Abnormality detection device, abnormality detection method, and abnormality detection program

Abstract

The generation unit (15b) generates a group for each predetermined communication feature amount, with an alarm indicating that an abnormal communication different from the normal communication pattern has been detected. The specifying unit (15c) specifies the cause of notification of the alarm for each of the generated alarm groups.

Description

Technical Field

[0001] The present invention relates to an anomaly detection device, an anomaly detection method, and an anomaly detection program. Background Art

[0002] Conventionally, anomaly detection techniques for detecting known threats have been known. For example, a method of grouping alerts by pattern matching for known threats is known (see Non-Patent Document 1). In addition, the following method is known: By inputting the source IP address, the destination IP address, the occurrence time, a positive integer ID representing the type of illegality, and a five-stage grade representing the degree of danger, network illegal intrusion monitoring is visualized (see Non-Patent Document 2). In addition, a false detection reduction method is known in which the source IP address, the occurrence time, and the signature are used as inputs, and the false detection is reduced by focusing on the occurrence amount of security alerts by signature in illegal intrusion detection (see Non-Patent Document 3).

[0003] Prior Art Documents

[0004] Non-Patent Documents

[0005] Non-Patent Document 1: "Nozomi Networks Gurdian", [online], NOZOMI NETWORKS, [searched on May 27, 2022], Internet <URL: https: / / www.nozominetworks.com / products / guardian / >

[0006] Non-Patent Document 2: Ito, Takakura, and two others, "A Visualization Method for Network Illegal Intrusion Monitoring", [online], Kyoto University, [searched on May 27, 2022], Internet <URL: http: / / itolab.is.ocha.ac.jp / ~itot / paper / ItotRDCPJ12.pdf>

[0007] Non-Patent Document 3: Iwasaki, Kakuta, and four others, "A False Detection Reduction Method Focusing on the Occurrence Amount of Security Alerts by Signature in Illegal Intrusion Detection", [online], October 2018, Computer Security Symposium 2018, [searched on May 27, 2022], Internet <URL: https: / / ipsj.ixsq.nii.ac.jp / ej / ?action=repository_uri&item_id=192157&file_id=1&file_no=1> Summary of the Invention

[0008] Problems to be solved by the invention

[0009] However, according to the prior art, in an abnormal type anomaly detection system, a real alarm may be missed. That is, in the prior art, since it relies on a pre-defined attack condition or attack result, it cannot be applied to an abnormal type anomaly detection system that detects unknown threats by detecting abnormal communications that deviate from a normal communication pattern.

[0010] Furthermore, even for an abnormality-type anomaly detection system, if there is insufficient learning data or a change in the normal communication pattern, the number of overdetections will increase, a large number of alarms that must be confirmed during monitoring will be generated, and there is a possibility that real alarms will be missed.

[0011] The present invention has been made in view of the above-mentioned circumstances, and an object of the present invention is to avoid missing a real alarm in an abnormality detection system of an abnormality type.

[0012] Means for solving problems

[0013] In order to solve the above-mentioned problems and achieve the purpose, the abnormality detection device of the present invention is characterized in that it has: a generating unit, which generates a group according to each specified communication characteristic quantity based on the following alarms, wherein the alarm indicates a situation in which abnormal communication different from the normal communication pattern is detected; and a determining unit, which determines the cause of the notification alarm for each generated alarm group.

[0014] Effects of the Invention

[0015] According to the present invention, in an abnormality type anomaly detection system, it is possible to avoid missing a real alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram illustrating a schematic configuration of the abnormality detection device according to the present embodiment.

[0017] Figure 2 This is a diagram for explaining the processing of the generating unit and the determining unit.

[0018] Figure 3 This is a diagram for explaining the processing of the determination unit.

[0019] Figure 4 This is a diagram for explaining the processing of the determination unit.

[0020] Figure 5 This is a diagram for explaining the processing of the determination unit.

[0021] Figure 6 This is a diagram for explaining the processing of the determination unit.

[0022] Figure 7This is a diagram for explaining the processing of the determination unit.

[0023] Figure 8 It is a diagram showing an example of a screen display of an abnormality detection processing result.

[0024] Fig. 9 is a flowchart showing the abnormality detection processing steps.

