CN117131543A - Mobile phone safety privacy protection system - Google Patents

Abstract

The invention relates to the technical field of information security, especially a mobile phone security and privacy protection system, which includes: an access authentication module: used to authenticate user identity; a software detection module: used to perform security detection on software installed by the user; and a permission access module: used to query And set the access rights of the software; the isolation operation module: used to run the software in isolation according to the set access rights. The invention ensures that the mobile phone is in a safe operating environment by confirming the identity of the user who accesses the mobile phone. At the same time, it performs security detection on the software through a security detection algorithm obtained by improving the Seagull optimization algorithm, ensuring the security of the installed software and reducing the exposure of user information. Risks of leakage are eliminated, and user information security is further ensured by inquiring about software permissions and running in isolation.

Description

Mobile phone security and privacy protection system

Technical field

The invention relates to the technical field of information security, in particular to a mobile phone security and privacy protection system.

Background technique

With the promotion of 5G technology and the popularity of smartphones, mobile phones are no longer only used in the field of personal consumption, but are also constantly used in the field of official business, such as drafting business documents, sending and receiving company emails, etc. With the increase in the number of application software installed on mobile phones, Its security issues are no longer limited to personal privacy, but also involve the information security issues of confidential organizations. The security protection of data on mobile phones has attracted more and more attention; the information security protection system currently established in mobile phones There are still flaws, and the identification and detection function of the software needs to be further improved to avoid the situation where the software downloaded by the user has the hidden malicious intent to obtain the user's private information and leak it, putting the user's security and privacy at risk.

Contents of the invention

The purpose of the present invention is to solve the above-mentioned defects in the background technology by proposing a mobile phone security and privacy protection system.

The technical solutions adopted by the present invention are as follows:

Provide mobile phone security and privacy protection system, including:

Access authentication module: used to authenticate user identity;

Software detection module: used to perform security detection on software installed by users;

Permission access module: used to query and set software access permissions;

Isolated running module: used to run the software in isolation according to the set access rights.

As a preferred technical solution of the present invention: it also includes a database, which encrypts and stores mobile phone data information based on the DES algorithm.

As a preferred technical solution of the present invention: the DES algorithm undergoes 16 rounds of DES encryption operations and generates a key. After the mobile phone authenticates the user's identity through the access authentication module, the reverse decryption process of the data is performed according to the user's permission.

As a preferred technical solution of the present invention: the access authentication module confirms the user's identity through face authentication, fingerprint authentication or password authentication.

As a preferred technical solution of the present invention: the software detection module detects the permission application information of the software and the basic information of the software.

As a preferred technical solution of the present invention: the software detection module detects software based on a security detection algorithm.

As a preferred technical solution of the present invention: the security detection algorithm is specifically as follows:

Normalize the software information, perform feature extraction on the normalized software information, and then divide the software information after feature extraction into the following centroid divisions:

Among them, c _j is the j-th centroid of software information, a _i is the i-th software feature vector, N is the number of software feature vectors, b _j (a _i ) is the relationship between the i-th software feature vector and the j-th centroid degree of association;

Among them, d(a _i ,c _j ) is the distance between the software feature vector and the centroid, mind(a _i ,c _j ) is the minimum distance between the software feature vector and the centroid;

Initialize the centroid and iterate, calculating the error rate and error value:

Among them, η ^l is the error rate of the l-th iteration, σ ^l-1 is the error value of the l-1 iteration, σ ^l is the error value of the l-th iteration, and σ is the error value;

The error threshold Δ is obtained based on the improved seagull optimization algorithm. When the error rate is not greater than the set error threshold, the algorithm returns to continue iteration until the error rate is greater than the set error threshold.

