CN108073809A - APT Heuristic detection methods and system based on abnormal component liaison - Google Patents
APT Heuristic detection methods and system based on abnormal component liaison Download PDFInfo
- Publication number
- CN108073809A CN108073809A CN201711420803.2A CN201711420803A CN108073809A CN 108073809 A CN108073809 A CN 108073809A CN 201711420803 A CN201711420803 A CN 201711420803A CN 108073809 A CN108073809 A CN 108073809A
- Authority
- CN
- China
- Prior art keywords
- component
- call relation
- environment information
- abnormal
- caching
- Prior art date
Links
- 230000002159 abnormal effects Effects 0.000 title claims abstract description 38
- 238000000034 methods Methods 0.000 claims abstract description 11
- 230000000875 corresponding Effects 0.000 claims abstract description 7
- 230000015572 biosynthetic process Effects 0.000 claims description 7
- 238000005755 formation reactions Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 3
- 238000007796 conventional methods Methods 0.000 description 3
- 241000700605 Viruses Species 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 238000010586 diagrams Methods 0.000 description 1
- 230000000750 progressive Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; 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
- G06F21/55—Detecting local intrusion or implementing counter-measures
Abstract
Description
Technical field
The present invention relates to computer network security technology field, more particularly to a kind of APT based on abnormal component liaison is opened Hairdo detection method and system.
Background technology
APT attacks are that a kind of advanced sustainability threatens, and APT attacks are with high concealment, specific aim, complexity Characteristic, the modularization of more and more functions uses, modular mode are realized.Component, which refers to, can complete function or a part of work( The independent individual of energy includes but not limited to executable program, dynamic link library etc..Modularization realize function, then need component it Between be associated calling, component liaison is to include but not limited to dynamic link to reach the relation of function formation between finger assembly Storehouse mode, process method of calling etc..The attack of attack load is launched in batches, and the batch time of component is launched in this kind of attack Interval time span is larger, but can not be malice by constructing each component meticulously, but the possible completion that combines is attacked Hit behavior.Traditional APT detection methods are detected based on single load, can not tackle the attack means of this complexity.
The content of the invention
Based on the above problem, the present invention proposes a kind of APT Heuristic detection methods based on abnormal component liaison and is System by component liaison relation, determines whether component is abnormal, solves the attacker that APT complexity can not be detected in conventional method Formula.
The present invention realizes by the following method:
A kind of APT Heuristic detection methods based on abnormal component liaison, including:
Whole launching process in monitoring system;
The call relation of all components and component environment information are recorded, and is cached to caching knowledge base;
To the component that system newly obtains, its call relation and component environment information are recorded;
By the call relation of the component newly obtained and component environment information and caching knowledge storehouse matching, based on matched rule, judge Whether the component in system is abnormal, if it is, being alerted to user, and risk is prompted, otherwise by the component of the new acquisition Call relation and the storage of component environment information are into caching knowledge base.
In the method, the component environment information includes at least:Finally be called time, component acquisition source and component Acquisition modes.
It is described based on matched rule in the method, judge whether the component in system is abnormal, is specially:If system Interior component meets following matched rule, then corresponding assembly is abnormal component in system:
It is longer into system interval, and form association from the inter-module being not called upon;
The inter-module formation that different component acquisition modes obtain source with component associates;
The different components that the frequent interconnected system of component newly obtains;
And user-defined matched rule.
In the method, further include:Call relation of the knowledge base by intelligence learning associated component is cached, to conventional tune White list is automatically added to relation.
A kind of heuristic detecting systems of APT based on abnormal component liaison, including:
Process monitoring module, whole launching process in monitoring system;
Data obtaining module records the call relation of all components and component environment information, and is cached to caching knowledge base;
To the component that system newly obtains, its call relation and component environment information are recorded;
Matching module, by the call relation of the component newly obtained and component environment information with caching knowledge storehouse matching, based on matching Rule judges whether the component in system is abnormal, if it is, being alerted to user, and risk is prompted, otherwise by the new acquisition Component call relation and component environment information storage to caching knowledge base in.
In the system, the component environment information includes at least:Finally be called time, component acquisition source and component Acquisition modes.
