25 results about "Spamming" patented technology

Embodiments of the technology described programmatically decrease the number of spam Uniform Resource locators (URLs) that are accessed from untrusted domains when the subdomain prefix is above a threshold probability of having been randomly generated. In this regard, prior to adding a discovered set of URLs to a crawl queue of a web crawler, the URLs are filtered into URLs from trusted domains and untrusted domains determined by a statistical model. The trusted domain URLs are added to the crawl queue, and the remaining URLs are sandboxed to filter out spam URLs. The subdomain prefixes of the sandboxed URLs are applied to a neural network to determine the probability that the subdomain prefixes are randomly generated. When a subdomain prefix is above a threshold probability of having been randomly generated, the subdomain is determined to be a spam subdomain and can be blocked.

To provide a training mail system capable of performing actually experienced junk mail training and junk mail education for acquiring knowledge by viewing contents in cooperation with each other.SOLUTION: A training mail system 100 includes user terminals 110, manager terminals 120, and a server 130, and browsers 121 of the manager terminals 120 display a training execution setting screen 124 on which training name information TG1 and content information ED2 for training can be freely designated. When the educational name information ED1, the trainee name information TG1, and the educational content information ED2 are inputted and designated on the educational implementation setting screen 124, the server 130 registers the educational name information ED1, the trainee name information TG1, and the educational content information ED2, and the server 130 transmits the educational guidance e-mail EDM related to the educational content information TG1 to the user terminals 110 based on the trainee information associated with the registered trainee name information ED2.SELECTED DRAWING: Figure 1

The embodiment of the invention provides a junk mail filtering method and device, equipment, a storage medium and a program product. The method comprises the following steps: preprocessing mail contents of mails to obtain mail text information related to mail classification; text feature extraction is conducted on the mail text information, a text feature vector corresponding to the mail text information is generated, the text feature vector comprises weight values corresponding to a plurality of segmented words in the mail text information, and the weight values are used for indicating the probability that the segmented words belong to spam content; calling a trained mail classification model according to the text feature vector, and determining mail classification information corresponding to the mail; the mail classification model is obtained by training based on text feature vectors of a plurality of historical mails; and if the mail classification information is a normal mail type, receiving the mail, and if the mail classification information is a junk mail type, rejecting the mail. According to the method, the spam filtering accuracy is improved.

Aspects of the disclosure relate to detecting and protecting against cybersecurity attacks using unprintable tracking characters. A computing platform may receive a character-limited message sent to a user device. Subsequently, the computing platform may detect that the character-limited message sent to the user device includes suspicious content. Then, the computing platform may generate a modified character-limited message by inserting one or more special characters into the character-limited message and cause transmission of the modified character-limited message to the user device. Next, the computing platform may receive, from the user device, a spam report that includes the modified character-limited message. Then, the computing platform may identify a presence of the one or more special characters included in the modified character-limited message and adjust one or more filters based on the identification.

The application provides an email threat perception system, belonging to the field of network security and email protection, and researches and practices email security threat perception technology, utilizes an email behavior detection model and a machine learning model to perform multi-dimensional and multi-level deep analysis on emails, so as to identify abnormal email behaviors, discover phishing links and sensitive contents, etc. On this basis, an active and low false alarm rate email security threat perception system is realized, which detects and filters spam emails, phishing emails and emails containing sensitive contents, and improves the security of email applications.

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a request for a plurality of data items from a computing device; determining at least one pattern for at least one metric associated with the computing device; generating at least one digital code based on the at least one pattern meeting a predetermined metric threshold, the at least one digital code comprising access to a plurality of resources; assigning the at least one digital code to the computing device to form at least one assigned digital code; and allowing access to the plurality of resources to facilitate performing a domain name availability determination for the plurality of data items.

Systems and methods for determining whether a linked content page may include spamming, malicious, and / or otherwise undesirable content. The linked content page may be crawled, scraped, and / or parsed to extract various information associated with the text, media items, and / or structure of the linked content page. The text, media, and / or structure information may be analyzed and processed to generate one or more textual features, media features, and / or structural features, which may then be processed by a trained machine learning model to determine whether the content page includes spamming, malicious, and / or otherwise undesirable content.

