US20220343067A1 - Text Analysis System, and Characteristic Evaluation System for Message Exchange Using the Same - Google Patents

Text Analysis System, and Characteristic Evaluation System for Message Exchange Using the Same Download PDF

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US20220343067A1
US20220343067A1 US17/639,866 US201917639866A US2022343067A1 US 20220343067 A1 US20220343067 A1 US 20220343067A1 US 201917639866 A US201917639866 A US 201917639866A US 2022343067 A1 US2022343067 A1 US 2022343067A1
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feature
text
time series
series signal
text data
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Hibiki Oka
Mitsuo Kojima
Akira Nakahashi
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Imatrix Holdings Corp
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Imatrix Holdings Corp
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Assigned to IMATRIX HOLDINGS CORP. reassignment IMATRIX HOLDINGS CORP. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KOJIMA, MITSUO, NAKAHASHI, Akira, OKA, HIBIKI
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/12—Use of codes for handling textual entities
    • G06F40/126—Character encoding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3344—Query execution using natural language analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/194—Calculation of difference between files
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/216—Parsing using statistical methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/226—Validation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems

Definitions

  • the present invention relates to a text analysis system and a feature evaluation system in message exchange using the same.
  • JP6267830B2 discloses a technique that sample data in which a character strings or the like are described is signalized as n-valued sample data (n is a natural number of 2 or more), similarity between the n-valued sample data and n-valued input data is calculated, and the input data is identified whether or not spam mail based on the calculated similarity.
  • the purpose of processing a message written in natural language is not only to understand the content but also to acquire the characteristics or features of the message creator.
  • the characteristics or features of message creators are also utilized in the field of information security.
  • Information leakage due to obstruction of operation of computer devices and electronic devices using messages, information fraud, fraudulent acts against users, etc. is a big problem, and there is a high demand for information leakage prevention by message analysis. In addition to this, high speed processing is also required.
  • One is a deliberate outflow by a malicious user. For example, a collaborator for fraud sends information to an external using a message tool or the like, or infects a computer with a malicious program such as malware to leak the information to an external computer.
  • the other is erroneous transmission by the user.
  • the user sends a message to an unknown destination, uses a topic or term that he does not normally use, or attaches a file that he does not normally attach.
  • a common feature of these is that these behaviors by the user are not usual. Therefore, it is possible to prevent information leakage due to message exchange by detecting the peculiarity existing in the message at high speed and by paying attention before transmission.
  • the present invention intends to provide a text analysis system that is low cost and fast compared to the conventional technique and is able to detect text with a specific expressive feature and structural feature. Further, the present invention intends to provide a feature evaluation system for detecting an anomaly in a text body in a message exchange.
  • the present invention achieves a system capable of processing a wide variety of languages with a single algorithm.
  • the present invention for text analysis system can be applied to the detection of features and exceptions of spoken language and sentences.
  • the present invention can discover differences in meaning, misunderstandings, injustices and their signs caused by wording errors and irregularities and can detect extraordinary ideas buried in mediocre ideas and a small number of intentions among great numbers.
  • the text analysis system of the present invention can be used in a wide variety of ways.
  • a text analysis system for analyzing text includes a conversion means for converting characters of the acquired text data into a numerical form to convert the text data into a time series signal(s); a feature extraction means for extracting feature information from the converted time series signal to store the extracted feature information; and a determination means for determining an identity of text data newly acquired by using the feature information.
  • the text analysis system further includes a detection means for detecting anomalous text different from the feature information, based on a determination result by the determination means.
  • the conversion means converts characters into numerical data based on a predetermined conversion table.
  • the conversion means normalizes the time series signal to converge them into the range from a minimal value “0” to a maximum value “1.”
  • the conversion means attenuates a value(s) of the time series signal that is more than a set threshold to normalize the attenuated time series signal.
  • the feature extraction means extracts a feature(s) from the normalized time series signal of text data composed with a normal expressive feature and/or structural feature, and learns the feature to acquire output waveform that reproduces input waveform of the time series signal by using the extracted feature.
  • the feature extraction means encodes the feature information by an auto-encoder.
  • the feature extraction means learns the feature information by a neural network.
  • a feature evaluation system for message exchange includes the above-described text analysis system, and the detection means detects an anomaly in an outgoing e-mail based on the determination result by the determination means.