It is described based on matched rule in the system, judge whether the component in system is abnormal, is specially:If system Interior component meets following matched rule, then corresponding assembly is abnormal component in system:
It is longer into system interval, and form association from the inter-module being not called upon;
The inter-module formation that different component acquisition modes obtain source with component associates;
The different components that the frequent interconnected system of component newly obtains;
And user-defined matched rule.
In the system, further include:Call relation of the knowledge base by intelligence learning associated component is cached, to conventional tune White list is automatically added to relation.
The present invention also proposes a kind of non-transitorycomputer readable storage medium, is stored thereon with computer program, the journey Any APT Heuristic detection methods based on abnormal component liaison as described above are realized when sequence is executed by processor.
Advantage of the invention is that:Traditional single load detection mode is changed into associated payload detection mode;Pass through Fine granularity environment information acquisition, based on intelligence learning algorithm, depth excavates abnormal associated component;And pass through a variety of decision plans And self-defined decision plan, increase detection uncertainty, improve APT intrusion scenes, reduce APT concealments.The technology of the present invention Scheme can effectively detection components, modularization, engineering, highly concealed type, the APT of complexity attack.Intelligence learning side simultaneously Method avoids frequent updating virus base in conventional method.
Description of the drawings
It, below will be to embodiment or the prior art in order to illustrate more clearly of technical solution of the invention or of the prior art Attached drawing is briefly described needed in description, it should be apparent that, the accompanying drawings in the following description is only in the present invention Some embodiments recorded, for those of ordinary skill in the art, without creative efforts, can be with Other attached drawings are obtained according to these attached drawings.
Fig. 1 is a kind of APT Heuristic detection method embodiment flow charts based on abnormal component liaison of the present invention;
Fig. 2 is a kind of heuristic detecting system structure diagrams of APT based on abnormal component liaison of the present invention.
Specific embodiment
In order to which those skilled in the art is made to more fully understand the technical solution in the embodiment of the present invention, and make the present invention's Above-mentioned purpose, feature and advantage can be more obvious understandable, technical solution in the present invention made below in conjunction with the accompanying drawings further detailed Thin explanation.
The present invention realizes by the following method:
A kind of APT Heuristic detection methods based on abnormal component liaison, as shown in Figure 1, including:
S101:Whole launching process in monitoring system;
S102:The call relation of all components and component environment information are recorded, and is cached to caching knowledge base;
S103:To the component that system newly obtains, its call relation and component environment information are recorded;
S104:By the call relation of the component newly obtained and component environment information and caching knowledge storehouse matching, based on matched rule, Judge whether the component in system is abnormal, if it is, being alerted to user, and risk is prompted, otherwise by the group of the new acquisition Call relation and component environment the information storage of part are into caching knowledge base.
In the method, the component environment information includes but not limited to, finally be called the time, component obtain source and Component acquisition modes etc., component obtain source including such as actively download, application program download and USB flash disk obtain.
It is described based on matched rule in the method, judge whether the component in system is abnormal, is specially:If system Interior component meets following matched rule, then corresponding assembly is abnormal component in system:
It is longer into system interval, and form association from the inter-module being not called upon;The component of such case, Ke Nengwei It is longer to enter system time for the multiple dispensing of APT attacks, such as a component, but do not generate calling with any other component Relation, but form chaining with the new inter-module into system, then there are suspicious;
The inter-module formation that different component acquisition modes obtain source with component associates;The component of such case may be that APT is attacked In order to hide the attack of itself, launched by various ways;
The different components that the frequent interconnected system of component newly obtains;Such as loader mode, dynamic link library is downloaded in Loader loadings;
And user-defined matched rule;By user-defined strategy, the hidden of APT attacks can be further reduced Tibetan property increases the uncertainty of detection.
In the method, further include:Call relation of the knowledge base by intelligence learning associated component is cached, to conventional tune White list is automatically added to relation.Component that conventional call relation in system is called as required for when system starts, open it is clear Component of loading etc. required for the component and playout software of the required loading of device of looking at.
A kind of heuristic detecting systems of APT based on abnormal component liaison, as shown in Fig. 2, including:
Process monitoring module 201, whole launching process in monitoring system;
Data obtaining module 202 records the call relation of all components and component environment information, and is cached to caching knowledge base;
To the component that system newly obtains, its call relation and component environment information are recorded;
Matching module 203, by the call relation of the component newly obtained and component environment information and caching knowledge storehouse matching, based on With rule, judge whether the component in system is abnormal, if it is, being alerted to user, and prompts risk, is otherwise newly obtained described Call relation and component environment the information storage of the component taken are into caching knowledge base.
