Comprehensive detection system for defending multiple network threats and implementation method thereof
By integrating multiple detection modules and intelligent detection algorithms in the network security system, the problems of limited detection capabilities, slow response speed and poor compatibility in the prior art are solved, and comprehensive detection and defense of multiple network threats in complex network environments are achieved.
Patent Information
- Application Number
- CN202411628201.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-14
- Publication Date
- 2025-03-18
AI Technical Summary
When detecting and defending against multiple network threats in complex network environments, the prior art has limited detection capabilities, slow response speed, poor compatibility, and difficult to fully cover multiple threat types.
It provides a comprehensive detection system, including malicious file detection module, penetration behavior detection module, Trojan activity detection module, blacklist detection module and abnormal behavior detection module, and uses multiple network protocols and intelligent detection algorithms to identify and alert multiple network threats.
It realizes comprehensive detection and defense of multiple network threats in complex network environments, improves detection capabilities and response speed, and ensures compatibility and integrity in different operating system environments.
Smart Images
Figure CN119652562A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of network security, and in particular to a comprehensive detection system for defending against multiple network threats and an implementation method thereof. Background Art
[0002] With the continuous development of network attack technology, threats such as malicious files, penetration behaviors, Trojan activities, blacklist attacks and abnormal behaviors are increasing day by day. Traditional security protection methods are difficult to fully cope with these complex network threats. Existing technologies have shortcomings in detecting and defending against these threats, especially in the detection capabilities and response speeds under multiple protocols and scenarios. Therefore, a system that can comprehensively detect and defend against multiple network threats is needed to cope with complex network environments and various types of attack behaviors.
[0003] In the prior art, network security detection usually relies on specific detection tools and protection methods. For example, some tools may only support malicious file detection under a specific protocol, while other tools may only support specific types of penetration behavior detection. In addition, the prior art often requires the use of different tools or plug-ins when dealing with multiple threat types, resulting in complex operations and low efficiency.
[0004] The closest implementations include:
[0005] Single threat detection tool: For example, some malicious file detection tools only support malicious file detection under specific protocols.
[0006] Specific behavior detection tools: For example, some penetration behavior detection tools only support specific types of penetration behavior detection.
[0007] Multi-threat detection tools: such as some comprehensive tools, support multiple threat detection, but the detection capabilities and response speed are limited.
[0008] However, the prior art has the following disadvantages:
[0009] Limited detection capabilities: Existing tools usually only support detection of a single or a few types of threats and cannot comprehensively cover multiple threat types.
[0010] Slow response speed: Existing tools often need to use different tools or plug-ins to handle multiple threat types, resulting in slow response speed.
[0011] Poor compatibility: Existing tools have poor compatibility in different operating system environments and cannot guarantee the integrity and accuracy of detection and defense. Summary of the invention
[0012] Based on this, the embodiments of the present application provide a comprehensive detection system for defending against multiple network threats and its implementation method, which can comprehensively detect and defend against various network threats such as malicious files, infiltration behaviors, Trojan activities, blacklist attacks and abnormal behaviors, and generate alarms and reports to cope with complex network environments and various types of attack behaviors.
[0013] In a first aspect, a comprehensive detection method for defending against multiple network threats is provided, the method comprising:
[0014] Malicious file detection module, used to identify and detect malicious files transmitted through various network protocols, and generate alarms and report the malicious files found; malicious files include at least secret stealing Trojans, remote control Trojans, backdoor programs, malicious documents and hacker tools;
[0015] The penetration behavior detection module is used to identify network penetration behaviors, system vulnerability exploitation behaviors, and WEB vulnerability exploitation behaviors, and generate alarms and report them when they are identified;
[0016] A Trojan activity detection module is used to monitor and detect Trojan activities by analyzing the communication load, and generate an alarm and report when the existence is identified; wherein Trojan activities at least include the control behavior and data theft behavior of secret stealing Trojans and remote control Trojans;
[0017] Blacklist detection module, used to detect various attack activities according to preset blacklist types, including domain names, IP addresses, URLs and accounts; it also supports detecting blacklist items contained in email headers and bodies to identify and report attack behaviors;
[0018] The abnormal behavior detection module is used to detect abnormal behavior in single-session traffic, and generate an alarm and report it when it is identified; wherein the abnormal behavior in single-session traffic at least includes abnormal login, abnormal transmission and abnormal email.