[0025] Fig.10 This is a diagram showing an example of a computer that executes an abnormality detection program. DETAILED DESCRIPTION

[0026] Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. In addition, the present invention is not limited to this embodiment. In the description of the accompanying drawings, the same parts are marked with the same symbols.

[0027] [Structure of abnormality detection device]

[0028] Figure 1 Schematic diagram showing the schematic structure of the abnormality detection device of this embodiment. Figure 1 As illustrated, the abnormality detection device 10 of the present embodiment is realized by a general-purpose computer such as a personal computer, and includes an input unit 11 , an output unit 12 , a communication control unit 13 , a storage unit 14 , and a control unit 15 .

[0029] The input unit 11 is implemented using input devices such as a keyboard and a mouse, and inputs various instruction information such as starting a process to the control unit 15 in response to an input operation by an operator. The output unit 12 is implemented by a display device such as a liquid crystal display, a printing device such as a printer, etc. For example, the output unit 12 displays the result of the abnormality detection process described later.

[0030] The communication control unit 13 is implemented by a NIC (Network Interface Card) or the like, and controls the communication between the external device and the control unit 15 via an electrical communication line such as a LAN (Local Area Network) or the Internet. For example, the communication control unit 13 controls the communication between the control unit 15 and a management device that manages various related information.

[0031] The storage unit 14 is implemented by a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 14 stores in advance a processing program for operating the abnormality detection device 10, data used in the execution of the processing program, etc., or temporarily stores them each time the processing is performed. In addition, the storage unit 14 may also be a structure that communicates with the control unit 15 via the communication control unit 13.

[0032] The control unit 15 is implemented using a CPU (Central Processing Unit), an NP (Network Processor), an FPGA (Field Programmable Gate Array), etc., and executes a processing program stored in a memory. Figure 1 As shown, the control unit 15 functions as an acquisition unit 15a, a generation unit 15b, a specification unit 15c, and a determination unit 15d, and executes an abnormality detection process.

[0033] In addition, these functional units may be installed in different hardware, or part of them may be installed in different hardware. For example, the acquisition unit 15a may be installed in hardware different from other functional units. In addition, the control unit 15 may also have other functional units. For example, the control unit 15 may also be built into an abnormality detection system of an abnormal type.

[0034] The acquisition unit 15a acquires an alarm indicating that abnormal communication different from the normal communication pattern is detected. Specifically, the acquisition unit 15a acquires an alarm indicating abnormal communication output from the abnormal type abnormality detection system via the input unit 11 or the communication control unit 13 .

[0035] In addition, the acquisition unit 15a acquires the learning data used in the learning of the abnormal type anomaly detection system. For example, the acquisition unit 15a acquires the learning data used in the learning of the detection model of the abnormal type anomaly detection system via the input unit 11, or acquires the learning data used in the learning of the detection model of the abnormal type anomaly detection system from the management device that manages the learning data via the communication control unit 13. The acquisition unit 15a may also cause the storage unit 14 to store the alarms and learning data acquired in advance. Alternatively, the acquisition unit 15a may not store the acquired alarms and learning data in the storage unit 14, but may immediately transmit them to the functional unit described below.

[0036] The generator 15b generates a group for each specified communication feature for an alarm indicating that abnormal communication different from the normal communication pattern is detected. Specifically, the generator 15b generates a group having the same communication source IP address, communication destination IP address and communication destination port number as the specified communication feature.

[0037] Here, Figure 2 This is a diagram for explaining the processing of the generating unit and the determining unit. Figure 2 The information items related to each communication are illustrated in FIG. 1 . For example, item numbers 3 to 7 are 5-tuple information. The generating unit 15b generates Figure 2 Among the information items (communication feature amounts) illustrated as examples, alarms targeting communications having the same source IP address, destination IP address, and destination port number included in the 5-tuple are grouped together.

[0038] in addition, Figure 3 to Figure 7 15c is a diagram for explaining the processing of the determination unit. The determination unit 15c determines the cause of the notification alarm for each generated alarm group.

