CN104408341A - Smart phone user identity authentication method based on gyroscope behavior characteristics - Google Patents
Smart phone user identity authentication method based on gyroscope behavior characteristics Download PDFInfo
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- CN104408341A CN104408341A CN201410641806.9A CN201410641806A CN104408341A CN 104408341 A CN104408341 A CN 104408341A CN 201410641806 A CN201410641806 A CN 201410641806A CN 104408341 A CN104408341 A CN 104408341A
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- 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
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/316—User authentication by observing the pattern of computer usage, e.g. typical user behaviour
Abstract
The invention discloses a smart phone user identity authentication method based on gyroscope behavior characteristics. The method comprises the following steps: extracting sensor behavior data corresponding to different operation events according to time information of screen touch operation by analyzing gyroscope behavior data generated during screen touch operation of a smart phone user to generate sensor behavior characteristics; establishing an identity authentication model of the user based on the behavior characteristics; performing identity authentication on the smart phone user. The method disclosed by the invention has the advantages that the method is easy to operate, and any additional equipment does not need to be added; the behavior characteristics of the mobile phone user in an authentication process are described by using gyroscope operation behaviors on the basis of not changing the user habit to lay a basis for user identity judgment, so that smear attack and observation attack can be effectively prevented, and extensive safety and adaptability are achieved.
Description
Technical field
The present invention relates to smart phone user authentication, particularly a kind of smart phone user identity identifying method utilizing mobile phone sensor data.
Background technology
Along with the fast development of mobile Internet, smart mobile phone penetrates into all trades and professions, affects each details of people's live and work.The significant data (as account password) that smart mobile phone record is more and more many with store mobile subscriber and privacy information (as note and video).Particularly a large amount of use of mobile-phone payment related application in recent years and the frequent generation of privacy compromise event, makes the safety protection problem of people to smart mobile phone more and more pay close attention to.
Authentication is safely and effectively the key problem of carrying out smart mobile phone safeguard protection, refers to that user is when accessing intelligent mobile phone system or accessing the system resource of different protection level, the process whether identity of this user of system validation is legal.Authentication mode at present for smart mobile phone mainly contains three kinds: 1) PIN password, 2) graphical passwords, 3) fingerprint.For the mode of PIN password, be easy to realize and simple and convenient, but easily pass into silence or be stolen; For the mode of graphical passwords (user need on the dot matrix of 3x3 according to some points that is necessarily linked in sequence as password), be easy to memory, but touch screen vestige and finger motion are easily observed causes guessing attack; For fingerprint recognition mode, although accuracy is high, need special hardware supported.
Summary of the invention
The action behavior utilizing smart phone user to represent in contact action process can as the good means of one replacing or strengthen PIN password/graphical passwords/finger print identifying as the foundation of authentication, simultaneously because mobile phone sensor (as accelerometer or gyroscope) is as built-in device intrinsic in smart mobile phone, can be directly, exactly the action behavior in user's contact action process is reflected and measured, the action behavior of mobile phone gyroscope behavioral data to user that user therefore can be adopted caused when contact action is measured, and in this, as the foundation of authenticating user identification.Compared with the authentication mode of aforementioned three kinds of current popular, gyroscope behavioural characteristic is used to carry out authentication: can not forget and lose, not easily stolen by others and forgery; Simple to operate, do not need special hardware supported, and cost is very low, easily promotes on a large scale; In addition, effectively can prevent from stain from attacking and observe attacking, there is security widely and applicability.
The object of this invention is to provide a kind of smart phone user identity identifying method based on mobile phone sensor data, particularly utilize user in contact action process by the behavioural characteristic of gyroscope record to judge the method for user identity legitimacy.
