Secure communication device based on artificial intelligence

Abstract

The invention discloses a safety communication device based on artificial intelligence, which comprises a processor, and the processor is connected with an encryption module, an identity verification module, an intrusion detection module, a machine learning module, a safety protocol module and a user interface module. The invention belongs to the technical field of intelligent communication, through the combination of the encryption module and the identity verification module, a trust relationship and a data encryption transmission channel can be established between two communication parties, the data security and integrity can be improved, potential malicious activities are monitored and identified in real time through the intrusion detection module, and the security of the communication parties is improved. Hacker attack and data leakage can be effectively prevented, meanwhile, through continuous learning and optimization of the machine learning module, the system can adapt to a new threat mode, flexible security configuration and management are provided, the overall security performance is improved, and the market prospect is wide.

Description

Technical Field

[0001] The present invention relates to the field of intelligent communication technology, and in particular to a secure communication device based on artificial intelligence. Background Art

[0002] With the rapid development of information technology, network communications are facing more and more security threats, such as data leakage, identity fraud, malicious attacks, etc. These threats may not only lead to the loss of personal privacy and property, but also have a serious impact on national security and social stability.

[0003] Most of the current security communication devices are loaded with intelligent systems, but traditional security communication intelligent systems are relatively weak in data encryption and identity authentication, and the system background's supervision of abnormal behavior data and potential threats needs to be improved, resulting in security risks in the security and integrity of data in actual security communication devices, increasing the risk of data leakage due to hacker attacks. Therefore, it is necessary to propose a security communication device based on artificial intelligence. Summary of the invention

[0004] To this end, the present invention provides a secure communication device based on artificial intelligence to solve the above-mentioned problems in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A secure communication device based on artificial intelligence, comprising a processor, wherein the processor is connected to:

[0007] The encryption module is used to establish an encrypted channel and encrypt data transmission in the session channel to ensure the confidentiality and integrity of the data;

[0008] The identity verification module is used for identity verification of both communicating parties to establish a trust relationship;

[0009] Intrusion detection module to monitor and identify potentially malicious activities and behaviors that do not conform to normal patterns;

[0010] The machine learning module is responsible for establishing a behavior model through algorithms, learning and adapting to new attack patterns through the model, and intelligently identifying whether there is abnormal behavior;

[0011] The security protocol module is responsible for defining the specifications and processes of data transmission to ensure the security of data transmission during the communication process;

[0012] User interface module for configuring and managing security settings, and viewing security status and logs.

[0013] As a further optimized solution of the present invention, the encryption module has multiple encryption units, and before data transmission, different encryption methods are selected according to the importance and sensitivity of the data, specifically including a symmetric encryption unit: using the AES algorithm to ensure the efficiency and security of the data transmission process;

[0014] Asymmetric encryption unit: using the RSA algorithm to enhance data confidentiality and integrity through the pairing of public and private keys;

[0015] Hash function unit: Use the SHA-256 algorithm to generate data digests for verifying data integrity;

[0016] Message Authentication Code Unit: Generates and verifies MAC to ensure message integrity and authenticity of source.

[0017] As a further optimized solution of the present invention, the identity authentication module includes

[0018] Two-factor authentication unit: combines user password and dynamic token for identity authentication;

[0019] Digital certificate unit: Use SSL / TLS certificates to ensure the authenticity of the identities of both parties in communication and the confidentiality of data;

[0020] One-time password unit: Generate a one-time password via SMS, email or dedicated app for temporary authentication.

[0021] As a further optimized solution of the present invention, the intrusion detection module includes

[0022] Signature-based detection unit: matches and detects network traffic based on a known attack pattern library;

[0023] Anomaly detection unit: Based on behavioral analysis technology, it identifies abnormal behaviors that do not conform to normal patterns;

[0024] The specific steps are as follows:

[0025] Monitor network traffic and user behavior in real time and collect relevant data;

[0026] Use signature-based detection units to match known attacks;

[0027] Use the anomaly detection unit to perform behavioral analysis on unknown attacks and identify potential threats.

[0028] As a further optimized solution of the present invention, the machine learning module includes

[0029] Data collection unit: collects security-related data on network traffic, user behavior, and system logs;

[0030] Feature extraction unit: extracts features from the collected data that help distinguish normal from abnormal;

[0031] Model training unit: Use supervised learning, unsupervised learning or reinforcement learning algorithms to train security detection models;

[0032] Real-time analysis unit: Inputs newly collected data into the trained model for real-time analysis and threat detection.