As a preferred technical solution of the present invention: the improved seagull optimization algorithm is specifically as follows:

y _I =δ×Y _I (τ)

Among them, y _I is the position of the I seagull individual after completing the collision avoidance, δ is an additional variable representing the movement behavior of the seagull individual in the search space, Y _I (τ) is the position of the I seagull individual in the τ iteration , r ₁ is a random number that linearly decreases from 2 to 0, and T is the maximum number of iterations;

b _I =B×(Y _b (τ)-Y _I (τ))

B＝2×δ ² ×r ₂

Among them, Y _b (τ) is the optimal seagull individual in the τ iteration, B is the global search and local development used for the balancing algorithm, b _I is the direction of the optimal seagull individual, and r ₂ is [0,1] random number between;

The seagull updates its location based on the location information of the optimal seagull individual:

_ZI = _yI + _bI

Among them, Z _I is the distance between the seagull individual and the optimal seagull individual;

Seagull individuals attack prey in a spiral flight trajectory during their attack behavior. On the x, y, and z planes, the attack behavior is:

x ^′ =r×cos q

y ^′ =r×sinq

z ^′ =r×q

r＝ε×a× ^eqb

Among them, r is the radius of each circle of the individual seagull's spiral flight trajectory, q is a random number between [0, 2π], a and b are constants used to define the shape of the spiral flight trajectory, ε is the dynamic convergence factor, r ₃ is a random number between [0,1], Y _I (τ+1) is the updated position of the I-th seagull individual in the τ+1 iteration, ρ is the seagull attack mode distinction threshold, x ^′ , y ^′ and z ^′ is the position updated after the individual seagull performs aggressive behavior on the x, y, and z planes.

As a preferred technical solution of the present invention: the permission access module sets permission questions and asks the user based on the permission application and information detection results of the software detection module, and sets permissions according to the user's selection results.

As a preferred technical solution of the present invention: the software detection module performs isolated operation of the software based on the setting result of the isolated operation module, and performs alarm and software stop operation processing for abnormal operation behavior of the software.

Compared with the existing technology, the mobile phone security and privacy protection system provided by the present invention has the following beneficial effects:

The present invention ensures that the mobile phone is in a safe operating environment by confirming the identity of the user who accesses the mobile phone. At the same time, it performs security detection on the software through a security detection algorithm obtained by improving the Seagull optimization algorithm, ensuring the security of the installed software and reducing the exposure of user information. Risks of leakage are eliminated, and user information security is further ensured by inquiring about software permissions and running in isolation.

Description of the drawings

Figure 1 is a modular structure diagram of the system of the present invention;

The meaning of each mark in the figure is: 100. Access authentication module; 200. Software detection module; 300. Permission setting module; 400. Isolated operation module; 500. Database.

Detailed ways

It should be noted that, without conflict, the embodiments and features in the embodiments can be combined with each other. The technical solutions in the embodiments of the present invention will be described below in conjunction with the drawings in the embodiments of the present invention. Clearly and completely described, it is obvious that the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

Referring to Figure 1, a preferred embodiment of the present invention provides a mobile phone security and privacy protection system, including:

Access authentication module 100: used to authenticate user identity;

Software detection module 200: used to perform security detection on software installed by users;

Permission access module 300: used to query and set software access permissions;

Isolated operation module 400: used to perform isolated operation of software according to the set access rights.

It also includes a database 500, which encrypts and stores mobile phone data information based on the DES algorithm.

The DES algorithm undergoes 16 rounds of DES encryption operations and generates a key. After the mobile phone authenticates the user's identity through the access authentication module 100, the data is reversely decrypted according to the user's permission.

The access authentication module 100 confirms the user's identity through face authentication, fingerprint authentication or password authentication.

The software detection module 200 detects the permission application information of the software and the basic information of the software.

The software detection module 200 detects software based on a security detection algorithm.

The security detection algorithm is specifically as follows:

Normalize the software information, perform feature extraction on the normalized software information, and then divide the software information after feature extraction into the following centroid divisions:

Among them, c _j is the j-th centroid of software information, a _i is the i-th software feature vector, N is the number of software feature vectors, b _j (a _i ) is the relationship between the i-th software feature vector and the j-th centroid degree of association;

Among them, d(a _i ,c _j ) is the distance between the software feature vector and the centroid, mind(a _i ,c _j ) is the minimum distance between the software feature vector and the centroid;

Initialize the centroid and iterate, calculating the error rate and error value:

Among them, η ^l is the error rate of the l-th iteration, σ ^l-1 is the error value of the l-1 iteration, σ ^l is the error value of the l-th iteration, and σ is the error value;

Based on the improved seagull optimization algorithm, the error threshold θ is obtained through optimization. When the error rate is not greater than the set error threshold, the algorithm returns to continue iteration until the error rate is greater than the set error threshold, and the algorithm is terminated.