  • the feature evaluation system for message exchange further includes a transmission control means for halting the transmission of an outgoing mail when an anomaly is detected in the outgoing mail
  • the feature evaluation system for message exchange further includes a notification means for notifying the halt of the transmission of the outgoing email when the transmission of the outgoing emails is halted by the transmission control means.
  • a text analysis program executed by a computer terminal includes the steps of: acquiring text data; converting characters of the acquired text data into a numerical form to convert the text data into a time series signal; extracting feature information from the converted time series signal to store the extracted feature information; and; determining an identity of text data newly acquired by using the feature information.
  • the step of determining an identity includes identifying an outgoing e-mail composed with an expressive feature or structural feature different from the feature information.
  • a text analysis method in a computer terminal includes the steps of: acquiring text data; converting characters of the acquired text data into a numerical form to convert the text data into a time series signal; extracting feature information from the converted time series signal to store the extracted feature information; and determining an identity of text data newly acquired by using the feature information.
  • the step of determining an identity includes identifying an outgoing e-mail composed with an expressive feature and/or structural feature different from the feature information.
  • the text data is converted into a time-series signal, it is possible to reduce the cost without requiring morphological analysis of the texts or sentences and dictionary data for that purpose. Furthermore, by determining the identity of the text or sentence data based on the feature information extracted from the time-series signal, it is possible to easily determine whether or not the sentence is sentence of the person himself/herself. Furthermore, according to the present invention, by detecting the peculiarity of the sent mail, it is possible to prevent the information leakage by stopping the transmission of the abnormal sent mail.
  • FIG. 1 is a block diagram illustrating a structure of a text analysis system according to a first embodiment of the present invention.
  • FIG. 2 is a block diagram illustrating an internal structure of a feature extraction portion shown in FIG. 1 .
  • FIG. 3 is an example of a part of Unicode.
  • FIG. 4 is an example illustrating that an electronic mail is acquired as text data and a time series signals of the electronic mail are normalized.
  • FIG. 5 is a flow chart illustrating an example of an operation of a signal normalization according to an embodiment of the present invention.
  • FIG. 6 is a diagram illustrating a feature extraction from an input by a signal classification portion according to an embodiment of the present invention.
  • FIG. 7 is a diagram illustrating an auto-encoder according to an embodiment of the present invention.
  • FIG. 8 is a diagram illustrating an example of a classification by a threshold of the signal classification portion.
  • FIG. 9 is a block diagram illustrating a structure of an outgoing e-mail monitoring system according to a second embodiment of the present invention.
  • FIG. 10 is a flow chart illustrating an operation of the outgoing email monitoring system according to a second embodiment of the present invention.
  • FIG. 11 is a graph showing one experimental result according to an embodiment of the present invention.
  • FIG. 12 is a graph showing another experimental result according to an embodiment of the present invention.
  • a text analysis system may be applied to any electronic devices having functions to electronically process text (such as computer device, mail server, client terminal, and smart phone).
  • FIG. 1 is a diagram illustrating an example of a structure of a text analysis system according to an embodiment herein.
  • a text analysis system 100 includes a text acquisition portion no for acquiring text data, a feature extraction portion 120 for extracting feature(s) of the text data acquired in the text acquisition portion no, a feature storage portion 130 for storing the feature extracted by the feature extraction portion 120 , and an anomalous text detection portion 140 for detecting anomalous text based on the feature in the feature extraction portion 120 or the feature storage portion 130 .
  • the text analysis system 100 is implemented by software such as mail server and client terminal etc., hardware, or the combination of software and hardware.
  • the text acquisition portion no acquires text data (for example, electronic mail etc.) composed by a user.
  • text data for example, electronic mail etc.
  • text data is an electronic mail
  • an electronic mail in HTML form composed by a mail soft loaded in a client terminal
  • an electronic mail sent from a client terminal to a mail server through internet or an electronic mail in a message exchange system is acquired.
  • the text acquisition portion no may acquire text data composed by multiple users.
  • text data acquired by the text acquisition portion no is normal text data that is composed in user's normal behaviors, i.e., composed with a normal expressive feature or structural feature.
  • the feature extraction portion 120 extracts a feature included in normal text data composed with the normal expressive feature or structural feature of users and learns the feature of user's text.
  • the text acquisition portion no acquires optional text data and the text analysis system 100 identifies whether a feature of the optional text data corresponds to the feature of text composed with the normal expressive feature or structural feature. For example, for a text composed by one user, it is identified that whether the text is composed with the normal expressive feature or structural feature or whether the text is composed by another user.