The disclosed invention pertains to a software system for secure digital communication. It involves generating and assigning a unique email address, structured with a prefix, domain and / or subdomain, to an external user for communication with an internal user. The system monitors subsequent communication, comparing it with the assigned email address to detect any compromise. In case of detected malicious intent, the system initiates appropriate countermeasures, enhancing the overall security of digital communication.

The embodiment of the invention provides an E-mail secure delivery method and related equipment, and belongs to the technical field of information security. The method comprises the following steps: carrying out explicit content extraction on a received email to obtain email explicit information; performing first classification processing on the emails according to the email explicit information to obtain a first classification result; when the first classification result represents the junk mail, intercepting the E-mail; under the condition that the first classification result shows the credible email, delivering the email to a recipient mailbox; and under the condition that the first classification result shows the suspicious mail, delivering the e-mail to a recipient mailbox, and carrying out second classification processing on the link pointing content of the link domain name in the e-mail to obtain a second classification result. According to the invention, the E-mail delivery security and timeliness can be improved.

The invention relates to a phishing mail interception system and method, and belongs to the technical field of information security, and the system comprises a mail preprocessing module which is used for receiving a mail in an HTML format, carrying out the structural analysis of the mail, and carrying out the preprocessing of the analyzed mail, and obtaining a target mail; the feature extraction and recognition module is used for extracting CSS attributes related to text hiding in a target mail, performing feature vector fusion and standardization on the extracted features to obtain a target feature vector, predicting the target feature vector based on a hybrid model of SVM and LSTM to obtain a prediction result of the mail, and outputting the prediction result of the mail. The prediction result comprises a judgment result and confidence of a normal mail or a suspicious CSS hidden mail; and the multi-dimensional decision-making module is used for acquiring the attribute information of the sender of the mail, and comprehensively evaluating the prediction result based on the attribute information to obtain the decision-making result of the mail. According to the system, the junk mail recognition rate is improved, and information leakage is reduced.

This invention discloses a spam classification method, apparatus, device, and storage medium, implemented through an extraction module, a detection module, a classification module, and a disposal module. The extraction module can be customized according to the specific needs of spam classification. The detection module employs multiple fusion feature vector techniques based on an LLM pre-trained model and feature engineering to achieve multimodal detection of spam metadata, text, images, hyperlinks, and visual data. The classification module, based on the fusion feature vectors, uses TabNet to enhance the ability to learn features and further subdivide spam / emails. Simultaneously, the disposal module provides user-customizable configurations to meet users' needs for customized and automated spam / email disposal. This solution not only improves the accuracy of spam detection but also provides more personalized settings, enabling users to better manage email classification rules, reduce false positives, and ensure that valuable emails are not misclassified.

The invention provides a junk mail identification method and device, electronic equipment and a storage medium. The junk mail identification method comprises the following steps: in response to a detected first mail sent to a first receiving end, detecting the first mail according to a detection rule; sending the first mail to the first receiving end according to a normal mail mode in response to the condition that the time consumed for detecting the first mail reaches a preset duration or the detection of the first mail fails, and continuing to asynchronously detect the first mail according to the detection rule to determine whether the first mail is abnormal; processing the first mail in the first receiving end in response to the fact that the result of asynchronously detecting the first mail shows that the first mail is abnormal; and in response to the received related information of the abnormal mail, determining historical mails which conform to the related information and are not determined to be abnormal from the historical mails of the first receiving end according to the related information, and processing the historical mails. According to the method disclosed by the invention, the delay of receiving the first mail by the user can be avoided, and historical mails can be traced.