[0019] Optionally, the malicious file detection module specifically includes:
[0020] Receive the file content transmitted in the network traffic and identify malicious files by checking whether the file content contains specific malicious signatures. Once a malicious file is detected, the system will trigger an alarm mechanism and report the relevant information to the security management center.
[0021] Optionally, the penetration behavior detection module specifically includes:
[0022] Analyze network traffic, specifically network data packets containing attack behaviors, and identify infiltration behaviors by detecting whether password cracking keywords are contained in network traffic. Once infiltration behaviors are detected, an alarm is generated and reported.
[0023] Optionally, the Trojan activity detection module specifically includes:
[0024] Receive and analyze network communication payloads, and identify Trojan activities by checking whether the payload contains control commands. Once Trojan activities are found, an alarm will be triggered and reported.
[0025] Optionally, the blacklist detection module specifically includes:
[0026] Receive network data, including email headers and email bodies;
[0027] Identify attack behaviors by checking whether the data contains domain names in the blacklist;
[0028] When a blacklist item is detected, an alarm will be generated and reported so that defensive measures can be taken.
[0029] Optionally, the abnormal behavior detection module specifically includes:
[0030] Analyze the network traffic of a single session and check whether the session traffic contains any signs of abnormal login behavior. Once abnormal behavior is found, an alarm will be triggered and reported.
[0031] Optionally, the system further comprises: a threat detection module for analyzing data packets in network traffic to identify and classify network threats, wherein the threat detection algorithm extracts features of the data packet content, including specific signatures, behavior patterns or abnormal traffic.
[0032] Optionally, the system further includes: an alarm reporting module, which is used to classify detected threats and trigger corresponding reporting mechanisms according to threat types; including evaluating the severity of the threat and deciding whether relevant personnel or systems need to be notified immediately.
[0033] Optionally, the data packet objects detected by the system include HTTP data packets, SMTP data packets and FTP data packets.
[0034] In a second aspect, a method for implementing a comprehensive detection system for defending against multiple network threats based on any one of the first aspect is provided, the method comprising:
[0035] Identify and detect malicious files transmitted through various network protocols, and generate alarms and report the malicious files found; malicious files include at least secret-stealing Trojans, remote control Trojans, backdoor programs, malicious documents, and hacker tools;
[0036] Identify network penetration, system vulnerability exploitation, and WEB vulnerability exploitation, and generate and report alarms when they are identified;
[0037] By analyzing the communication payload monitoring and detecting Trojan activities, an alarm is generated and reported when the existence is identified; Trojan activities at least include the control behavior and data theft behavior of secret stealing Trojans and remote control Trojans;
[0038] Detect various attack activities based on preset blacklist types, including domain names, IP addresses, URLs, and accounts; also supports detecting blacklist items contained in email headers and bodies to identify and report attack behaviors;
[0039] Detect abnormal behaviors in single-session traffic, and generate alarms and report them when they are identified; wherein, abnormal behaviors in single-session traffic include at least abnormal logins, abnormal transmissions, and abnormal emails.
[0040] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:
[0041] Comprehensiveness: Supports multiple transmission protocols and threat types, and can fully cover complex network environments and various types of threats.
[0042] Compatibility: The integrity and accuracy of threat detection and defense can be guaranteed in different operating system environments.
[0043] Efficiency: Intelligent detection and efficient alarm reporting algorithms ensure fast and accurate detection and reporting of threat information.