The details of the improved Seagull optimization algorithm are as follows:

y _I =δ×Y _I (τ)

Among them, y _I is the position of the I seagull individual after completing the collision avoidance, δ is an additional variable representing the movement behavior of the seagull individual in the search space, Y _I (τ) is the position of the I seagull individual in the τ iteration , r ₁ is a random number that linearly decreases from 2 to 0, and T is the maximum number of iterations;

b _I =B×(Y _b (τ)-Y _I (τ))

B＝2×δ ² ×r ₂

Among them, Y _b (τ) is the optimal seagull individual in the τ iteration, B is the global search and local development used for the balancing algorithm, b _I is the direction of the optimal seagull individual, and r ₂ is [0,1] random number between;

The seagull updates its location based on the location information of the optimal seagull individual:

_ZI = _yI + _bI

Among them, Z _I is the distance between the seagull individual and the optimal seagull individual;

Seagull individuals attack prey in a spiral flight trajectory during their attack behavior. On the x, y, and z planes, the attack behavior is:

x ^′ =r×cos q

y ^′ =r×sinq

z ^′ =r×q

r＝ε×a× ^eqb

Among them, r is the radius of each circle of the individual seagull's spiral flight trajectory, q is a random number between [0, 2π], a and b are constants used to define the shape of the spiral flight trajectory, ε is the dynamic convergence factor, r ₃ is a random number between [0,1], Y _I (τ+1) is the updated position of the I-th seagull individual in the τ+1 iteration, ρ is the seagull attack mode discrimination threshold, x ^′ , y ^′ and z ^′ is the position updated after the individual seagull performs aggressive behavior on the x, y, and z planes.

The permission access module 300 sets permission questions and asks the user based on the permission application and information detection results of the software detection module 200, and sets permissions according to the user's selection results.

The software detection module 400 performs isolation operation of the software based on the setting result of the isolation operation module 300, and performs alarm and software stop operation processing for abnormal operation behavior of the software.

In this embodiment, the database 500 collects the user's mobile phone data information, and performs encryption processing of the user's mobile phone data information based on the DES algorithm. In the DES algorithm, first, a 56-bit key needs to be generated; usually, a cryptographically secure pseudo-random number generator is used to generate the key, and then a key generation algorithm is used to extend the 56-bit key to 16 48 bit subkey; each subkey is used for the corresponding round function; the data to be encrypted is grouped into groups of 64 bits. If the data length is not a multiple of 64 bits, padding is used to complete it; for each The 64-bit data blocks are initially replaced by IP to disrupt their order to increase the randomness of encryption; finally, each 64-bit data block is encrypted for 16 rounds of iterations. The advantages of the DES algorithm are fast encryption speed and high security. In the encryption process of mobile phone data information, the corresponding encryption data structure can be selected based on different scenarios, the key points and difficulties of confidentiality can be analyzed, and a reasonable and scientific algorithm type can be selected.

The access authentication module 100 confirms the user's identity through face authentication, fingerprint authentication or password authentication, confirms that the operator is the user himself, and ensures the safe operation of the mobile phone.

The software detection module 200 performs basic detection on the software's permission application information, version information, software name information, etc., and performs malware detection through a security detection algorithm to confirm whether the software has the risk of information leakage:

First, the software information is normalized to standardize the software information, balance the impact of the software information on the distance, and help reflect the true degree of dissimilarity of the software information.

Then calculate the centroid of the software information features, divide the software information into multiple data categories, and perform iterative division of the data through the error threshold obtained through optimization.