[0033] As a further optimized solution of the present invention, the security protocol module includes

[0034] Session establishment unit: establishes a secure session channel according to the SSL / TLS security protocol;

[0035] Data transmission unit: performs encrypted data transmission on the established session channel;

[0036] Session Termination Unit: Safely ends the session and cleans up session resources.

[0037] As a further optimized solution of the present invention, the user interface module includes

[0038] Configuration management unit: allows users to configure security settings, such as encryption algorithm selection, key management, authentication method, etc.;

[0039] Log viewing unit: provides log recording and viewing functions to help users monitor security status;

[0040] Alert Notification Unit: Sends alert notifications to users when potential threats are detected.

[0041] A method for using a secure communication device based on artificial intelligence, the method steps are as follows:

[0042] S1 initialization: When the device starts, the encryption module and the identity authentication module are loaded to establish a basic security environment;

[0043] S2 connection establishment: The communicating parties perform identity authentication through the identity authentication module to establish a trust relationship;

[0044] S3 session encryption: Use the encryption module to establish an encrypted channel, through which all data transmission passes;

[0045] S4 Continuous Monitoring: The intrusion detection module and machine learning module continuously monitor network traffic and user behavior to identify abnormal behavior;

[0046] S5 response mechanism: When abnormal behavior is detected, the system takes corresponding measures to deal with the abnormal behavior, such as blocking the connection, sending an alarm, etc.

[0047] S6 Session Management: Regularly check the integrity and security of the session, and terminate and re-establish the session when necessary;

[0048] S7 user interaction: Allows users to view security status, adjust security settings, and handle security events through the user interface.

[0049] The present invention has the following advantages:

[0050] Through the combination of the encryption module and the identity authentication module, a trust relationship and a data encryption transmission channel can be established between the communicating parties, which can improve data security and integrity. The intrusion detection module can monitor and identify potential malicious activities in real time, which can effectively prevent hacker attacks and data leaks. At the same time, through continuous learning and optimization of the machine learning module, the system can adapt to new threat patterns and provide flexible security configuration and management to improve overall security performance. It has broad market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a system block diagram of the present invention;

[0052] Figure 2 It is a schematic diagram of the workflow of the present invention. DETAILED DESCRIPTION

[0053] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, a secure communication device based on artificial intelligence includes a processor, wherein the processor is connected to:

[0055] The encryption module is used to establish an encrypted channel and encrypt data transmission in the session channel to ensure the confidentiality and integrity of the data;

[0056] The identity verification module is used for identity verification of both communicating parties to establish a trust relationship;

[0057] Intrusion detection module to monitor and identify potentially malicious activities and behaviors that do not conform to normal patterns;

[0058] The machine learning module is responsible for establishing a behavior model through algorithms, learning and adapting to new attack patterns through the model, and intelligently identifying whether there is abnormal behavior;

[0059] The security protocol module is responsible for defining the specifications and processes of data transmission to ensure the security of data transmission during the communication process;

[0060] A user interface module for configuring and managing security settings, and viewing security status and logs;

[0061] Through the combination of the encryption module and the identity authentication module, a trust relationship and a data encryption transmission channel can be established between the communicating parties, which can improve data security and integrity. The intrusion detection module can monitor and identify potential malicious activities in real time, which can effectively prevent hacker attacks and data leaks. At the same time, through continuous learning and optimization of the machine learning module, the system can adapt to new threat patterns and provide flexible security configuration and management to improve overall security performance. It has broad market prospects.

[0062] Specifically, the encryption module has multiple encryption units. Before data transmission, different encryption methods are selected according to the importance and sensitivity of the data, including

[0063] Symmetric encryption unit: uses AES algorithm to ensure high efficiency and security during data transmission;

[0064] Asymmetric encryption unit: using the RSA algorithm to enhance data confidentiality and integrity through the pairing of public and private keys;

[0065] Hash function unit: Use the SHA-256 algorithm to generate data digests for verifying data integrity;

[0066] Message Authentication Code Unit: Generates and verifies MAC to ensure message integrity and source authenticity;

[0067] Before data transmission, the data is encrypted using a symmetric or asymmetric encryption algorithm, a data summary is generated through a hash function and attached to the encrypted data, and the receiving end uses the same algorithm and key to decrypt and verify the integrity of the data.