In the system, the component environment information includes at least:Finally be called time, component acquisition source and component Acquisition modes.
It is described based on matched rule in the system, judge whether the component in system is abnormal, is specially:If system Interior component meets following matched rule, then corresponding assembly is abnormal component in system:
It is longer into system interval, and form association from the inter-module being not called upon;
The inter-module formation that different component acquisition modes obtain source with component associates;
The different components that the frequent interconnected system of component newly obtains;
And user-defined matched rule.
In the system, further include:Call relation of the knowledge base by intelligence learning associated component is cached, to conventional tune White list is automatically added to relation.
The present invention also proposes a kind of non-transitorycomputer readable storage medium, is stored thereon with computer program, the journey Any APT Heuristic detection methods based on abnormal component liaison as described above are realized when sequence is executed by processor.
Advantage of the invention is that:Traditional single load detection mode is changed into associated payload detection mode;Pass through Fine granularity environment information acquisition, based on intelligence learning method, depth excavates abnormal associated component;And pass through a variety of decision plans And self-defined decision plan, increase detection uncertainty, improve APT intrusion scenes, reduce APT concealments.The technology of the present invention Scheme can effectively detection components, modularization, engineering, highly concealed type, the APT of complexity attack.Intelligence learning side simultaneously Method avoids frequent updating virus base in conventional method.
Each embodiment in this specification is described by the way of progressive, identical similar portion between each embodiment Point just to refer each other, and the highlights of each of the examples are difference from other examples.It is real especially for system For applying example, since it is substantially similar to embodiment of the method, so description is fairly simple, related part is referring to embodiment of the method Part explanation.
Although depicting the present invention by embodiment, it will be appreciated by the skilled addressee that the present invention there are many deformation and Change the spirit without departing from the present invention, it is desirable to which appended claim includes these deformations and changes without departing from the present invention's Spirit.
Claims (9)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711420803.2A CN108073809A (en) | 2017-12-25 | 2017-12-25 | APT Heuristic detection methods and system based on abnormal component liaison |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711420803.2A CN108073809A (en) | 2017-12-25 | 2017-12-25 | APT Heuristic detection methods and system based on abnormal component liaison |
Publications (1)
Publication Number | Publication Date |
---|---|
CN108073809A true CN108073809A (en) | 2018-05-25 |
Family
ID=62155898
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201711420803.2A CN108073809A (en) | 2017-12-25 | 2017-12-25 | APT Heuristic detection methods and system based on abnormal component liaison |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN108073809A (en) |
Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1818823A (en) * | 2005-02-07 | 2006-08-16 | 福建东方微点信息安全有限责任公司 | Computer protecting method based on programm behaviour analysis |
CN101373502A (en) * | 2008-05-12 | 2009-02-25 | 公安部第三研究所 | Automatic analysis system of virus behavior based on Win32 platform |
US20090158430A1 (en) * | 2005-10-21 | 2009-06-18 | Borders Kevin R | Method, system and computer program product for detecting at least one of security threats and undesirable computer files |
US20100058431A1 (en) * | 2008-08-26 | 2010-03-04 | Mccorkendale Bruce | Agentless Enforcement of Application Management through Virtualized Block I/O Redirection |
CN104794399A (en) * | 2015-04-23 | 2015-07-22 | 北京北信源软件股份有限公司 | Terminal protection system and method based on massive program behavior data |
CN104811447A (en) * | 2015-04-21 | 2015-07-29 | 深信服网络科技(深圳)有限公司 | Security detection method and system based on attack association |
CN104850780A (en) * | 2015-04-27 | 2015-08-19 | 北京北信源软件股份有限公司 | Discrimination method for advanced persistent threat attack |
