75 results about "Spamming" patented technology

The invention relates to the technical field of network security, in particular to a mail content risk assessment method and system based on a triple cascade model and a generative adversarial network, and the method comprises the steps: collecting mail content sample data, and carrying out the preprocessing of the mail content sample data; constructing a triple cascade model composed of a first model, a second model and a third model; training the triple cascade model by using the mail content sample data, generating an adversarial sample of the mail content sample data through a GAN adversarial network, optimizing the first model and the second model by using the adversarial sample, and taking the trained and optimized triple cascade model as a mail risk assessment model; and inputting the text features of the to-be-detected mail content into the mail risk assessment model to identify the risk level of the to-be-detected mail content. According to the method, malicious contents such as junk mails in a target network are identified through cascade detection of lightweight filtering, medium complexity analysis and deep semantic understanding, and on-demand allocation of computing resources can be realized.

To provide a system.SOLUTION: A system comprises: means for analyzing the content of a junk mail; means for analyzing the transmission mail address of the junk mail; and means for blocking the junk mail on the basis of the analysis result.SELECTED DRAWING: Figure 1

Disclosed are systems and methods that monitor for malicious and unauthorized behaviors, determine categories for detected malicious behaviors, determine why a domain is determined to be malicious, and provide information to users that identifies the categories and reasons as to why a domain is determined to be malicious. In some implementations, the disclosed systems and methods may be utilized to provide monitoring security to customers of a cloud service. For example, customers of a cloud service may maintain an account with the cloud service and the disclosed implementations may be utilized to protect those accounts from malicious attacks and cybercrimes such as, but not limited to, spam, phishing, malware, botnets, etc.

The invention discloses a multi-language junk mail detection method and system and terminal equipment. The method comprises the following steps: obtaining mail data to be detected; inputting the mail data into a preset semantic recognition model to convert a first text feature extracted from the mail data into a text matrix, and determining a corresponding first text vector based on the text matrix; and inputting the first text vector into a preset junk mail detection model to determine a probability value of the mail data belonging to junk mails based on the first text vector, and determining the junk mails based on the probability value, the junk mail detection model is obtained by training according to multi-dimensional features in a second mail training set and a second text vector, and the second text vector is determined by reasoning second text features in the second mail training set according to the semantic recognition model. According to the invention, the accuracy and efficiency of multi-language junk mail detection can be improved.

Emails or other communications are labeled with a category label such as “spam” or “good” without using confidential or Personally Identifiable Information (PII). The category label is based on features of the emails such as metadata that do not contain PII. Graphs of inferred relationships between email features and category labels are used to assign labels to emails and to features of the emails. The labeled emails are used as a training dataset for training a machine learning model (“MLM”). The MLM identifies unwanted emails such as spam, bulk email, phishing email, and emails that contain malware.

The invention discloses a junk mail classification method, device and equipment and a storage medium. The junk mail classification method, device and equipment are realized through an extraction module, a detection module, a classification module and a disposal module. The extraction module can be determined according to specific requirements of junk mail classification, the detection module adopts multiple fusion feature vector technologies based on an LLM pre-training model and feature engineering to realize multi-modal detection of metadata, texts, pictures, hyperlinks and vision of junk mails, and the classification module can perform classification on the junk mails based on fusion feature vectors. By using a TabNet mode, the capability of learning features is improved, further subdivision of the junk mails / mails is realized, and meanwhile, a disposal module provides custom configuration for a user, so that the customized and automatic disposal requirements of the user on the junk mails / mails can be met. According to the scheme, the detection accuracy of junk mails is improved, more personalized settings can be provided, a user can better manage mail classification rules, misinformation is reduced, and it is ensured that valuable mails cannot be classified mistakenly.

Systems and methods are provided for providing an anti-spam artificial intelligence service. Initially, a message is received at a user equipment (UE) corresponding to a user. Text corresponding to the message is forwarded to an anti-spam engine to determine if the message is spam. A response is received, at the UE, from the anti-spam engine. The user is prompted, at the UE, to select an action to perform corresponding to the message. In various aspects, the action is to ignore, delete, or read the message.

To provide a system for effectively detecting spam emails.SOLUTION: A system for learning and detecting characteristics of spam includes data processing means for analyzing email header information and body text, machine learning algorithm for learning the characteristics of spam, and means for classifying a new email as spam using a learned model.SELECTED DRAWING: Figure 1

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.

The invention discloses a junk mail detection method and system and terminal equipment, and the method comprises the steps: carrying out the text recognition and extraction of uploaded mail data according to a data type, and obtaining to-be-detected information; performing anti-spam detection, address detection and anti-virus detection on different contents of the to-be-detected information to obtain a mail analysis log; and inputting the cut mail analysis log into a preset language analysis model to interpret a mail detection result, and generating a risk judgment result of the mail analysis log and a corresponding mail processing suggestion. According to the method, the junk mail can be identified by uploading the original mail or the image, the mail detection result and related processing suggestions which are more convenient for the user to understand are generated, and the use experience of the user is improved.