[0039] Specifically, first, the determination unit 15c determines the cause of the notification alarm by determining whether the communication for the alarmed group has been learned in a manner that can output whether it is normal or abnormal. That is, the determination unit 15c determines whether there is learning data in which the combination of the communication source IP address, the communication destination IP address, and the communication destination port number is the same as that of the group in the learning data for learning the detection model of the abnormal type anomaly detection system. In addition, in this embodiment, the completion of learning means that the learning is completed using the normal mode.

[0040] For example, Figure 3 As shown, if any one of the communication source IP address, the communication destination IP address, and the communication destination port number is different from the learning data, it is determined that the learning is not completed. In the case where the learning is not completed as described above, the determination unit 15c determines that a new communication to a new communication destination has occurred as the cause of the alarm notification. In this case, the abnormality detection device 10 can recommend the following response to the operator: confirm the details of the communication of the group.

[0041] Next, when learning has been completed for the communication of the alarmed group in such a way that it can output whether it is normal or abnormal, the determination unit 15c determines the cause of the notification alarm by determining whether the characteristic amount of the communication of the group is similar to the learned characteristic amount. For example, the determination unit 15c focuses on Figure 2The feature quantities of item numbers 8 to 18 are integrated and clustered using a Gaussian mixture model (GMM). Then, it is determined whether the clustered category exists in the learning data.

[0042] For example, in Figure 4 In FIG. 1 , the distribution of the total number of uplink bytes is illustrated as a characteristic quantity of each communication. Then, Figure 4 As shown in the example, when the distribution of the feature quantity of the learning data is different from the distribution of the feature quantity of the group of the alarm, it is determined that the feature quantities of the two are not similar. In this way, when the feature quantity of the communication of the group is not similar to the feature quantity of the learning data that has been learned, the determination unit 15c determines that the situation where the communication status has changed although the communication destination remains unchanged is the cause of the alarm notification. In this case, the abnormality detection device 10 recommends the following response to the operator: confirm the details of the communication of the group.

[0043] Next, when the feature amount of the communication of the alarmed group is similar to the learned feature amount, the determination unit 15c determines the cause of the alarm notification by determining the degree of deviation of the value for detecting the communication of the alarmed group as abnormal communication from a predetermined threshold.

[0044] For example, in Figure 5 The example in which the threshold of the determination value of the abnormal determination is set to 40, and the determination value of the abnormal determination of the communication of the group is 100, 150, 130 is illustrated. In this case, the determination unit 15c determines that the degree of deviation is significantly deviated by more than 1.2 times, for example. In this way, when the determination value of the abnormal determination of the communication of the alarm group deviates significantly from the threshold by more than a certain multiple, it is inferred that the alarm is notified due to the insufficient maturity of the model for abnormal detection. In this case, the abnormality detection device 10 issues the following suggestion to the operator: the learning data is insufficient, and it is recommended to add the communication of the group as the learning data of the model for abnormal detection, for example.

[0045] On the other hand, Figure 6 In the example, the characteristic quantity of the learning data (distribution of the total number of bytes in the upstream) shown by the oblique line shadow is similar to the characteristic quantity of the communication of the alarm group, and the judgment value of the abnormality judgment between the two deviates from the threshold value by less than a certain multiple. In this way, when the deviation degree of the judgment value of the abnormality judgment of the communication of the alarm group from the threshold value is less than a certain multiple and is small, it is inferred that the alarm is notified because an abnormality close to normal has occurred. In this case, the abnormality detection device 10 also issues the following suggestions to the operator: insufficient learning, for example, it is recommended to add the communication of the alarm group as the learning data of the abnormality detection model.

[0046] In this way, Figure 7As shown in FIG. 1 , the determination unit 15c determines the cause of the alarm notification for each alarm group. Figure 7 Among them, group (1) is an alarm group that determines that the occurrence of communication to a new communication destination is the cause of the alarm notification. In addition, group (2) is an alarm group that determines that the situation of communication has changed although the communication destination has not changed as the cause of the alarm notification. In addition, group (3) is an alarm group that determines that the maturity of the model is insufficient because the judgment value of the abnormal judgment deviates greatly from the prescribed threshold as the cause of the alarm notification. In addition, group (4) is an alarm group that determines that the learning is insufficient because the judgment value of the abnormal judgment deviates slightly from the prescribed threshold as the cause of the alarm notification.