[0068] Specifically, the identity verification module includes

[0069] Two-factor authentication unit: combines user password and dynamic token (such as SMS verification code, hardware token, etc.) for identity authentication;

[0070] Digital certificate unit: Use SSL / TLS certificates to ensure the authenticity of the identities of both parties in communication and the confidentiality of data;

[0071] One-time password unit: Generate a one-time password via SMS, email or dedicated app for temporary identity verification;

[0072] Before transmitting data, the communicating parties must first authenticate their identities and enter corresponding verification information (such as passwords, tokens, digital certificates, etc.) based on the selected authentication method. The system verifies the correctness of the input information and a trust relationship is established.

[0073] Specifically, the intrusion detection module includes

[0074] Signature-based detection unit: matches network traffic according to a known attack pattern library, which contains various known attack features and signatures. When network traffic or user behavior matches these features, the system will issue an alarm. For example, for SQL injection attacks, the signature-based detection unit can identify network requests containing specific SQL statement patterns and immediately trigger an alarm.

[0075] Anomaly detection unit: Based on behavioral analysis technology, it identifies abnormal behaviors that do not conform to normal patterns. It uses machine learning algorithms to learn normal behavior patterns from historical data and analyze current network traffic and user behavior in real time to detect anomalies. For example, if a user makes a large number of abnormal login attempts in a short period of time, or a certain IP address suddenly generates a large number of unusual data transmission requests, the anomaly detection unit will mark these behaviors as abnormal and trigger an alarm;

[0076] In the anomaly detection unit, the machine learning algorithm needs to use historical data to learn normal behavior patterns. Assume that the historical data set is D, which contains N normal behavior samples. The goal of the algorithm is to learn a model f so that for any new behavior sample x, f(x) can output a score indicating whether the behavior is normal or not. This score is calculated based on the similarity or distance between the behavior feature and the normal pattern. For example, the measurement method such as Euclidean distance or cosine similarity can be used;

[0077] In the actual detection process, for a new behavior sample x, the algorithm will calculate the score of f(x) and compare it with the preset threshold T. If the score of f(x) is greater than T, the behavior is considered abnormal; otherwise, the behavior is considered normal. The setting of threshold T needs to be adjusted according to the actual situation to ensure the accuracy and robustness of the system.

[0078] Assume that a company's secure communication device is running, and the intrusion detection module is monitoring network traffic in real time. At a certain moment, a malicious user from an external IP address attempts to obtain sensitive information in the company's database through SQL injection attacks. The signature-based detection unit identifies the characteristics of the attack and immediately triggers an alarm. At the same time, the anomaly detection unit also notices the user's abnormal behavior pattern, such as multiple attempts to log in to the database in a short period of time and sending a large number of network requests containing SQL statements. Combined with this information, the system can determine that the user is conducting an SQL injection attack and take corresponding defensive measures, such as blocking the user's connection and recording the attack log.

[0079] The intrusion detection steps are as follows:

[0080] 1. Data collection: Collect historical data such as network traffic and user behavior for training machine learning models;

[0081] 2. Feature extraction: Extract features from the data that help distinguish normal from abnormal, such as the number of login attempts, data transfer volume, request type, etc.

[0082] 3. Model training: Use historical data and extracted features to train machine learning models to learn normal behavior patterns;

[0083] 4. Real-time detection: New network traffic and user behavior are input into the model for real-time analysis, and the behavior score is calculated and compared with the threshold;

[0084] 5. Alert and response: If abnormal behavior is detected, the system will immediately trigger an alert and take corresponding defensive measures, such as blocking the connection and recording logs.

[0085] Specifically, the machine learning module includes

[0086] Data collection unit: collects security-related data on network traffic, user behavior, and system logs;

[0087] Feature extraction unit: extracts features from the collected data that help distinguish normal from abnormal;

[0088] Model training unit: Use supervised learning, unsupervised learning or reinforcement learning algorithms to train security detection models;

[0089] Real-time analysis unit: Inputs newly collected data into the trained model for real-time analysis and threat detection.

[0090] The specific steps are as follows:

[0091] 1. Regularly collect and pre-process security-related data

[0092] a. First, identify the data source, which may include network communication logs, firewall logs, intrusion detection system (IDS) alerts, user behavior logs, etc.

[0093] b. Use API, database query, log file parsing and other methods to crawl data from data sources.