The present invention is a method and device for filtering spam based on a large language model, and the method comprises the following steps: 1) email preprocessing to extract the content of received emails, including the body, attachments and metadata; 2) feature extraction and vectorization: converting the email content into a high-dimensional semantic vector through a large language model to capture the semantic information and contextual relationship of the email content; 3) classification and screening: using the large language model combined with a fine-tuned spam classifier to classify emails into spam and non-spam, and the classifier makes a comprehensive judgment based on contextual semantics, sending behavior patterns and metadata features; 4) feedback mechanism: introducing user feedback and model adaptive update mechanism to improve model performance through feedback learning. The present invention improves the accuracy and adaptability of spam filtering by introducing a pre-trained large language model.

Techniques herein balance the need for flexibility with the need accuracy, using a reactive approach to viral spam detection. After content (e.g., a social media platform news feed or timeline post) is created, interaction activity (e.g., content views) with the content is monitored. Based on the monitoring of the interactivity activity, it is determined whether a reactive viral spam analysis condition is satisfied for the content (e.g., because the number of content views exceeds a threshold). In response to determining that the reactive viral spam analysis condition is satisfied, a determination is made whether the content is or is not viral spam. If the content is determined to be viral spam, then it may be reported or flagged for further action (e.g., take down after manual confirmation).

The system receives a request for communication from an originator UE. The request for communication includes a unique identifier of the originator UE and a unique identifier of a receiver UE. The system obtains profile information of the originator UE including a name or a region of the originator UE. The system obtains profile information of the receiver UE including a communication history or calendar entry of the receiver UE. Based on the profile information of the originator UE and the receiver UE, the system determines whether the communication is spam. If the communication is valid, the system routes the communication to the originator UE. If the communication is spam, the system indicates to the receiver UE that there is an incoming communication that is likely spam. The system stores in a database the unique identifier of the originator UE and the determination of whether the communication is spam.

The system receives a request for communication from an originator UE. The request for communication includes a unique identifier of the originator UE and a unique identifier of a receiver UE. The system obtains profile information of the originator UE including a name or a region of the originator UE. The system obtains profile information of the receiver UE including a communication history or calendar entry of the receiver UE. Based on the profile information of the originator UE and the receiver UE, the system determines whether the communication is spam. If the communication is valid, the system routes the communication to the originator UE. If the communication is spam, the system indicates to the receiver UE that there is an incoming communication that is likely spam. The system stores in a database the unique identifier of the originator UE and the determination of whether the communication is spam.

A machine learning (ML) model (200) is used to identify spam messages and spammers in a computer game setting based on learned gamer behavior. An evaluator module (500, 502) receives a score(s) from the ML model indicating whether a particular message is spam or other undesirable, and if the score meets a threshold, passes the message to a punishment module (412) to determine whether to warn, suspend, or ban the sending account.

A system for analyzing email deliverability and security includes a user computational device connected to a server gateway via a network. The server gateway directs emails to an accessible server and determines if an email recipient inbox has received the email and its categorization, such as SPAM. An analysis engine analyzes the category and content of each email to identify connections between them. The system also features a probe sender that sends test emails based on user instructions. Additionally, the system may directly access email inboxes, interface with multiple email servers, and include servers for collecting additional email data and cybersecurity information. The server gateway can connect to various email data sources and interfaces, including IMAP and JSON based APIs, and may utilize OAuth and OIDC protocols for programmatic email label and folder determination.

To provide a system capable of effectively managing improper content and spam in an online community.SOLUTION: A system comprises a detection unit, a deletion unit, an identification unit, a block unit, a classification unit, a tagging unit, and a guidance unit. The detection unit detects contents which have been determined to be improper from among contents posted on an online community. The deletion unit deletes contents which have been determined to be improper. The identification unit determines whether the content to be posted is a content containing a spam content or a content including an unnecessary link. The block unit blocks the posting of content which has been determined to a content containing a spam content or an unnecessary link. The classification unit classifies a content into related topics. The tagging unit assigns a tag to the classified contents. The guidance unit guides a community guide and rules to a new member.SELECTED DRAWING: Figure 1

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 invention provides a document classification method and device based on a large model, an intelligent agent and electronic equipment, and relates to the technical field of artificial intelligence, in particular to the technical fields of natural language processing, large models and the like. The method can be applied to scenes of enterprise internal document management based on artificial intelligence, patient record management of medical institutions, news classification, social media topic classification, spam filtering, medical literature classification, legal document classification and the like. According to the specific implementation scheme, an input document is obtained; determining the document type of the input document, wherein the document type is divided into a streaming document and a format document; generating corresponding target prompt word information according to the document type; document classification is conducted on the input document based on the target prompt word information through the multi-modal large model, the category of the input document is obtained, and the category of the input document refers to the semantic category of the input document. The prediction performance of the document classification system can be improved, and the prediction time can be shortened.