For reaching above object, the present invention adopts following technical scheme to realize:
Based on a smart phone user identity identifying method for gyroscope behavioural characteristic, it is characterized in that, by train and certification two parts are formed, wherein:
Described training department divides and comprises the steps:
(1) behavioral data that repeatedly formed in contact action process of gyroscope collection recording user, forms training data;
(2) for the behavioral data that each gyroscope records, the gyroscope behavioral data that contact action is corresponding is extracted;
(3) for the gyroscope behavioral data that contact action is corresponding, extract behavioural characteristic vector, obtain reference feature vector wherein, calculate behavioural characteristic vector and the distance of reference feature vector, obtain distance feature vector, form training characteristics set;
(4) adopt single classifier to set up the Model of Identity Authentication System of user, be that positive class is trained Model of Identity Authentication System by the training characteristics aggregated label of validated user, obtain the judging identity threshold value of validated user;
Described authentication section comprises the steps:
(1) when certification, gyroscope obtains the behavioral data that user's contact action identical with training produces, and extracts the gyroscope behavioral data that contact action is corresponding;
(2) for the gyroscope behavioral data that contact action is corresponding, extract behavioural characteristic vector, the reference feature vector obtained when training with the authentication model described in training part carries out distance metric, obtains distance feature vector;
(3) using vectorial for this distance feature input as Model of Identity Authentication System, obtain the detected value of authentication, and this detected value and judging identity threshold value are compared, and then judge the legitimacy of user identity.
In said method, the gyroscope behavioral data that described extraction contact action is corresponding refers to for each contact action, extract contact action when starting and at the end of time, obtain the time interval of contact action, and this time interval is mated with the behavioral data timestamp of gyroscope record, gyroscope behavioral data corresponding under obtaining each contact action.
The described gyroscope behavioral data corresponding for contact action, the concrete grammar extracting behavioural characteristic vector is: extract for gyroscope behavioral data corresponding to contact action, comprise statistical nature and distance feature, specific as follows:
Statistical nature refers to the statistics description amount of the gyroscope behavioral data that each contact action is corresponding, comprising:
Difference, coefficient of kurtosis, the coefficient of skewness of the maximal value of X-axis angular velocity, minimum value, average, intermediate value, standard deviation, 75% fractile and 25% fractile;
Difference, coefficient of kurtosis, the coefficient of skewness of the maximal value of Y-axis angular velocity, minimum value, average, intermediate value, standard deviation, 75% fractile and 25% fractile;
Difference, coefficient of kurtosis, the coefficient of skewness of the maximal value of Z axis angular velocity, minimum value, average, intermediate value, standard deviation, 75% fractile and 25% fractile;
Distance feature refers to the distance metric on identical contact action between gyroscope behavioral data and reference behavioral data, and its concrete calculation procedure is:
1) in identical contact action corresponding gyroscope Behavioral training data, comprise and organize sample data more, adopt dynamic time warping to calculate often to organize sample data and other organizes distance between sample data, obtain distance sum after cumulative, the minimum gyroscope sample data of chosen distance sum is as with reference to behavioral data;
2) adopt the distance of dynamic time warping calculating on each identical contact action between gyroscope behavioral data and reference behavioral data, obtain distance feature.
The acquisition of described reference feature vector refers in the gyroscope behavioral data that contact action is corresponding, manhatton distance is adopted to calculate the distance of each behavioural characteristic vector to other behavioural characteristic vector, form the distance vector that this proper vector is corresponding, the minimum proper vector of chosen distance vector mould is as with reference to proper vector.
The advantage of the inventive method is: gyroscope behavioural characteristic is without the need to memory and carry, not easily stolen by others and forgery; Simple to operate, without the need to increasing any extras; The behavioral trait basis not changing user habit using gyroscope operation behavior describe cellphone subscriber to embody in verification process, in this, as the foundation that user identity judges, effectively can prevent from stain from attacking and observe attacking, there is security widely and applicability.In addition, adopt the character representation method of distance metric effectively can reduce the impact of behavior fluctuation, improve the robustness of authentication significantly.
Accompanying drawing explanation
Below in conjunction with the drawings and the specific embodiments, the present invention is described in further detail.
Fig. 1 is the overall procedure schematic diagram of the inventive method.