A method for detecting Arabic phishing emails includes connecting a log management solution to an email management solution that includes a spam repository and one or more emails. The spam repository includes one or more Indicators of Compromise (IoCs) that is digital information associated with a cyberattack. An IoC index of the log management solution is populated with the IoCs. The log management solution searches the emails using the IoC index and flags one or more potential phishing emails when an email matches an IoC in the IoC index. The log management solution assigns an authenticity score to each of the potential phishing emails and generates a potential phishing email report containing the potential phishing emails and the authenticity score assigned to each of the potential phishing emails. Based on the potential phishing email report, an assigned user performs one or more remediation actions.

According to one embodiment, the electronic device may include a communication circuit, a memory that stores instructions and includes one or more storage media, and at least one processor that includes a processing circuit. When the instructions are executed individually or collectively by the at least one processor, the electronic device may be caused to receive a message from an external electronic device using the communication circuit, identify the message as an allowed message using the content of the message and the sender number of the message, identify a category of the message and a filtering level set according to the category based on the content of the message, obtain information regarding the probability that the message is a spam message that is distinguished from the allowed message, identify the message as the spam message based on identifying that the probability is within the probability range according to the filtering level, and maintain the message as the allowed message based on identifying that the probability is outside the probability range according to the filtering level.

Embodiments of a method for facilitating spam blocking in a tiered software framework include: providing instructions to a machine learning module (MLM) to generate a threshold for classifying spam in messages generated in a tiered software framework. The instructions include inputs comprising government regulations; carrier guidelines; feedback on previously sent messages; and previously flagged messages. The instructions specify that the threshold is to prevent false positives while allowing false negatives. The method further includes, receiving the threshold according to the instructions from the MLM; parsing a message; automatically performing a semantic search using natural language processing for regulated content in the parsed message by comparing semantics of text of the parsed message to the inputs to find matches; assigning a score to the message based on matches found; and responsive to the score being higher than the threshold, blocking sending the message from the tiered software framework.

The invention relates to the field of mail recognition, and particularly provides a junk mail recognition method based on multi-modal features and adaptive learning, which comprises the following steps: S1, analyzing an input mail, extracting multi-modal feature vectors in parallel, and constructing a comprehensive feature vector; s2, inputting the extracted multi-modal feature vector into a feature fusion network based on attention to generate a unified mail comprehensive feature representation; s3, inputting the comprehensive feature representation into a classifier to obtain a prediction result and confidence of whether the mail is a junk mail; and S4, continuously operating the system, and driving online optimization of the model by utilizing feedback data. According to the scheme, high-performance and self-evolution intelligent spam filtering is realized.

A messaging system for detecting spam and inserting data into an SMS message that includes a message handling module and an antispam detection module where the antispam module detects whether a message to be sent comprises spam and inserts spam data into the message when the message is determined to be spam. The message handling module breaking the message into segments if the message exceeds a threshold length and inserting concatenated message data into at least one of the message segments. The message segment are then transmitted without being saved on a storage or database.

The invention provides a junk mail detection method, electronic equipment, a chip, a storage medium and a program product. The method comprises the following steps: acquiring a to-be-detected mail, and extracting label type features and text type features of the to-be-detected mail; converting the label class features into embedding vectors with fixed dimensions, and generating binary values; utilizing a Skip-gram word embedding model to convert the text type features into a word embedding sequence; and fusing the embedded vector, the input scalar and the feature word embedded sequence by using a pre-constructed convolutional neural network to form a fused feature vector, predicting a probability value of a junk mail by using a Sigmoid activation function in combination with the fused feature vector, and judging that the mail is a junk mail if the probability value is greater than a preset threshold.

The disclosed invention pertains to a software system for secure digital communication. It involves generating and assigning a unique email address, structured with a prefix, domain and / or subdomain, to an external user for communication with an internal user. The system monitors subsequent communication, comparing it with the assigned email address to detect any compromise. In case of detected malicious intent, the system initiates appropriate countermeasures, enhancing the overall security of digital communication.