  • FIG. 2 shows an internal structure of the feature extraction portion 120 .
  • the feature extraction portion 120 includes a character signalizing portion 122 for receiving text data acquired in the text acquisition portion no to convert characters described in a text to time series signals, a normalization portion 124 for normalizing the time series signals that are converted into signals by the character signalizing portion 122 , and a signal classification portion 126 for classifying the normalized signals.
  • the character signalizing portion 122 converts a series of characters described in a text into one-dimensional time series signals. In one preferred example, the character signalizing portion 122 converts each of characters in the text into a numerical data based on Unicode.
  • Unicode is one of the international standards for character code, where codes are assigned to characters, numbers, or symbols of various languages in the world.
  • FIG. 3 shows an example of a part of Unicode. In Unicode, ASCII, Chinese character, Arabic, and Greek symbols etc. are coded to binary data in 16 bit or more.
  • the character signalizing portion 122 may have the amount of data in which the number of bits per one numerical value converted from one character multiplied by the number of characters. Also, the character signalizing portion 122 may convert fixed-length data to one continuous sequence data or to varying-length data.
  • a conversion table may be previously prepared in which the relationship between character, idiom, and phrase etc. and numerical data is uniquely defined.
  • the character signalizing portion 122 may convert each character or idiom etc. in a text to numerical data by using such conversion table.
  • the character signalizing portion 122 converts characters from the first to the last in a text to numerical data. For example, if the text has the size of P row(s) ⁇ Q column(s) (P and Q are any integer number), time series signals including binary value data corresponding to the number of characters in P ⁇ Q may be generated.
  • character is a concept including characters in natural language, numbers, symbols, figures, and blank (space) without any characters.
  • characters may be sequentially scanned from the first line to the last line, from left to right or from right to left.
  • characters may be sequentially scanned from the first line to the last line, from the top to the bottom or from the bottom to the top.
  • characters from the first to the last may be converted to numerical data.
  • the scanning direction may be optionally determined. If page information configuring text data (the number of lines, the number of characters in one line) is required, the page information may be acquired at the same time. Thus, characters from the first to the last may be identified in reference to the page information.
  • the time series signals generated by the character signalizing portion 122 may be regarded as a non periodic waveform composed by characters in the text.
  • Words or idioms included in the text are expressed as a waveform pattern. For example, when a user uses a word or idiom “XX” frequently, a waveform pattern corresponding to “XX” may be included in the time series signals.
  • a waveform pattern expressing them may be included. Such waveform patter is one feature for identifying user.
  • the character signalizing portion 122 converts characters into signals based on Unicode or the conversion table. Thus, it may be applied to multiple languages without depending on a certain language. Language differences may be expressed as the difference of waveforms of time series signals. Further, the character signalizing portion 122 does not perform morphological analysis and/or syntax analysis, so that dictionaries such as corpus etc. are not required, which reduces cost.
  • the signal normalization portion 124 normalizes a time series signal generated by the character signalizing portion 122 .
  • each numerical value for generating a time series signal is expressed in a discrete value whose range may be extremely large.
  • the signal normalization portion 124 performs a process for suppressing outliers of the time series signals and a process for normalizing the range.
  • a numerical value that is more than a preset threshold value is attenuated.
  • the process is performed by the following equation, where “avg” is an average, “std” is a standard deviation, “x” is a target value (in this case, a numerical value of a time series signal), “rate” is an attenuation rate, and “d” is a coefficient that is multiplied by a numerical value to be added for raising the overall value.
  • the threshold value (threshold) is set inside by an infinitesimal d from a point away from the average by G, as described above (
  • the process of normalization of the range is performed.
  • the standard deviation (std) is normalized to 1 and the average (avg) is normalized to 0, after that, minimum value is normalized to 0 and maximum value is normalized to 1 again, so that the time series signals are converged into the range of 0-1.
  • FIG. 4 shows an example of a normalization, where characters of the body of an electronic mail are converted to time series signals when the electronic mail is received as text data, and the time series signals are normalized to be converged to the range of 0-1.
  • FIG. 5 shows a flow chart for an example of an operation of the signal normalization portion 124 according to an embodiment herein.
  • each character in an acquired text is converted into a numerical form by the character signalizing portion 122 based on UNICODE at step S 100 .
  • the numerical value of the time series signals are multiplied by an integer by the signal normalization portion 124 to extend a waveform at step S 102 .