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a notification to engage a transmission guard bot in a communication session; generating a trained transmission guard bot configured with communication capabilities to engage the communication session; engaging the trained transmission guard bot into the communication session between the plurality of computing devices associated with the plurality of users; utilizing the trained transmission guard bot to continually monitor a plurality of content data associated with the communication session for a predetermined period of time; detecting a suspicious content data point within the plurality of content data associated with the communication session based on the continual monitoring of the at least one transmission guard bot; determining a risk assessment metric associated with the communication session; and invoking an action related to spam prevention via the at least one trained transmission guard bot.

To protect security and privacy of an elderly person by, using a chat GPT application residing in an elderly people-friendly smartphone: analyzing contents of junk mails and solicitation calls; ranking malicious ones thereof; and notifying his or her family of the ranked malicious ones.SOLUTION: There is provided a system configured to, using a chat GPT application residing in an elderly people-friendly smartphone: analyze contents of junk mails or solicitation calls; rank malicious ones thereof; and notifies his or her family of the ranked malicious ones. This system includes means for: receiving the contents of junk mail or solicitation call; and, upon the analysis by the chat GPT application having detected the malicious ones, notifying his or her family of the malicious ones.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.

The present invention relates to a marketing email delivery system and method, belonging to the technical field of email delivery. The delivery system includes an email content generation module, an email delivery module, an email prediction module, a collection and analysis module, and a customer feedback module. The present invention realizes personalized email content generation and multi-modal email content detection through the email content generation module, reducing the costs of manual writing of email template content and manual review of email content. The email delivery module and the email prediction module are used to predict the email content elements before delivery, and make a decision to deliver or cancel the delivery of the current email based on the prediction score, thereby improving the overall delivery success rate of emails. The collection and analysis module realizes continuous optimization of email content and delivery success rate and effectively supplements the local spam feature library. The customer feedback module reduces the complaint rate and improves the customer experience.

A spammy app detection system may search a database for any new social media application discovered during a recent time period. A spammy app detection algorithm can be executed on the spammy app detection system on an hourly basis to determine whether any of such applications is spammy (i.e., posting to a social media page anomalously). The spammy app detection algorithm has a plurality of stages. When a new social media application fails any of the stages, it is identified as a spammy app. The spammy app detection system can update the database accordingly, ban the spammy application from further posting to a social media page monitored by the spammy app detection system, notify an entity associated with the social media page, further process the spammy application, and so on. In this way, the spammy app detection system can reduce digital risk and spam attacks.

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 invention provides a federated learning method for a junk mail detection model based on model splitting cooperation, and the method comprises the steps: carrying out the clustering of nodes based on the similarity of a feature matrix of a training sample in each node, and obtaining a plurality of node clusters; performing sub-network splitting on the junk mail detection model to obtain a plurality of sub-networks; different node subsets are selected from each node cluster to train different sub-networks, and sub-network parameters are fed back; and on the basis of the feedback information, global model parameters are aggregated and generated until training is terminated, final global model parameters are output, and a trained junk mail detection model is acquired. Therefore, compared with a traditional federated learning method, the method can generate the junk mail detection model based on less model training workload, lower hardware resource consumption, less model training and communication time consumption and higher parameter convergence speed, so that the method can be applied to scenes with limited calculation, storage and network communication resources.

Described herein are techniques, devices, and systems for utilizing subscriber and user equipment (UE) related content to generate limitations of rich communications service (RCS) capabilities as antispam security measures. The RCS capabilities limitations can be utilized to strip RCS capabilities from possible malicious UEs. The UEs can be identified as the possible malicious UEs by scanning telecommunications networks for behavior of the UEs that is identified as being possible malicious behavior. Stripping the RCS capabilities can include downgrading capabilities of the possible malicious UEs from RCS capabilities to short messaging service (SMS) / multimedia messaging service (MMS) capabilities. Communications associated with the possible malicious UEs being identified as malicious UEs can be monitored and analyzed to restrict communication of communications identified as malicious messages.

To improve accuracy of creating spam email text in a message creation system using generative AI.SOLUTION: A message creation system using generative AI, includes means for providing learning data, means comprising a feedback function for improving accuracy of a generated message, and means for generating spam email text.SELECTED DRAWING: Figure 1

The invention discloses an improved GADT model auxiliary junk mail detection method based on a genetic algorithm, and relates to the technical field of junk mail detection and classification, and the method comprises the following steps: S10, carrying out structured preprocessing on an input text, including text standardization, stop word deletion and stem extraction; according to the method, feature space redundancy and noise are effectively reduced, key semantic information is reserved, and the data scale is compressed by performing structured preprocessing on the mail text and combining TF-IDF feature coding and PCA dimension reduction; a decision tree pruning parameter confidence factor is adaptively optimized by using a genetic algorithm, the complexity and generalization ability of the decision tree are dynamically balanced, and the classification accuracy and the model robustness are remarkably improved; the feature dimension reduction and model optimization cooperate to reduce the training reasoning complexity and improve the detection real-time performance, and the method is significantly superior to the prior art in accuracy, robustness and calculation efficiency, and has good expansibility and application and popularization value.

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.