on the generated set of features;
• a real-time detection engine operatively connected to the machine learning model, configured to apply the machine learning model to the set of features and provide real-time phishing detection and alerts;
• a model update module configured to periodically update the machine learning model with new data to improve accuracy and adapt to emerging phishing tactics;
• a reporting module configured to generate reports on detected phishing activities, including details on the classified phishing web pages, the set of features used for classification, and confidence scores;
• a feedback mechanism integrated into the web crawler and web scraping agent, configured to refine their operations based on the detected phishing activities and user inputs;
• a compliance module configured to ensure that the system adheres to relevant data protection regulations throughout a phishing detection process;
• a user interface configured to allow users to review and manage the detected phishing activities, report false positives, and confirm detections;
• a Session Initiation Protocol (SIP) module configured to process SIP invites with detailed authentication information about a caller, including identity, location, and call initiation timestamp;
• a caller identification engine comprising a machine learning model trained on a dataset of legitimate and fraudulent calls, configured to analyze the SIP invites and determine legitimacy of the caller;
• a call blocking module operatively connected to the caller identification engine, configured to block calls at an initiation stage if the calls determined to be likely unwanted or spam;
• an anomaly detection engine employing a Viterbi algorithm, configured to analyze caller speech patterns during voicemail messages if analyzing the SIP invites remains inconclusive;
• a voicemail analysis module configured to identify suspicious characteristics in voicemail analysis, the suspicious characteristics comprising unusual speech patterns, inconsistencies, high-pressure tactics, or keywords associated with spam;
• a callback blocking module configured to automatically block any callback attempts from a recipient if the suspicious characteristics are detected during the voicemail analysis;
• a notification module configured to notify a customer of a potential spam or unwanted call and provide detailed information about a detected threat;
• an adaptive learning module configured to continuously improve detection capabilities of the system by adapting to new scam tactics;
• an integration module configured to ensure compatibility with existing telecommunications infrastructure, enhancing security measures without requiring significant changes;
• and
• an analytics dashboard for service providers, configured to provide insights and analytics on call patterns, characteristics of spam and fraud, and metrics on call blocking rates.

An artificial intelligence (AI) method for phishing detection and call screening using web crawling, web scraping, machine learning, and real-time speech analysis, comprising the steps of:

• deploying a web crawler to identify and retrieve web pages potentially associated with phishing activities;
• using a web scraping agent to extract content from the retrieved web pages;
• performing feature engineering on the extracted content to generate a set of features indicative of phishing characteristics;
• training a random forest algorithm on a dataset comprising known phishing and legitimate web pages to create a trained phishing detection model;
• applying the trained phishing detection model to the set of features to classify the web pages as phishing or legitimate;
• integrating the phishing detection model into a system's security infrastructure to provide real-time phishing detection and alerts;
• updating the phishing detection model periodically with new data to improve accuracy and adapt to emerging phishing tactics;
• generating a report on detected phishing activities, including details on the classified phishing web pages and the set of features used for classification;
• implementing a feedback mechanism to refine the web crawler and web scraping agent based on the detected phishing activities and user inputs;
• ensuring compliance with relevant data protection regulations throughout a phishing detection process;
• providing an interface for users to review and manage the detected phishing activities;
• deploying a machine learning engine integrated with a Session Initiation Protocol (SIP) to attempt caller identification before transitioning to a voice call;
• augmenting SIP invite with detailed authentication information about a caller, including identity, location, and call initiation timestamp;
• analyzing the SIP invite with the machine learning engine, comparing the SIP invite against known patterns of legitimate and fraudulent calls;
• blocking the call before an initiation stage if the machine learning engine determines the call to be likely unwanted or spam;
• employing an anomaly detection engine using a Viterbi algorithm to analyze caller speech patterns during voicemail messages if analyzing the SIP invite remains inconclusive;
• identifying suspicious characteristics in voicemail analysis, the suspicious characteristics comprising unusual speech patterns, inconsistencies, high-pressure tactics, or keywords associated with spam;
• automatically blocking any callback attempts from a recipient if the suspicious characteristics are detected during the voicemail analysis;
• notifying a customer of a potential spam or unwanted call and providing detailed information about a detected threat;
• continuously improving detection capabilities of the system by adapting to new scam tactics through an adaptive learning process;
• integrating the system with existing telecommunications infrastructure to enhance security measures without requiring significant changes;
• and
• providing insights and analytics to service providers by analyzing call patterns and identifying common characteristics of spam and fraud to develop new strategies for enhancing call security.