[0044] Ease of use: The system is easy to operate, and users can complete threat detection and defense tasks without using multiple tools or plug-ins. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.
[0046] Figure 1 An architecture diagram of a comprehensive detection system for defending against multiple network threats provided in an embodiment of the present application;
[0047] Figure 2 A threat detection flow chart provided for an embodiment of the present application;
[0048] Figure 3 HTTP data packet structure diagram provided for the embodiment of the present application;
[0049] Figure 4 SMTP data packet structure diagram provided for the embodiment of the present application;
[0050] Figure 5 This is a diagram of the FTP data packet structure provided in an embodiment of the present application. DETAILED DESCRIPTION
[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0052] In the description of the present invention, the terms "comprises", "has" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may also include other steps or units that are not explicitly listed but are inherent to these processes, methods, products or apparatuses, or steps or units added based on further optimization schemes conceived by the present invention.
[0053] The purpose of the present invention is to provide a multi-protocol, multi-threat type network security detection system that can comprehensively detect and defend against multiple network threats such as malicious files, penetration behaviors, Trojan horse activities, blacklist attacks and abnormal behaviors, and generate alarms and reports to cope with complex network environments and various types of attack behaviors. Figure 1 , which shows a comprehensive detection system architecture diagram for defending against multiple network threats. The present invention proposes a multi-protocol, multi-threat type network security detection system, which mainly includes the following modules:
[0054] The malicious file detection module is used to identify and detect malicious files transmitted through various network protocols, and generate alarms and report the discovered malicious files.
[0055] Among them, malicious files include at least secret-stealing Trojans, remote-control Trojans, backdoor programs, malicious documents and hacker tools; specifically, the module receives file contents transmitted in network traffic, and identifies malicious files by checking whether the file contents contain specific malicious signatures. Once a malicious file is detected, the system will trigger an alarm mechanism and report relevant information to the security management center.
[0056] The penetration behavior detection module is used to identify network penetration behavior, system vulnerability exploitation behavior, and WEB vulnerability exploitation behavior, and generate an alarm and report it when it is identified.
[0057] Specifically, the module analyzes network traffic, including network data packets containing attack behaviors, and identifies infiltration behaviors by detecting whether password cracking keywords are contained in the network traffic. After infiltration behaviors are detected, an alarm is generated and reported.
[0058] The Trojan activity detection module is used to monitor and detect Trojan activities by analyzing the communication load, and generate an alarm and report it when it is identified.
[0059] Among them, Trojan horse activities at least include control behaviors and data theft behaviors of secret stealing Trojan horses and remote control Trojan horses. Specifically, the module analyzes network traffic, including network data packets containing attack behaviors, and identifies penetration behaviors by detecting whether password cracking keywords are contained in network traffic. After detecting penetration behaviors, an alarm is generated and reported.
[0060] The blacklist detection module is used to detect various attack activities based on preset blacklist types, including domain names, IP addresses, URLs, and accounts; it also supports detecting blacklist items contained in email headers and bodies to identify and report attack behaviors.
[0061] Specifically, the module receives and analyzes network communication payloads, identifies Trojan activity by checking whether the payload contains control commands, and triggers an alarm and reports once Trojan activity is found.
[0062] The abnormal behavior detection module is used to detect abnormal behavior in single-session traffic, and generate an alarm and report it when it is identified; wherein the abnormal behavior in single-session traffic at least includes abnormal login, abnormal transmission and abnormal email.
[0063] Specifically, the module receives network data, including email headers and email bodies; identifies attack behaviors by checking whether the data contains domain name items in the blacklist; after detecting a blacklist item, an alarm will be generated and reported so that defensive measures can be taken.
[0064] In an optional embodiment of the present application, the code representation of each of the above modules is given below:
[0065] 1. Malicious file detection module
[0066] It has the ability to detect malicious files such as secret-stealing Trojans, remote control Trojans, backdoor programs, malicious documents, hacker tools, etc. transmitted through multiple network protocols, generate alarms and report them.