[0047] Then, the determination unit 15c outputs the cause of the alarm notification determined for each alarm group to the output unit 12 as the result of the abnormality detection process. Figure 8 FIG. 2 is a diagram showing an example of a screen display of an abnormality detection processing result. Figure 8 As shown, the determination unit 15c outputs a similarity cause name indicating the cause of the determined alarm notification for each group determined by the communication source IP address, the communication destination IP address, and the communication destination port number. In addition, for the above-mentioned groups (3) and (4), the number of pieces of learning data that have been learned is output.

[0048] exist Figure 8 In the example shown, "Occurrence of new communication" of cause classification (1) corresponds to the alarm group of the above group (1). Also, "Change in communication status" of cause classification (2) corresponds to the alarm group of the above group (2). Also, "Insufficient model maturity" of cause classification (3) corresponds to the alarm group of the above group (3). Also, "Abnormality close to normal data" of cause classification (4) corresponds to the alarm group of the above group (4).

[0049] Return to Figure 1 The determination unit 15d determines whether there is periodicity related to the occurrence time of the alarm for each alarm group. For example, the determination unit 15d totals the occurrence time of the alarm every hour to determine the number of alarm occurrences. Then, the determination unit 15d determines whether the alarm group of the group has periodicity, and if so, determines the number of alarms in one cycle.

[0050] like Figure 8 As shown in FIG. 1 , the determination unit 15d adds the determined periodicity to the abnormality detection processing result and outputs it. Figure 8 In the example shown, the number of alarms, the alarm occurrence time, and the presence or absence of periodicity are added to each group.

[0051] In this way, by indicating the cause of the alarm notification for each alarm group, even when a large number of alarms occur, the possibility of overlooking the real alarms that should be confirmed can be reduced. For example, the operator can recognize that the alarm groups of cause categories (1) and (2) need to confirm the details of the group communication. In addition, the operator can recognize that the alarm groups of cause categories (3) and (4) are estimated to have insufficient learning data and need to be investigated.

[0052] [Abnormal detection processing]

[0053] Next, refer to Fig. 9 The abnormality detection process of the abnormality detection device 10 according to the present embodiment will be described. Fig. 9 is a flowchart showing the abnormality detection processing steps. Fig. 9 The flowchart starts, for example, when the user performs an operation input instructing the start.

[0054] First, the generating unit 15b generates a group for each specified communication feature, with the alarm indicating that an abnormal communication pattern different from the normal communication pattern is detected as the object, for example, a group having the same communication source IP address, communication destination IP address and communication destination port number as the specified communication feature is generated (step S1).

[0055] Next, the determination unit 15c determines the cause of the notification alarm for each alarm group generated. Specifically, first, the determination unit 15c determines whether the communication for the alarm group has been learned in a manner that can output whether it is normal or abnormal (step S2). That is, it is determined whether there is learning data in the learning data in which the combination of the communication source IP address and the communication destination IP address is the same as that of the group. In the case where the communication of the group has not been learned (step S2, no), the determination unit 15c determines that the communication with the new communication destination has occurred as the cause of the alarm notification (step S3), and the processing enters step S9.

[0056] On the other hand, when the communication of the alarmed group has been learned in a manner that can output whether it is normal or abnormal (step S2, yes), the determination unit 15c determines whether the characteristic amount of the communication of the alarmed group is similar to the characteristic amount of the learned data (step S4). When the characteristic amount of the communication of the alarmed group is not similar to the characteristic amount of the learned data (step S4, no), the determination unit 15c determines that the change in the communication status is the cause of the alarm notification (step S5), and the processing proceeds to step S9.

[0057] On the other hand, for the communication of the alarm group, when the feature quantity of the alarm communication is similar to the feature quantity learned in a manner that can output normal or abnormal (step S4, yes), the determination unit 15c determines the degree of deviation of the determination value for determining abnormal communication from the prescribed threshold value (step S6). When the degree of deviation of the determination value of the communication of the alarm group from the threshold value is greater than the prescribed value (step S6, yes), the determination unit 15c determines that the model maturity of abnormality detection is insufficient as the cause of the alarm notification (step S7), and the processing proceeds to step S9.