Fig. 2 is the idiographic flow schematic diagram of the gyroscope behavioral data extraction that in the inventive method, contact action is corresponding.
Fig. 3 is the idiographic flow schematic diagram of gyroscope behavior distance feature vector extracting method in the inventive method.
Fig. 4 is the screenshotss photo adopting the present invention to carry out the experimental implementation of smart phone user authentication.
Fig. 5 is the experimental result picture adopting the present invention to carry out smart phone user authentication.
Embodiment
See Fig. 1, a kind of smart phone user identity identifying method based on gyroscope behavioural characteristic, can be used for touch-screen equipment operator identity legitimacy and carries out certification, realizes the security protection of individual to touch-screen equipment storage inside and sensitive information.The present invention comprises Model of Identity Authentication System training and operator's authentication two parts, and concrete implementation step is as follows:
1) Model of Identity Authentication System training department divides and comprises the steps:
(1) the gyroscope behavioral data in the click repeatedly in fixing touch screen position of gyroscope collection recording user and slide (contact action) process, obtains training dataset; The form of gyroscope behavioral data is: { angular velocity of horizontal right direction (X-axis), the vertically upward angular velocity of direction (Y-axis), the angular velocity in screen front face normal will direction (Z axis), timestamp }.
(2) for the behavioral data that each gyroscope records, the gyroscope behavioral data (see Fig. 2) that contact action is corresponding is extracted.Be specially:
The first step, during extraction contact action, finger contact and the time leaving touch-screen, obtain the time interval of contact action;
Second step, mates the timestamp of the time interval of each contact action with gyroscope behavioral data, gyroscope behavioral data corresponding under obtaining each contact action.
(3) for the gyroscope behavioral data that contact action is corresponding, extraction behavioural characteristic vector, obtains reference feature vector wherein, calculates the distance of behavioural characteristic vector and reference feature vector, obtain distance feature vector, form training characteristics set (see Fig. 3).Be specially:
The first step, the gyroscope behavioral data that in each contact action process, contact action is corresponding is concentrated for training data, extract gyroscope behavioural characteristic vector, being specially contact action causes smart mobile phone to rock derived a series of gyroscope behavior measures, comprises statistical nature and distance feature two class.Wherein, statistical nature refers to the statistics description amount of the gyroscope behavioral data that each contact action is corresponding, comprises difference, coefficient of kurtosis, the coefficient of skewness of X-axis, Y-axis, the maximal value of Z axis angular velocity, minimum value, average, intermediate value, standard deviation, 75% fractile and 25% fractile; Distance feature refers to gyroscope behavioral data on identical contact action and with reference to the distance metric between behavioral data, its concrete calculation procedure is: first, in identical contact action corresponding gyroscope Behavioral training data, employing dynamic time warping calculates the distance between each gyroscope sample data and other gyroscope sample data, obtain distance sum after cumulative, the minimum gyroscope sample data of chosen distance sum is as with reference to behavioral data; Then, adopt the distance of dynamic time warping calculating on each identical contact action between gyroscope behavioral data and reference behavioral data, obtain distance feature;
Suppose that validated user training acquires n group data, use DTW (dynamic time warping) to calculate the distance sum of certain group data and other n-1 group data vectors respectively.Choose minimum with other n-1 group data Euclidean distances one group as with reference to sample;
Second step, adopt Euclidean distance to calculate gyroscope behavioural characteristic vector and the distance of other gyroscope behavioural characteristic vector in training set in each contact action process, obtain the distance vector (wherein P represent the number of times of in training set contact action process) of dimension for (P-1); Calculation training concentrates the mould of each distance vector, and the gyroscope behavior vector selecting modulus value minimum is as vectorial with reference to behavioural characteristic;
3rd step, manhatton distance is adopted to calculate gyroscope behavioural characteristic vector and the difference value vector with reference to behavior proper vector in each contact action process, as the distance feature vector of gyroscope behavioral data in each contact action process, form gyroscope Behavioral training feature set;
(4) one-class classifier is adopted to set up the Model of Identity Authentication System of validated user, be that positive class is trained Model of Identity Authentication System by the training characteristics aggregated label of validated user, obtain the judging identity threshold value σ (σ chooses according to the precision of model training) of validated user;