[0067] class MaliciousFileDetector:
[0068] def__init__(self,file_content):
[0069] self.file_content=file_content
[0070] defdetect_malicious_file(self):
[0071] #Simulation detection logic
[0072] ifb'malicious_signature'in self.file_content:
[0073] return True
[0074] return False
[0075] 2. Penetration behavior detection module
[0076] It supports the detection and discovery of network penetration behaviors such as password cracking and vulnerability exploitation, as well as system vulnerability exploitation and WEB vulnerability exploitation, and generates alarms and reports.
[0077] class PenetrationBehaviorDetector:
[0078] def__init__(self,network_traffic):
[0079] self.network_traffic=network_traffic
[0080] defdetect_penetration_behavior(self):
[0081] #Simulation detection logic
[0082] ifb'password_crack'in self.network_traffic:
[0083] return True
[0084] return False
[0085] 3. Trojan activity detection module
[0086] It supports detecting secret-stealing Trojans, remote-control Trojans, data theft and other behaviors based on communication payload detection, generating alarms and reporting them.
[0087] class TrojanActivityDetector:
[0088] def__init__(self, payload):
[0089] self.payload = payload
[0090] defdetect_trojan_activity(self):
[0091] #Simulation detection logic
[0092] ifb'control_command'in self.payload:
[0093] return True
[0094] return False
[0095] 4. Blacklist detection module
[0096] It can detect various attack activities according to the blacklist type, including but not limited to domain names, IP addresses, URLs, accounts, etc., generate alarms and report them; it supports detecting attack behaviors that contain IP, domain names, URLs, and account blacklists in email headers and email bodies, and generate alarms and report them.
[0097]
[0098] 5. Abnormal behavior detection module
[0099] It supports the detection of single-session traffic, and can detect abnormal login, abnormal transmission, abnormal email and other behaviors, generate alarms and report them.
[0100]
[0101] From the above, it can be seen that the innovative points of this application include:
[0102] Multi-protocol support: The system can detect files transmitted via HTTP, mail (SMTP, POP3, IMAP), FTP, SMB, NFS, instant messaging, network disk, etc.
[0103] Multiple threat type support: The system can detect multiple threat types such as malicious files, infiltration behaviors, Trojan activities, blacklist attacks and abnormal behaviors.
[0104] Intelligent detection algorithm: The system is equipped with an intelligent detection algorithm that can perform intelligent detection based on threat characteristics to ensure the accuracy and completeness of threat detection. Figure 2 A threat detection flow chart for this application is given.
[0105] Efficient alarm reporting: For different threat types, the system calls the corresponding alarm reporting algorithm to ensure fast and accurate reporting of threat information.
[0106] In addition, it should be noted that the objects processed by this application include HTTP data packets, SMTP data packets and FTP data packets. Figure 3 , Figure 4 , Figure 5The HTTP data packet structure diagram, SMTP data packet structure diagram and FTP data packet structure diagram are given respectively.
[0107] HTTP data packet structure: HTTP data packet structure includes two forms: request and response, which are composed of message header and message body, separated by blank lines. The request message contains the request line, request header field, common header field, entity header field, and other possible header fields (such as Cookie). The response message contains the status line, response header field, common header field, entity header field, and other header fields (such as Set-Cookie). The HTTP protocol is stateless, which means that there is no direct connection between each request and response, and the Cookie mechanism is introduced to maintain the state.
[0108] SMTP packet structure: SMTP (Simple Mail Transfer Protocol) packet structure involves the interaction between the client and the server, including the process of connection, authentication, mail transmission and disconnection. SMTP interaction starts with the client successfully connecting to the server, followed by user authentication, then sending the mail content, and ending the SMTP session with the exit command. In the SMTP protocol, status words such as 220 indicate successful connection, 250 indicate successful handshake with the server, 334 indicate acceptance of user login, and 235 indicate successful user login.