  • the numeral value may be adjacent due to languages, this process is performed to correct it.
  • the process for suppressing outliers is performed by the signal normalization portion 124 as shown above at step S 104 . In the process for suppressing outliers, numerical values more than the threshold value are attenuated.
  • the attenuation may be performed multiple times, at step 106 .
  • the number of times of attenuation may be adjusted according to data.
  • the variance and the average are normalized by the signal normalization portion 124 , after that, the minimum values is normalized to “0” and the maximum value is normalized to “1.” Unless the value of the variance is below a certain threshold value, the processes of steps S 104 -S 108 are repeated. An upper limit may be set to the number of times of the repeated process.
  • the signal classification portion 126 receives a normalized time series signal from the signal normalization portion 124 to extract a feature included in the time series signal.
  • the extracted feature is the one from which the input can be reproduced.
  • the signal classification portion 126 learns the feature.
  • the signal classification portion 126 learns text data only that is composed with a normal expressive feature or structural feature. For example, a feature is extracted from the normalized input form as shown in FIG. 6 . To acquire output waveforms that can reproduce almost input waveforms by using the extracted feature, the feature is learned.
  • the signal classification portion 216 reduces dimensionality(s) of the feature by an auto-encoder using neural network and suppresses the amount of information.
  • FIG. 7 shows a concept of the auto-encoder using neural network.
  • the auto-encoder is configured with fully connected layers only and includes four encoder layers and four decoder layers. The width of each layer of neural network is variable according to the length of a signal converted from the character string.
  • the encoder reduces unrequired dimensionality(s) of input to compress the feature.
  • the decoder reproduces the input from the compressed feature.
  • Neural network adjusts the respective weights of the encoder and the decoder by using the learning function. In this example, neural network reproduces the input with symmetrical configuration. The input has a fixed-length.
  • the signal classification portion 126 also includes a function to inspect the reproducibility of the output waveform. Specifically, the distances between each point in two time series of the input waveform and the output waveform as shown in FIG. 6 are compared in a round-robin manner to detect a path with the shortest distance of two time series.
  • the path is regarded as DTW (Dynamic Time Warping) distance. While the reproduced waveform has some deviations, the inspection is tough to phase shift etc.
  • the DTW distance is used to measure the reproducibility of new data after learning model is defined.
  • new data is new text data that is determined whether or not it is unique by the text analysis system 100 . New text data is processed by the auto-encoder.
  • the reproducibility is low and the text data is determined as unique data (that has no normal expressive feature and/or normal structural feature).
  • the determination result is provided to the anomalous text detection portion 140 .
  • the signal classification portion 126 calculates a threshold value for classifying waveforms.
  • evaluation data i.e., a feature that is extracted from a text (sentence) written by a normal expressive feature and/or structural feature and is compressed by the auto-encoder (which is expressed as the weight of the auto-encoder, for example, as a coefficients of equation which each neuron has) is evaluated to calculate identity.
  • the median value and the standard deviation of the identity are obtained and a threshold value is calculated by the following equation.
  • the threshold value means that almost 95% waveforms are included within the range from the median value to the standard deviation*2, if the waveforms show generally a normal distribution.
  • threshold value median value ⁇ standard deviation ⁇ 2 Equation 2
  • FIG. 8 shows an example of a classification according to a threshold value.
  • dashed lines are one user's text that has already learned, and solid lines are another user's text.
  • the threshold value of the feature is 50.8. A text that has a feature more than this value is detected as another user's text.
  • the feature storage portion 130 stores a feature extracted by the feature extraction portion 120 and its threshold value. Each time text data is learned, the feature and the threshold value are updated.
  • the anomalous text detection portion 140 detects anomalous text by using the result of the pre-learning. That is, an arbitrary text A is obtained by the text acquisition portion no, then the feature of the text A is extracted by the feature extraction portion 120 .
  • the signal classification portion 126 compares the feature extracted from the text A with a threshold value stored in the feature storage portion 130 . When the feature is more than the threshold value, the text A is determined as anomalous text. The result of the determination is provided to the anomalous text detection portion 140 .
  • the anomalous text detection portion 140 detects that the text A determined as anomalous text is not composed with a normal expressive feature and/or structural feature. For example, the text A is estimated as a text that is composed by another user other than one user or a text that is composed by the one user himself with a specific expressive feature and/or structural feature.
  • FIG. 9 shows an application example of a text analysis system according to an embodiment herein to an outgoing email monitoring system.