And perform feature extraction processing on the normalized software information, and divide the software information after feature extraction processing into the following centroid divisions:

Among them, c _j is the j-th centroid of software information, a _i is the i-th software feature vector, N is the number of software feature vectors, b _j (a _i ) is the relationship between the i-th software feature vector and the j-th centroid degree of association;

Among them, d(a _i ,c _j ) is the distance between the software feature vector and the centroid, mind(a _i ,c _j ) is the minimum distance between the software feature vector and the centroid;

Initialize the centroid and iterate, calculating the error rate and error value:

Among them, η ^l is the error rate of the l-th iteration, σ ^l-1 is the error value of the l-1 iteration, σ ^l is the error value of the l-th iteration, and σ is the error value;

Based on the improved seagull optimization algorithm, the error threshold θ is optimized:

y _I =δ×Y _I (τ)

Among them, y _I is the position of the I seagull individual after completing the collision avoidance, δ is an additional variable representing the movement behavior of the seagull individual in the search space, Y _I (τ) is the position of the I seagull individual in the τ iteration , r ₁ is a random number that linearly decreases from 2 to 0, and T is the maximum number of iterations;

b _I =B×(Y _b (τ)-Y _I (τ))

B＝2×δ ² ×r ₂

Among them, Y _b (τ) is the optimal seagull individual in the τ iteration, B is the global search and local development used for the balancing algorithm, b _I is the direction of the optimal seagull individual, and r ₂ is [0,1] random number between;

The seagull updates its location based on the location information of the optimal seagull individual:

_ZI = _yI + _bI

Among them, Z _I is the distance between the seagull individual and the optimal seagull individual;

Seagull individuals attack prey in a spiral flight trajectory during their attack behavior. On the x, y, and z planes, the attack behavior is:

x ^′ =r×cos q

y ^′ =r×sinq

z ^′ =r×q

r＝ε×a× ^eqb

Among them, r is the radius of each circle of the individual seagull's spiral flight trajectory, q is a random number between [0, 2π], a and b are constants used to define the shape of the spiral flight trajectory, ε is the dynamic convergence factor, r ₃ is a random number between [0,1], Y _I (τ+1) is the updated position of the I-th seagull individual in the τ+1 iteration, ρ is the seagull attack mode discrimination threshold, x ^′ , y ^′ and z ^′ is the position updated after the individual seagull performs aggressive behavior on the x, y, and z planes.

The Seagull optimization algorithm has the problem of single attack direction when attacking. The improved attack method can enhance the direction diversity of the Seagull optimization algorithm when performing spiral attacks and reduce the possible attack blind spots during the local search process.

Judgment is made based on the error threshold θ obtained through optimization. When the error rate is not greater than the set error threshold, the algorithm returns and continues to iterate until the error rate is greater than the set error threshold, and the algorithm is terminated.

The software abnormality information is determined based on the iterative division results. For cases where abnormal data is determined, the software is determined to be malware and an abnormality alarm is issued to the user.

For software that is classified as normal, the permission setting module 300 asks the user whether to grant corresponding permissions based on the detected permission application information. The isolation operation module 400 performs isolation operation of the software based on the user's selection results to prevent the mobile phone from appearing during the operation of the software. For the behavior of data information theft, alarms and stop operations will be processed for detected abnormal behaviors.

It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the embodiments should be regarded as illustrative and non-restrictive from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is therefore intended that all claims falling within the claims All changes within the meaning and scope of equivalent elements are included in the present invention. Any reference signs in the claims shall not be construed as limiting the claim in question.

In addition, it should be understood that although this specification is described in terms of implementations, not each implementation only contains an independent technical solution. This description of the specification is only for the sake of clarity, and those skilled in the art should take the specification as a whole. , the technical solutions in each embodiment can also be appropriately combined to form other implementations that can be understood by those skilled in the art.

Claims (10)

1. The mobile phone safety privacy protection system is characterized in that: comprising the following steps:

access authentication module (100): for authenticating the identity of the user;

software detection module (200): the security detection device is used for carrying out security detection on software installed by a user;

rights access module (300): for querying and setting access rights of the software;

isolation run module (400): and the software isolation operation is performed according to the set access authority.