[0109] FTP packet structure: The FTP (File Transfer Protocol) packet structure involves two parts: the control connection and the data connection. The control connection is responsible for transmitting control commands and response codes, while the data connection is responsible for transmitting the actual file data. The FTP protocol supports a variety of file types and structures, as well as different transfer modes. FTP uses port 21 as the default port for the control connection, while the port for the data connection is dynamically selected as needed. The FTP protocol allows users to send commands (such as USER, PASS, PASV, PORT, etc.) through the control connection to control file uploads, downloads, and directory listings.
[0110] In an optional embodiment of the present application, the system further includes:
[0111] Threat Detection Module,Threat detection algorithms are based on analyzing packets in network traffic to identify and classify network threats.,These algorithms usually rely on feature extraction of packet content, such as specific signatures,,behavior patterns, or abnormal traffic.,Deep learning techniques, especially deep neural networks, have been used to improve the,accuracy and efficiency of threat detection. These models are able to learn multi-level hidden,representations from complex data to identify potential malicious activities.
[0112] The threat detection algorithm identifies threats based on their signatures. Here is a simplified Python code example:
[0113]
[0114] Alarm reporting module, the implementation of the alarm reporting algorithm involves classifying the detected threats and triggering the corresponding reporting mechanism according to the threat type. This may include assessing the severity of the threat and deciding whether relevant personnel or systems need to be notified immediately. The alarm reporting algorithm needs to consider the accuracy and timeliness of the alarm to ensure effective incident response. Alarm convergence technology is also used to reduce false alarms and duplicate alarms and improve the availability of the alarm system.
[0115] The following is a simplified code snippet of an alarm reporting algorithm implementation, which is used to report different types of threats:
[0116]
[0117]
[0118] The embodiment of the present application also provides a method for implementing a comprehensive detection system for defending against multiple network threats.
[0119] include:
[0120] Identify and detect malicious files transmitted through various network protocols, and generate alarms and report the malicious files found; malicious files include at least secret-stealing Trojans, remote control Trojans, backdoor programs, malicious documents, and hacker tools;
[0121] Identify network penetration, system vulnerability exploitation, and WEB vulnerability exploitation, and generate and report alarms when they are identified;
[0122] By analyzing the communication payload monitoring and detecting Trojan activities, an alarm is generated and reported when the existence is identified; Trojan activities at least include the control behavior and data theft behavior of secret stealing Trojans and remote control Trojans;
[0123] Detect various attack activities based on preset blacklist types, including domain names, IP addresses, URLs, and accounts; also supports detecting blacklist items contained in email headers and bodies to identify and report attack behaviors;
[0124] Detect abnormal behaviors in single-session traffic, and generate alarms and report them when they are identified; wherein, abnormal behaviors in single-session traffic include at least abnormal logins, abnormal transmissions, and abnormal emails.
[0125] The comprehensive detection method for defending against multiple network threats provided in the embodiment of the present application is implemented in the above-mentioned comprehensive detection system for defending against multiple network threats. For the specific definition of the comprehensive detection method for defending against multiple network threats, please refer to the definition of the comprehensive detection system for defending against multiple network threats above, which will not be repeated here. The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0126] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.
Claims
1. A comprehensive detection system for defending against multiple network threats, characterized in that: The system comprises: Malicious file detection module, used to identify and detect malicious files transmitted through various network protocols, and generate alarms and report the malicious files found; malicious files include at least secret stealing Trojans, remote control Trojans, backdoor programs, malicious documents and hacker tools; The penetration behavior detection module is used to identify network penetration behaviors, system vulnerability exploitation behaviors, and WEB vulnerability exploitation behaviors, and generate alarms and report them when they are identified; A Trojan activity detection module is used to monitor and detect Trojan activities by analyzing the communication load, and generate an alarm and report when the existence is identified; wherein Trojan activities at least include the control behavior and data theft behavior of secret stealing Trojans and remote control Trojans; Blacklist detection module, used to detect various attack activities according to preset blacklist types, including domain names, IP addresses, URLs and accounts; it also supports detecting blacklist items contained in email headers and bodies to identify and report attack behaviors; The abnormal behavior detection module is used to detect abnormal behavior in single-session traffic, and generate an alarm and report it when it is identified; wherein the abnormal behavior in single-session traffic at least includes abnormal login, abnormal transmission and abnormal email.