2) operator's identity continues authentication section, comprises the steps:
(1) when certification, gyroscope obtains the behavioral data that user's contact action identical with training produces, and extracts the gyroscope behavioral data that contact action is corresponding;
(2) for the gyroscope behavioral data that contact action is corresponding, extract behavioural characteristic vector, the reference feature vector obtained when training with the authentication model described in training part carries out distance metric, obtains distance feature vector;
(3) using the input of this distance feature vector as Model of Identity Authentication System, obtain the detected value of authentication, and by this detected value and validated user judging identity threshold value σ compare, if detected value is greater than threshold value, then the active user judged is as disabled user; If detected value is less than threshold value, then judge that active user is as validated user;
The experiment of smart phone user authentication is carried out according to the present invention
The present invention has carried out experimental verification for the authentication of smart phone user, and concrete steps are as follows:
The first step, generating training data.Requirement of experiment 10 users carry out contact action according to the mode of Fig. 4 respectively on smart mobile phone, repeat 30 times, comprise 3 touch screen clicking operation and 4 touch screen slides at every turn, gather and record gyroscope behavioral data when these users carry out aforesaid operations.In the mode shown in Fig. 4, user carries out touch screen clicking operation on point one, two, three, between point four, five, six, seven, eight, carry out touch screen slide.
Second step, extracts the gyroscope behavioral data that contact action is corresponding in each contact action process.For each user, extract contact action when starting and at the end of time, obtain the time interval that each contact action is corresponding; The timestamp of the time interval of each contact action with gyroscope behavioral data is mated, gyroscope behavioral data corresponding under extracting each contact action.
3rd step, generates distance feature vector.For each user, extract gyroscope behavioural characteristic vector in each contact action process, and choose reference feature vector, obtain distance feature vector after comparison behavioural characteristic vector sum reference feature vector, form training characteristics set.
4th step, Model of Identity Authentication System builds.For each user, be positive class by the training characteristics data markers of this user, adopt single category support vector machines to build the Model of Identity Authentication System of validated user, and utilize training characteristics the set pair analysis model to learn.
5th step, generates test data.For each user, require that its mode according to Fig. 4 carries out contact action, repeat 20 times, obtain test data set.
6th step, the certification of user identity legitimacy.Select a certain user as validated user, for wherein each test sample book, generation distance feature vector, it can be used as the input of this authenticating user identification model, obtain the detected value of authentication, and this detected value is compared with threshold value σ (σ is set as 0.55), if detected value is less than threshold value σ, then judge that this user is as disabled user; Otherwise, then judge that this user is as valid operation.
7th step, selects remaining users successively as validated user, repeats the process of above-mentioned 6th step, obtain user's average authentication result used.
For all users, test the inventive method carries out the accuracy of certification to smart phone user identity.Fig. 5 is average Receiver Operating Characteristics (the ReceiverOperating Characteristic of the present embodiment smart phone user, ROC) curve, horizontal ordinate is false acceptance rate (False-Acceptance Rate, FAR), the disabled user's sample number being judged to validated user when representing row authentication accounts for the number percent of disabled user's total sample number of test, is used for weighing disabled user and is determined as the probability of validated user by checking mistakenly; Ordinate is false rejection rate (False-Rejection Rate, FRR), the validated user sample number being judged to disabled user when representing and carry out authentication accounts for the number percent of the validated user total sample number of test, is used for weighing validated user and is determined as the probability of disabled user's refusal by checking mistakenly.
As can be seen from illustrated experimental result, the inventive method can carry out certification to the identity of smart phone user exactly.When FAR is 8.32%, FRR is 3.75%; When FAR is 2.14%, FRR is 7.28%; When FAR and FRR is equal, the error rate such as average of authentication is 5.78%.The above results demonstrates feasibility of the present invention and validity, shows that the method can be used as the identification safety authentication technology of a kind of efficient smart machine user.