[0058] On the other hand, when the deviation degree of the judgment value of the communication of the alarm group from the threshold is less than the specified value (step S6, no), the determination unit 15c determines that an abnormal situation close to normal has occurred as the cause of the alarm notification (step S8), and enters the processing into step S9.

[0059] In the process of step S9 , the specifying unit 15 c outputs the cause of the alarm notification specified for each alarm group to the output unit 12 as a result of the abnormality detection process.

[0060] Furthermore, the determination unit 15d determines whether there is periodicity related to the alarm generation time for each alarm group, and adds the determined periodicity to the result of the abnormality detection process and outputs it. Thus, a series of abnormality detection processes are completed.

[0061] [Effect]

[0062] As described above, in the abnormality detection device 10 of this embodiment, the generating unit 15b generates a group for each predetermined communication feature quantity for an alarm indicating that abnormal communication different from the normal communication pattern is detected. In addition, the determining unit 15c determines the cause of the notification alarm for each generated alarm group.

[0063] Specifically, the generating unit 15 b generates a set of the same communication source IP address, communication destination IP address, and communication destination port number as predetermined communication feature quantities.

[0064] Thus, it is possible to determine whether it is necessary to confirm the details of the communication for each alarm group. In this way, the number of alarms for which the details of the communication need to be confirmed can be reduced, and the time required for confirmation can be shortened. Therefore, in the case where there are real alarms among a large number of alarms, the possibility of missing the real alarms can be avoided. In this way, in an abnormal type anomaly detection system, it is possible to avoid missing real alarms.

[0065] In addition, the determination unit 15c determines the cause of the notification alarm by determining whether the communication with respect to the alarmed group has been learned in a manner that can output whether it is normal or abnormal. For example, if the learning is not completed, it can be determined that the cause is that a communication with respect to a new communication destination has occurred. Therefore, the following coping method can be recommended to the operator: confirm the details of the communication.

[0066] In addition, when the communication of the alarmed group has been learned in a manner that can output whether it is normal or abnormal, the determination unit 15c determines whether the characteristic amount of the communication of the alarmed group is similar to the characteristic amount of the learned data, thereby determining the cause of the notification alarm. For example, when the characteristic amount of the communication of the alarmed group is not similar to the characteristic amount of the learned data, even if the communication destination does not change, it can be estimated that the cause is that the communication status has changed. Therefore, the following coping method can be recommended to the operator: confirm the details of the communication.

[0067] In addition, the determination unit 15c determines the cause of the alarm notification by determining the degree of deviation of the value used to determine the abnormal communication from the prescribed threshold value when the feature quantity of the communication of the alarm group is similar to the feature quantity that has been learned in a manner that can output whether it is normal or abnormal, with respect to the communication of the alarm group. For example, when the determination value of the communication of the alarm group deviates greatly from the prescribed threshold value, it is inferred that the maturity of the abnormality detection model is insufficient. In addition, when the determination value of the communication of the alarm group deviates slightly from the prescribed threshold value, it is inferred that the cause is an abnormality close to normal. In either case, it is possible to recommend to the operator that the communication of the alarm group be added as learning data of the abnormality detection model.

[0068] In addition, the determination unit 15d determines whether there is periodicity in the alarm generation time for each alarm group. Thus, by checking the alarm with attention to the periodicity, it is possible to more reliably avoid missing a real alarm.

[0069] [program]

[0070] It is also possible to generate a program that describes the processing performed by the anomaly detection device 10 of the above-mentioned embodiment in a language that can be executed by a computer. As an embodiment, the anomaly detection device 10 can be implemented by installing the anomaly detection program that performs the above-mentioned anomaly detection processing as a software package or online software on a desired computer. For example, by causing an information processing device to execute the above-mentioned anomaly detection program, the information processing device can function as the anomaly detection device 10. The information processing device referred to herein includes a desktop or notebook personal computer. In addition, in addition to this, the information processing device also includes mobile communication terminals such as smart phones, mobile phones, PHS (Personal Handyphone System), and tablet terminals such as PDA (Personal Digital Assistant) are also included in this category. In addition, the function of the anomaly detection device 10 can also be installed on a cloud server.