  • An outgoing email monitoring system 200 may be achieved for example in mail server, client terminal (computer device, mobile device, etc.) with mail sending/receiving function.
  • the outgoing email monitoring system 200 includes an outgoing email acquisition portion 210 for acquiring an outgoing mail composed by a user; a feature extraction portion 220 for extracting a feature of the outgoing mail that is acquired by the outgoing email acquisition portion 210 ; a feature storage portion 230 for storing the extracted feature; an anomalous email detection portion 240 for detecting whether or not the acquired outgoing mail has anomalous; and a transmission control portion 250 for controlling the transmission of the outgoing mail based on the detection result of the anomalous email detection portion 240 .
  • These functions may be performed by software in mail server or client terminal, hardware, or the combination of software and hardware.
  • the outgoing email acquisition portion 210 acquires an electronic mail in HTML form composed by mail soft that is mounted in a client terminal or a acquires an electronic mail for sending uploaded from a client terminal to mail server.
  • the feature extraction portion 220 operates similar to the feature extraction portion 120 of the above-described text analysis system.
  • the feature extraction portion 220 shall be preliminary learned a feature of an electronic mail that is composed by user X with a normal expressive feature and/or structural feature. Accordingly, if an outgoing email acquired from the outgoing email acquisition portion 210 is composed by user X, the outgoing mail has the feature same as the learned feature. Thus, the outgoing mail is identified as a mail that is composed by user X with a normal expressive feature and/or structural feature. If an outgoing mail is composed by user X with specific expressive and/or structural features or composed by another user, the outgoing mail does not have the feature same as the learned feature. Thus, the outgoing mail is identified as a mail that is composed by user X with specific expressive and/or structural features or composed by another user. As shown in FIG. 8 , whether or not the electronic mail has identity is determined based on the threshold value.
  • the anomalous email detection portion 240 detects the outgoing mail as anomalous mail and provides the detection result to the transmission control portion 250 .
  • the transmission control portion 250 instructs, for example, a client terminal or mail server to halt or hold the transmission of the outgoing mail and alerts user to non-delivery. For example, non-delivery is displayed on the display of the client terminal or voice guidance may be used.
  • the outgoing mail is sent to the client terminal or mail server.
  • FIG. 10 is a flow chart for explaining an example of an operation of the outgoing email monitoring system.
  • an outgoing mail is acquired by the outgoing email acquisition portion 210 (S 200 ).
  • each character of the body of the outgoing mail is converted into signals by the feature extraction portion 220 to generate a one-dimensional time series signal (S 202 ).
  • the time series signal is normalized (S 206 ).
  • a feature is extracted from the time series signals.
  • whether or not there is any identity between the extracted feature and learned feature is determined (S 208 ).
  • the outgoing mail is determined as the one that is composed with user's usual expressive and/or structural features (S 210 ).
  • the outgoing mail is sent to a sending address (S 212 ).
  • the outgoing mail is determined as the one that is composed by user with specific expressive and/or structural features or composed by another user (S 220 ).
  • Sending the outgoing mail is halted (S 222 ).
  • an outgoing mail is determined if the mail is composed with usual expressive and/or structural features.
  • sending of the outgoing mail is halted.
  • information leak by unsolicited outgoing mail may be prevented.
  • FIG. 11 shows the probability that the email magazines in each languages are identified as the one other than email magazine A.
  • the mail magazines B, C were identified with well probability, while the mail magazine D has some scatterings between languages. This is caused by the difference of feature(s) of each language.
  • the number of characters in Japanese language is 50+50+lowercase characters+Chinese characters
  • English language has 26 characters+their lowercase characters
  • Chinese and Taiwanese languages have 87,000 characters (Unicode11)
  • French language have 26 characters+lowercase characters+7 characters
  • Hindi language hass 156 characters+lowercase characters
  • Korean language have 11,172 characters
  • Finnish language has 29 characters+lowercase characters.
  • the length of one sentence is different and/or the amplitude when converting into signals is different.
  • the accuracy may be finally improved by optimal normalization.
  • FIG. 12 is a graph showing a rate whether or not users B, C are identified as the one other than user A.
  • the rate in which user A were identified as others (mail composed with a specific expressive and/or structural features) is 5.95%.
  • the rate in which users B, C were identified as the one other than user A (mail composed with a specific expressive and/or structural features) is 62.00% and 51.00%, respectively.

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