Claims (4)
1., based on a smart phone user identity identifying method for gyroscope behavioural characteristic, it is characterized in that, by train and certification two parts are formed, wherein:
Described training department divides and comprises the steps:
(1) behavioral data that repeatedly formed in contact action process of gyroscope collection recording user, forms training data;
(2) for the behavioral data that each gyroscope records, the gyroscope behavioral data that contact action is corresponding is extracted;
(3) for the gyroscope behavioral data that contact action is corresponding, extract behavioural characteristic vector, obtain reference feature vector wherein, calculate behavioural characteristic vector and the distance of reference feature vector, obtain distance feature vector, form training characteristics set;
(4) adopt single classifier to set up the Model of Identity Authentication System of user, be that positive class is trained Model of Identity Authentication System by the training characteristics aggregated label of validated user, obtain the judging identity threshold value of validated user;
Described authentication section comprises the steps:
(1) when certification, gyroscope obtains the behavioral data that user's contact action identical with training produces, and extracts the gyroscope behavioral data that contact action is corresponding;
(2) for the gyroscope behavioral data that contact action is corresponding, extract behavioural characteristic vector, the reference feature vector obtained when training with the authentication model described in training part carries out distance metric, obtains distance feature vector;
(3) using vectorial for this distance feature input as Model of Identity Authentication System, obtain the detected value of authentication, and this detected value and judging identity threshold value are compared, and then judge the legitimacy of user identity.
2. the smart phone user identity identifying method based on gyroscope behavioural characteristic according to claim 1, it is characterized in that, the gyroscope behavioral data that described extraction contact action is corresponding refers to for each contact action, extract contact action when starting and at the end of time, obtain the time interval of contact action, and this time interval is mated with the behavioral data timestamp of gyroscope record, gyroscope behavioral data corresponding under obtaining each contact action.
3. the smart phone user identity identifying method based on gyroscope behavioural characteristic according to claim 1, it is characterized in that, the described gyroscope behavioral data corresponding for contact action, the concrete grammar extracting behavioural characteristic vector is: extract for gyroscope behavioral data corresponding to contact action, comprise statistical nature and distance feature, specific as follows:
Statistical nature refers to the statistics description amount of the gyroscope behavioral data that each contact action is corresponding, comprising:
Difference, coefficient of kurtosis, the coefficient of skewness of the maximal value of X-axis angular velocity, minimum value, average, intermediate value, standard deviation, 75% fractile and 25% fractile;
Difference, coefficient of kurtosis, the coefficient of skewness of the maximal value of Y-axis angular velocity, minimum value, average, intermediate value, standard deviation, 75% fractile and 25% fractile;
Difference, coefficient of kurtosis, the coefficient of skewness of the maximal value of Z axis angular velocity, minimum value, average, intermediate value, standard deviation, 75% fractile and 25% fractile;
Distance feature refers to the distance metric on identical contact action between gyroscope behavioral data and reference behavioral data, and its concrete calculation procedure is:
1) in identical contact action corresponding gyroscope Behavioral training data, comprise and organize sample data more, adopt dynamic time warping to calculate often to organize sample data and other organizes distance between sample data, obtain distance sum after cumulative, the minimum gyroscope sample data of chosen distance sum is as with reference to behavioral data;
2) adopt the distance of dynamic time warping calculating on each identical contact action between gyroscope behavioral data and reference behavioral data, obtain distance feature.
4. the smart phone user identity identifying method based on gyroscope behavioural characteristic according to claim 1, it is characterized in that, the acquisition of described reference feature vector refers in the gyroscope behavioral data that contact action is corresponding, manhatton distance is adopted to calculate the distance of each behavioural characteristic vector to other behavioural characteristic vector, form the distance vector that this proper vector is corresponding, the minimum proper vector of chosen distance vector mould is as with reference to proper vector.
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