Methods, systems, and devices are provided for detecting spam filtering. According to one aspect, the system can inject, by a server, into a client email campaign a plurality of emails for transmission to a plurality of seed email addresses separate from a plurality of non-injected emails of the client email campaign for transmission to a plurality of target email addresses. The system can detect whether the one or more injected emails were categorized as spam or not spam by each of the plurality of seed email addresses and generate a first input to an analysis module. The system can monitor a plurality of sensing signals from the non-injected emails and generate a second input to the analysis module. The analysis module can predict based on the first input and second input whether the non-injected emails are categorized as spam by the target email addresses.

This invention discloses a general method for scanning and sending data to applications, relating to the field of data processing technology. The method includes the following steps: scanning device sending emails, parsing and filtering emails, data processing, file processing and storage, file sending and encapsulation, process control, and log management. In this invention, the SMTP email receiving service module is used to parse the email structure, filter valid scanned data, and block spam; the data processing and parsing module is used to extract user identity information and scan metadata, completing identity matching and metadata standardization; the file processing module is used to manage temporary file storage, realize intelligent conversion of scanned file formats, and ensure compliance with application requirements; the file sending service module is used to encapsulate files and metadata, call application interfaces for transmission, and determine the sending status; the process control module is used to monitor the process, perform dynamic retries, and clean up temporary files; and the log management unit records the entire process to ensure traceability.

The application discloses an education network user personalized mail processing method and system based on a large model, and belongs to the technical field of large models. The method is as follows: a user's social relationship database is constructed according to paper information, communication relationships and user historical operations in the last period; based on a currently received mail, a plurality of prompt words are extracted from the social relationship database; a preset large model is used to predict the user's mail receiving demand based on the prompt words, the currently received mail is analyzed according to the mail receiving demand, and the academic relevance degree and mail receiving suggestion of the currently received mail are output; and based on the academic relevance degree and mail receiving suggestion, the currently received mail is delivered to a corresponding mailbox directory using a preset mail screening condition. Therefore, by implementing the application, the problem of a high misjudgment rate of junk mail in the education network user scenario in the prior art can be solved.

The application belongs to the technical field of natural language processing, and particularly relates to a text detection method based on multi-granularity clustering, which comprises the following steps: obtaining a text data set and preprocessing the text data set to obtain a preprocessed text data set, and performing word segmentation on the preprocessed text data set to obtain a word library; adopting a granular ball purity self-adaptive method to cluster all words in the word library to obtain a synonym set; adopting the synonym set to perform disturbance processing on the preprocessed text data set to obtain an adversarial text data set; constructing an information detection model, and adopting the adversarial text data to perform adversarial training on the information detection model; after the training is completed, inputting to-be-processed text data into the trained information detection model to output a detection result; the application proposes a more efficient and more robust text detection method, which can improve the accuracy of recognizing spam mails and spam information, and significantly reduce the workload of manual screening, and brings an innovative solution to the field of natural language processing.

Systems and processes are disclosed for a multi-layered approach to fraud prevention by leveraging a machine learning engine integrated with the Session Initiation Protocol (SIP) to attempt caller identification before transitioning to a voice call, allowing for the potential blocking of unwanted calls. If SIP-based identification remains inconclusive, an anomaly detection engine employing the Viterbi algorithm analyzes the caller's speech patterns during voicemail messages. The Viterbi algorithm converts spoken language into text, identifying suspicious characteristics such as unusual speech patterns, inconsistencies, and keywords associated with scams. If suspicious characteristics are detected, the system automatically blocks callback attempts and notifies the customer of potential spam or unwanted calls. This proactive approach addresses both live and recorded fraudulent calls, enhancing the security of telecommunications by preventing fraudulent interactions before they can cause harm. The system continuously learns from new data, adapting to evolving fraud tactics, providing robust, long-term protection for telecommunications users.

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a request for a plurality of data items from a computing device; determining at least one pattern for at least one metric associated with the computing device; generating at least one digital code based on the at least one pattern meeting a predetermined metric threshold, the at least one digital code comprising access to a plurality of resources; assigning the at least one digital code to the computing device to form at least one assigned digital code; and allowing access to the plurality of resources to facilitate performing a domain name availability determination for the plurality of data items.