Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of security system. More particularly, the present disclosure relates to a method for adaptive event threat score estimation for Perimeter Surveillance System (PSS).

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Security agencies use the PSS (Perimeter Surveillance System) Software to provide command and control solutions for protecting the perimeter of the desired perimeters. The PSS (Perimeter Surveillance System) comprises multiple layers of sensors like Radar, PTZ cameras, Fixed Cameras, UVDS, ESPF, etc. The perimeter is categorised into different zone based on risk factors like standard and risk zones. Each sensor can detect events/alarms/alerts, which are forwarded to C2 software. Some of these alerts/alarms may be false or unwanted.
[0004] A fair amount of work focus on the problem of Physical Intrusion detection. Some work handles individual sensors, and some focus on multi-sensor multi-layered systems. One of the prior art suggested a system to improve border security electronically with automation and decrease the workload of the defence forces that incessantly monitor and secure the border 24×7. This project will not entirely remove the duty of soldiers who will share maximum responsibility and will lessen human efforts on the border. Another prior art evaluate the use of Frequency Modulated Continuous Wave (FMCW) Radar for perimeter detection to be used for border protection. The necessity to use many simple perimeter Radars along the border and send information to the base station is also established. A prior art proposes the motion detection method from a video feed using background image subtraction. Their method of improved detection of humans by implementing the usage of dynamic thresholding and contour area detection makes the system reliable. Further, another prior art brings the concept of using a Laser fence to secure a perimeter and send alert signals to the control station when occlusion of the Laser beams occurs at the fence.
[0005] An existing prior art utilises High range Pulse Doppler RADARs that can help in long-range sections. RADARs can detect tara targets in a 360° direction. The RADAR is very beneficial to innovative border management. It gets the expected result to see objects towards an observer at a velocity of 200m/s from a 30 km distance.
[0006] Although many security systems and surveillance systems exist, only a few have been implemented and used for monitoring international borders and the safekeeping of nearby military establishments, such as the Border Security Forces and army posts, like BOLD QIT]. A prior art focuses on designing and implementing a novel security domain surveillance system framework that incorporates multimodal information sources to assist the event detection task from video and social media sources. Physical security is often overlooked when it comes to information security. A prior art presents PSO, an ontological framework, and a methodology for improving physical security and insider threat detection. PSO can facilitate forensic data analysis and proactively mitigate insider threats by leveraging rule-based anomaly detection. Apply Physical Intrusion detection on a different domain and propose a data centre system that stores information and quickly manages data access.
[0007] Challenge and Complexity increase with multi-sensor, multi-layer scenarios. The currently used system, like BOLD QIT, uses a Fiber optic network-based visual surveillance system which also requires manual monitoring. Radar systems for perimeter security applications are used in simple short-range scenarios for motion and presence detection at the guarded perimeter areas. Traditional radar systems are generally not used for long-range terrestrial human presence and approach detection. Many border surveillance systems use visual surveillance through cameras, but most rely on human monitoring or backend processing. Existing technology make use of technologies in a standalone manner, such as Laser Tripwires, FMCW radar systems for short perimeter detection, and visual surveillance systems. A prior art disclose a multi-point flagging system is required to give a real heads-up about any possible intruder around a guarded area, where the presence and approach of the intruder are detected at multiple distance points using the various systems. This is intended to be implemented in a 3 Stage mechanism. First, A dual Doppler Radar System is to detect the possible presence of an intruder at a far distance and predict the speed and time of intrusion. Second, A camera-based surveillance and motion detection system with distance approximation capability. Third, the Dynamic Laser Tripwire to confirm a perimeter breach and raise a final flag while triggering the master alarm and all consequential programmed functions.
[0008] Some patent also considers task of perimeter security. A method for automatically spotting trespassers in a prohibited location. Using visible and infrared digital camera photos, the approach offers an operator with the intruder's location in real time. Assistance is given to the operator in detecting, identifying, and evaluating the possible threat of an intruder. This work does not consider prioritizing the possible threat.