2. The mobile phone security privacy protection system of claim 1, wherein: the mobile phone data encryption method further comprises a database (500), and the database (500) stores mobile phone data information in an encryption mode based on a DES algorithm.

3. The mobile phone security privacy protection system of claim 2, wherein: the DES algorithm generates a secret key through 16 rounds of DES encryption operation, and after the mobile phone authenticates the user identity through the access authentication module (100), the reverse decryption processing of the data is performed according to the user permission.

4. The mobile phone security privacy protection system of claim 1, wherein: the access authentication module (100) confirms the identity of the user through a mode of face authentication, fingerprint authentication or password authentication.

5. The mobile phone security privacy protection system of claim 1, wherein: the software detection module (200) detects authority application information of the software and basic information of the software.

6. The mobile phone security privacy protection system of claim 5, wherein: the software detection module (200) detects software based on a security detection algorithm.

7. The mobile phone security privacy protection system of claim 6, wherein: the security detection algorithm is specifically as follows:

normalizing the software information, performing feature extraction processing on the software information after the normalization processing, and performing centroid division on the software information after the feature extraction processing as follows:

wherein c _j Is the j-th centroid of software information, a _i For the ith software feature vector, N is the number of the software feature vectors, b _j (a _i ) The degree of association of the ith software feature vector and the jth centroid;

wherein d (a) _i ,c _j ) For the distance of the software feature vector from the centroid, mini (a _i ,c _j ) The minimum distance between the software feature vector and the centroid;

initializing the centroid and iterating, and calculating error rate and error value:

wherein eta ^l Error rate, σ, for the first iteration ^l-1 For the error value, σ, of iteration 1-1 ^l The error value of the first iteration is shown as sigma, and the error value of the first iteration is shown as sigma;

and optimizing and acquiring an error threshold theta based on the improved seagull optimization algorithm, returning to continue iteration when the error rate is not greater than the set error threshold until the error rate is greater than the set error threshold, and terminating the algorithm.

8. The mobile phone security privacy protection system of claim 7, wherein: the improved seagull optimization algorithm is specifically as follows:

y _I ＝δ×Y _I (τ)

wherein y is _I The position after collision avoidance is completed for the I-th seagull individual, delta is an additional variable representing the motion behavior of the seagull individual in the search space, Y _I (τ) is the position of the individual seagull of the τ iteration, r ₁ For a random number linearly decreasing from 2 to 0, T is the maximum number of iterations;

b _I ＝B×(Y _b (τ)-Y _I (τ))

B＝2×δ ² ×r ₂

wherein Y is _b (τ) is the optimal individual seagull for the τ -th iteration, B is the global search and local development for the balancing algorithm, B _I Is the direction of the optimal seagull individual, r ₂ Is [0,1]Random numbers in between;

the seagull updates the position according to the position information of the optimal seagull individual:

Z _I ＝y _I +b _I

wherein Z is _I Is the distance between the individual seagulls and the optimal individual seagulls;

the seagull individuals attack prey objects in a spiral flight track in the attack behaviors, and the attack behaviors are as follows in the x, y and z planes:

x′＝r×cos q

y′＝r×sin q

z′＝r×q

r＝ε×a×e ^qb

wherein r is the radius of each circle of the individual helical flight track of the seagull, and q is [0,2 pi ]]A random number, a and b are constants for defining the shape of the spiral flight path, epsilon is a dynamic convergence factor, and r ₃ Is [0,1]Random number, Y between _I (tau+1) is the updated position of the I-th seagull individual in the tau+1 iteration, ρ is the threshold value of the seagull attack mode, and x ', y ' and z ' are the updated positions of the seagull individual after the attack on the x, y and z planes.

9. The mobile phone security privacy protection system of claim 8, wherein: the authority access module (300) sets an authority problem according to the authority application information and the detection result of the software detection module (200), inquires the user, and sets the authority according to the user selection result.

10. The mobile phone security privacy protection system of claim 9, wherein: the software detection module (400) performs isolated operation of the software based on the setting result of the isolated operation module (300), and alarms abnormal operation behaviors of the software and software stopping operation processing.