2. The comprehensive detection system for defending against multiple network threats according to claim 1, characterized in that: The malicious file detection module specifically includes: Receive the file content transmitted in the network traffic and identify malicious files by checking whether the file content contains specific malicious signatures. Once a malicious file is detected, the system will trigger an alarm mechanism and report the relevant information to the security management center.
3. The comprehensive detection system for defending against multiple network threats according to claim 1, characterized in that: The penetration behavior detection module specifically includes: Analyze network traffic, specifically network data packets containing attack behaviors, and identify infiltration behaviors by detecting whether password cracking keywords are contained in network traffic. Once infiltration behaviors are detected, an alarm is generated and reported.
4. The comprehensive detection system for defending against multiple network threats according to claim 1, characterized in that: The Trojan activity detection module specifically includes: Receive and analyze network communication payloads, and identify Trojan activities by checking whether the payload contains control commands. Once Trojan activities are found, an alarm will be triggered and reported.
5. The comprehensive detection system for defending against multiple network threats according to claim 1, characterized in that: The blacklist detection module specifically includes: Receive network data, including email headers and email bodies; Identify attack behaviors by checking whether the data contains domain names in the blacklist; When a blacklist item is detected, an alarm will be generated and reported so that defensive measures can be taken.
6. The comprehensive detection system for defending against multiple network threats according to claim 1, characterized in that: The abnormal behavior detection module specifically includes: Analyze the network traffic of a single session and check whether the session traffic contains any signs of abnormal login behavior. Once abnormal behavior is found, an alarm will be triggered and reported.
7. The comprehensive detection system for defending against multiple network threats according to claim 1, characterized in that: The system also includes: a threat detection module, which is used to analyze data packets in network traffic to identify and classify network threats. The threat detection algorithm extracts features from the content of the data packets, including specific signatures, behavior patterns or abnormal traffic.
8. The comprehensive detection system for defending against multiple network threats according to claim 1, characterized in that: The system also includes: an alarm reporting module, which is used to classify the detected threats and trigger the corresponding reporting mechanism according to the threat type; including evaluating the severity of the threat and deciding whether it is necessary to notify relevant personnel or systems immediately.
9. The comprehensive detection system for defending against multiple network threats according to claim 1, characterized in that: The data packets detected by the system include HTTP data packets, SMTP data packets, and FTP data packets.
10. A method for implementing a comprehensive detection system for defending against multiple network threats based on any one of claims 1 to 9, characterized in that: The implementation method includes: Identify and detect malicious files transmitted through various network protocols, and generate alarms and report the malicious files found; malicious files include at least secret-stealing Trojans, remote control Trojans, backdoor programs, malicious documents, and hacker tools; Identify network penetration, system vulnerability exploitation, and WEB vulnerability exploitation, and generate and report alarms when identified; By analyzing the communication payload monitoring, Trojan horse activities are detected, and when they are identified, an alarm is generated and reported; Trojan horse activities include at least the control behavior and data theft behavior of secret stealing Trojan horses and remote control Trojan horses; Detect various attack activities based on preset blacklist types, including domain names, IP addresses, URLs, and accounts; also supports detecting blacklist items contained in email headers and bodies to identify and report attack behaviors; Detect abnormal behavior in single-session traffic, and generate an alarm and report it when it is identified; wherein the abnormal behavior in single-session traffic includes at least abnormal login, abnormal transmission and abnormal email.