CN104866765A (en) * | 2015-06-03 | 2015-08-26 | 康绯 | Behavior characteristic similarity-based malicious code homology analysis method |
CN105681286A (en) * | 2015-12-31 | 2016-06-15 | 中电长城网际系统应用有限公司 | Association analysis method and association analysis system |
CN106802821A (en) * | 2017-02-14 | 2017-06-06 | 腾讯科技(深圳)有限公司 | Recognition application installs the method and device in source |
-
2017
- 2017-12-25 CN CN201711420803.2A patent/CN108073809A/en active Search and Examination
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1818823A (en) * | 2005-02-07 | 2006-08-16 | 福建东方微点信息安全有限责任公司 | Computer protecting method based on programm behaviour analysis |
US20090158430A1 (en) * | 2005-10-21 | 2009-06-18 | Borders Kevin R | Method, system and computer program product for detecting at least one of security threats and undesirable computer files |
CN101373502A (en) * | 2008-05-12 | 2009-02-25 | 公安部第三研究所 | Automatic analysis system of virus behavior based on Win32 platform |
US20100058431A1 (en) * | 2008-08-26 | 2010-03-04 | Mccorkendale Bruce | Agentless Enforcement of Application Management through Virtualized Block I/O Redirection |
CN104811447A (en) * | 2015-04-21 | 2015-07-29 | 深信服网络科技(深圳)有限公司 | Security detection method and system based on attack association |
CN104794399A (en) * | 2015-04-23 | 2015-07-22 | 北京北信源软件股份有限公司 | Terminal protection system and method based on massive program behavior data |
CN104850780A (en) * | 2015-04-27 | 2015-08-19 | 北京北信源软件股份有限公司 | Discrimination method for advanced persistent threat attack |
CN104866765A (en) * | 2015-06-03 | 2015-08-26 | 康绯 | Behavior characteristic similarity-based malicious code homology analysis method |
CN105681286A (en) * | 2015-12-31 | 2016-06-15 | 中电长城网际系统应用有限公司 | Association analysis method and association analysis system |
CN106802821A (en) * | 2017-02-14 | 2017-06-06 | 腾讯科技(深圳)有限公司 | Recognition application installs the method and device in source |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10699011B2 (en) | Efficient white listing of user-modifiable files | |
EP3528458B1 (en) | A cyber security appliance for a cloud infrastructure | |
US9916440B1 (en) | Detection efficacy of virtual machine-based analysis with application specific events | |
US10902117B1 (en) | Framework for classifying an object as malicious with machine learning for deploying updated predictive models | |
AU2017254815B2 (en) | Anomaly detection to identify coordinated group attacks in computer networks | |
US20170214701A1 (en) | Computer security based on artificial intelligence | |
US9807120B2 (en) | Method and system for automated incident response | |
US10873597B1 (en) | Cyber attack early warning system | |
US9794279B2 (en) | Threat indicator analytics system | |
US20170213031A1 (en) | Kernel-Level Security Agent | |
JP2016053979A (en) | System and method for local protection against malicious software | |
US9244671B2 (en) | System and method for deploying preconfigured software | |
EP3216193B1 (en) | Recombinant threat modeling | |
US9106692B2 (en) | System and method for advanced malware analysis | |
US10147049B2 (en) | Automatic generation of training data for anomaly detection using other user's data samples | |
JP6208761B2 (en) | Return-oriented programming threat detection | |
Pincus et al. | Beyond stack smashing: Recent advances in exploiting buffer overruns | |
CN102902909B (en) | A kind of system and method preventing file to be tampered | |
US10574675B2 (en) | Similarity search for discovering multiple vector attacks | |
US20160197951A1 (en) | Method and system for virtual asset assisted extrusion and intrusion detection and threat scoring in a cloud computing environment | |
US20190156023A1 (en) | System for securing software containers with embedded agent | |
KR102189295B1 (en) | Continuous classifiers for computer security applications | |
US9306962B1 (en) | Systems and methods for classifying malicious network events | |
US9021584B2 (en) | System and method for assessing danger of software using prioritized rules | |
US9392017B2 (en) | Methods, systems, and media for inhibiting attacks on embedded devices |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
CB02 | Change of applicant information |
Address after: 150028 Building 7, Innovation Plaza, Science and Technology Innovation City, Harbin Hi-tech Industrial Development Zone, Harbin, Heilongjiang Province (838 Shikun Road) Applicant after: Harbin antiy Technology Group Limited by Share Ltd Address before: 150090 Room 506, No. 162 Hongqi Street, Nangang District, Harbin Development Zone, Heilongjiang Province Applicant before: Harbin Antiy Technology Co., Ltd. |
|
CB02 | Change of applicant information |