[0071] Fig.10 The computer 1000 includes, for example, a memory 1010 , a CPU 1020 , a hard disk drive interface 1030 , a disk drive interface 1040 , a serial port interface 1050 , a video adapter 1060 , and a network interface 1070 . These components are connected via a bus 1080 .

[0072] The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1031. The disk drive interface 1040 is connected to the disk drive 1041. A removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1041. For example, a mouse 1051 and a keyboard 1052 are connected to the serial port interface 1050. For example, a display 1061 is connected to the video adapter 1060.

[0073] Here, the hard disk drive 1031 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. Each information described in the above embodiment is stored in the hard disk drive 1031 and the memory 1010, for example.

[0074] The abnormality detection program is stored in the hard disk drive 1031 as a program module 1093 describing instructions executed by the computer 1000. Specifically, the program module 1093 describing each process executed by the abnormality detection device 10 described in the above embodiment is stored in the hard disk drive 1031.

[0075] Data used in information processing by the abnormality detection program is stored as program data 1094, for example, in hard disk drive 1031. Then, CPU 1020 reads program module 1093 and program data 1094 stored in hard disk drive 1031 to RAM 1012 as needed, and executes the above steps.

[0076] In addition, the program module 1093 and program data 1094 involved in the abnormality detection program are not limited to being stored in the hard disk drive 1031, and may be stored in a removable storage medium, for example, and read out by the CPU 1020 via the disk drive 1041, etc. Alternatively, the program module 1093 and program data 1094 involved in the abnormality detection program may also be stored in other computers connected via a network such as a LAN or WAN (Wide Area Network), and read out by the CPU 1020 via the network interface 1070.

[0077] The above describes the embodiment to which the invention completed by the inventor is applied, but the present invention is not limited to the description and drawings that constitute a part of the disclosure of the present invention of the embodiment. That is, other embodiments, examples, and application technologies completed by those skilled in the art based on the embodiment are all included in the scope of the present invention.

[0078] Description of Reference Numerals

[0079] 10Abnormality detection device

[0080] 11 Input section

[0081] 12 Output section

[0082] 13Communication Control Unit

[0083] 14 Storage

[0084] 15. Control Unit

[0085] 15a Acquisition

[0086] 15b Generation

[0087] 15c Determination

[0088] 15d identification unit

Claims

1. An abnormality detection device, characterized in that: have: a generating unit for generating a group for each predetermined communication characteristic amount with respect to an alarm indicating that abnormal communication having a pattern different from that of normal communication is detected; as well as A determination unit determines a reason for notifying the alarm for each of the groups of generated alarms.

2. The abnormality detection device according to claim 1, characterized in that: The generating unit generates the set having the same communication source IP address, communication destination IP address, and communication destination port number as predetermined communication feature quantities.

3. The abnormality detection device according to claim 1, characterized in that: The determination unit determines the cause by judging whether learning has been completed for the communication of the group in such a manner that it can output whether it is normal or abnormal.

4. The abnormality detection device according to claim 3, characterized in that: When learning has been completed for the communication of the group, the determination unit determines the cause by judging whether a feature amount of the communication of the group is similar to a learned feature amount.

5. The abnormality detection device according to claim 4, characterized in that: When the feature amount of the communication of the group is similar to the learned feature amount, the determination unit determines the cause by determining a degree of deviation of a value for detecting the communication of the group as abnormal communication from a predetermined threshold.

6. The abnormality detection device according to claim 1, characterized in that: The abnormality detection device further includes a determination unit that determines whether or not there is periodicity associated with the timing of occurrence of the alarm for each of the groups.

7. An anomaly detection method, which is an anomaly detection method performed by an anomaly detection device, characterized in that: include: a generating step of generating a group for each predetermined communication characteristic amount with respect to an alarm indicating that abnormal communication different in pattern from normal communication is detected; and A determining step is performed to determine a reason for notifying the alarm for each of the group of generated alarms.

8. An abnormality detection program, which is used to cause a computer to execute the following steps: a generating step of generating a group for each predetermined communication feature quantity with respect to an alarm indicating that abnormal communication different in pattern from normal communication is detected; and A determining step of determining, for each of the group of generated alarms, a reason for notifying the alarm.

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