[0094] c. Integrate data from different data sources to form a unified data set;

[0095] d. Remove duplicate data, invalid data, and data with too many missing values;

[0096] e. Convert data in different formats into a unified format, such as converting timestamps into readable time formats;

[0097] f. Standardize or normalize the numerical features so that they are on the same scale;

[0098] g. Encode categorical features, such as using One-Hot Encoding or Label Encoding;

[0099] 2. Extract data features and construct feature vectors

[0100] a. Calculate the statistics of communication data, such as mean, standard deviation, maximum, minimum, etc.;

[0101] b. Analyze the time series characteristics of communication data, such as communication frequency, communication duration distribution, etc.;

[0102] c. If the communication data contains text information (such as email content, chat messages), use text mining technology to extract features, such as word frequency, TF-IDF, word embedding, etc.;

[0103] d. Use correlation coefficients (such as Pearson correlation coefficient and Spearman rank correlation coefficient) to analyze the correlation between features and target variables;

[0104] e. Use model-based feature importance assessment methods, such as random forest feature importance scores, Lasso regression coefficients, etc.

[0105] f. Apply feature selection algorithms (such as recursive feature elimination, model-based feature selection, etc.) to screen the optimal feature combination;

[0106] 3. Use machine learning algorithms to train security detection models

[0107] a. Choose appropriate machine learning models, such as support vector machines (SVM), decision trees, random forests, neural networks, and deep learning models, based on the nature of the problem and the characteristics of the data;

[0108] b. Choose a model with moderate complexity to avoid overfitting or underfitting;

[0109] c. Choose a suitable training algorithm, such as gradient descent, stochastic gradient descent, or Adam optimizer;

[0110] d. Use cross-validation, grid search, random search and other methods to optimize the model's hyperparameters, such as learning rate, regularization parameter, number and depth of trees, etc.;

[0111] e. During the training process, use the validation set to monitor the performance of the model. If the performance on the validation set no longer improves, stop training to avoid overfitting.

[0112] 4. Input new data into the model for real-time analysis to identify potential threats

[0113] a. Choose appropriate evaluation indicators to evaluate the performance of the model, such as accuracy, recall, F1 score, AUC-ROC curve, etc.;

[0114] b. Use the confusion matrix to intuitively display the classification effect of the model, including the number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN);

[0115] c. Combine multiple features into new features to improve the performance of the model;

[0116] d. Use ensemble learning methods (such as bagging, boosting, stacking) to combine the prediction results of multiple models to improve the stability and accuracy of the model;

[0117] e. Based on the evaluation results, continuously adjust the parameters and features of the model to optimize the performance of the model;

[0118] f. Deploy the trained model to a secure communication device to ensure that the model can run stably in a real-time environment;

[0119] g. Monitor the performance of the model in real time, including indicators such as response time and accuracy;

[0120] h. Establish a model update mechanism to regularly collect new data and retrain the model to adapt to the ever-changing communication environment and security threats;

[0121] i. Use the deployed model to monitor and classify communication data in real time and detect abnormal communication behavior in a timely manner;

[0122] j: When abnormal communication is detected, an alarm is issued in time and corresponding security measures are taken, such as blocking communication, recording logs, notifying administrators, etc.;

[0123] k. Record all alerts and response actions for subsequent analysis and auditing.

[0124] Specifically, the security protocol module includes

[0125] Session establishment unit: establishes a secure session channel according to the SSL / TLS security protocol;

[0126] Data transmission unit: performs encrypted data transmission on the established session channel;

[0127] Session termination unit: safely ends the session and cleans up session resources;

[0128] The specific steps are as follows:

[0129] The communicating parties establish a secure session channel through a security protocol stack;

[0130] Encrypted data transmission is performed on the established session channel;

[0131] After the session ends, session resources are safely cleaned up by the session termination unit.

[0132] Specifically, the user interface module includes

[0133] Configuration management unit: allows users to configure security settings, such as encryption algorithm selection, key management, authentication method, etc.;

[0134] Log viewing unit: provides log recording and viewing functions to help users monitor security status;

[0135] Alert Notification Unit: Sends alert notifications to users when potential threats are detected.

[0136] The specific steps are as follows:

[0137] Users configure security settings through the user interface;

[0138] The system records and stores security logs for users to review;

[0139] When a potential threat is detected, the system sends an alert notification to the user through the user interface.

[0140] like Figure 2 As shown, a method for using a secure communication device based on artificial intelligence, the method steps are as follows:

[0141] S1 initialization: When the device starts, the encryption module and the identity authentication module are loaded to establish a basic security environment;

[0142] S2 connection establishment: The communicating parties perform identity authentication through the identity authentication module to establish a trust relationship;

[0143] S3 session encryption: Use the encryption module to establish an encrypted channel, through which all data transmission passes;

[0144] S4 Continuous Monitoring: The intrusion detection module and machine learning module continuously monitor network traffic and user behavior to identify abnormal behavior;

[0145] S5 response mechanism: When abnormal behavior is detected, the system takes corresponding measures to deal with the abnormal behavior, such as blocking the connection, sending an alarm, etc.

[0146] S6 Session Management: Regularly check the integrity and security of the session, and terminate and re-establish the session when necessary;

[0147] S7 user interaction: Allows users to view security status, adjust security settings, and handle security events through the user interface.

Claims

1. A secure communication device based on artificial intelligence, comprising a processor, characterized in that: The processor is connected with: The encryption module is used to establish an encrypted channel and encrypt data transmission in the session channel to ensure the confidentiality and integrity of the data; The identity verification module is used for identity verification of both communicating parties to establish a trust relationship; An intrusion detection module is used to monitor and identify potential malicious activities and behaviors that are inconsistent with normal patterns. The intrusion detection module includes Signature-based detection unit: matches and detects network traffic based on a known attack pattern library; Anomaly detection unit: Based on behavioral analysis technology, it identifies abnormal behaviors that do not conform to normal patterns; The machine learning module is responsible for establishing a behavior model through algorithms, learning and adapting to new attack patterns through the model, and intelligently identifying whether there is abnormal behavior; The security protocol module is responsible for defining the specifications and processes of data transmission to ensure the security of data transmission during the communication process; User interface module for configuring and managing security settings, and viewing security status and logs.

2. The artificial intelligence-based secure communication device according to claim 1, characterized in that: The encryption module has multiple encryption units. Before data transmission, different encryption methods are selected according to the importance and sensitivity of the data. Specifically, Symmetric encryption unit: uses AES algorithm to ensure high efficiency and security during data transmission; Asymmetric encryption unit: using the RSA algorithm to enhance data confidentiality and integrity through the pairing of public and private keys; Hash function unit: Use the SHA-256 algorithm to generate data digests for verifying data integrity; Message Authentication Code Unit: Generates and verifies MAC to ensure message integrity and authenticity of source.

3. The artificial intelligence-based secure communication device according to claim 1, characterized in that: The identity verification module includes Two-factor authentication unit: combines user password and dynamic token for identity authentication; Digital certificate unit: Use SSL / TLS certificates to ensure the authenticity of the identities of both parties in communication and the confidentiality of data; One-time password unit: Generate a one-time password via SMS, email or dedicated app for temporary authentication.

4. The artificial intelligence-based secure communication device according to claim 1, characterized in that: The machine learning module includes Data collection unit: collects security-related data on network traffic, user behavior, and system logs; Feature extraction unit: extracts features from the collected data that help distinguish normal from abnormal; Model training unit: Use supervised learning, unsupervised learning or reinforcement learning algorithms to train security detection models; Real-time analysis unit: Inputs newly collected data into the trained model for real-time analysis and threat detection.

5. The artificial intelligence-based secure communication device according to claim 1, characterized in that: The security protocol module includes Session establishment unit: establishes a secure session channel according to the SSL / TLS security protocol; Data transmission unit: performs encrypted data transmission on the established session channel; Session Termination Unit: Safely ends the session and cleans up session resources.

6. The artificial intelligence-based secure communication device according to claim 1, characterized in that: The user interface module includes Configuration management unit: allows users to configure security settings, such as encryption algorithm selection, key management, authentication method, etc.; Log viewing unit: provides log recording and viewing functions to help users monitor security status; Alert Notification Unit: Sends alert notifications to users when potential threats are detected.

7. A method for using an artificial intelligence-based secure communication device according to any one of claims 1 to 6, characterized in that: The steps are as follows: S1 initialization: When the device starts, the encryption module and the identity authentication module are loaded to establish a basic security environment; S2 connection establishment: The communicating parties perform identity authentication through the identity authentication module to establish a trust relationship; S3 session encryption: Use the encryption module to establish an encrypted channel, through which all data transmission passes; S4 Continuous Monitoring: The intrusion detection module and machine learning module continuously monitor network traffic and user behavior to identify abnormal behavior; S5 response mechanism: When abnormal behavior is detected, the system takes corresponding measures to deal with the abnormal behavior, such as blocking the connection, sending an alarm, etc. S6 Session Management: Regularly check the integrity and security of the session, and terminate and re-establish the session when necessary; S7 user interaction: Allows users to view security status, adjust security settings, and handle security events through the user interface.

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