[0009] An existing technology defines process for identifying things that are crossing or attempting to cross a border as a danger. The method involves the following steps: segmenting the border into border elements of uniform terrain features, infrastructure, and weather conditions; gathering data on incidents occurring along a given border element; identifying a threat potential for said border element; identifying a protection factor; and identifying a threat against the border element based on the threat potential and protection factor. This work is not considering the multi sensor scenario as well as prioritization of threat base on available information.
[0010] Thus, there is a need to prioritise events to get the proper resource for event handling, like user attention, no of the screen, and QRT to handle the event. Adaptiveness is required to incorporate in the prioritisation of events for a highly dynamic environment and conditions.
[0011] Therefore, there is a need in the art to provide a method for adaptive event threat score estimation for Perimeter Surveillance System (PSS).
OBJECTS OF THE PRESENT DISCLOSURE
[0012] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0013] It is an object of the present disclosure monitors the perimeter to find intrusion activities.
[0014] It is an object of the present disclosure reduces the number of less critical alerts of threats presented to security operators.
[0015] It is an object of the present disclosure provides threat scores of different events, and operators need to focus on only high-threat score events.
[0016] It is an object of the present disclosure is to enhance the security of a physical location by providing improved detection, constant monitoring, cost-effective surveillance, deterrence, and integration with other security systems.
[0017] It is an object of the present disclosure is to provide a cost-effective solution, where the PSS can be set up to cover large areas and can be automated to trigger alerts and responses without the need for human intervention.

SUMMARY
[0018] The present disclosure relates to the field of security system. More particularly, the present disclosure relates to a method for adaptive event threat score estimation for Perimeter Surveillance System (PSS).
[0019] An aspect of the present disclosure pertains to a method for adaptive event threat score estimation for a Perimeter Surveillance System (PSS). The method comprises receiving one or more parameters pertaining to information from at least one user associated with at least one computing device. Further, computing, by the at least one computing device, an adaptive event threat score based on one or more parameters, to minimize the number of fewer critical alerts of threats.
[0020] In an aspect, the one or more parameters comprises at least one of a surveillance zone profile, a sensors profile, a intrusion behavior patterns.
[0021] In an aspect, a user assessment of SZ Threat Score comprises loading at least one of a past user assessments, a past system-generated Surveillance Zone (SZ) Threat Score, and a past events, and current anticipated events. Further, generating a log of user assessment of SZ threat score; and checking at least one surveillance zone to create a record of user assessment.
[0022] In an aspect, a surveillance zone profile comprises loading at least one of a SZ past event data, a surveillance zone sensor data, a terrain data, a location data, and a proximity with VAVP data. Further, processing the surveillance zone along with all the above data and parameters and generate SZ Threat Score.
[0023] In an aspect, a multi-sensors profile subsystem comprises loading one or more data, wherein the one or more data comprises at least one of a sensor deployment data, type, accuracy, and reliability. The selected surveillance zone processing of the one or more data along with sensor characteristics and behaviour parameters, multi-sensor profile threat score is generated.
[0024] In an aspect, an intrusion characteristics and behavior patterns score subsystem comprises: loading one or more elements, wherein one or more elements comprises at least one of a event type, a event location and time, a event life span, a distance from boundary/perimeter, a direction and speed of movement. Further, processing the event based on the input data and the one or more elements and generate the ICB score.
[0025] In an aspect, the aggregation subsystem comprises loading one or more factors, wherein the one or more factors comprises at least one of the user-assessed SZ Threat score, the System Generated SZ Threat score, a multi-sensors profile score, and a ICB Score. Further, processing the event as per the existing system threat score, aggregate and generate an adaptive event threat score.
[0026] In an aspect, a explainable outcome subsystem comprises displaying relationship between the adaptive event threat score and one or more factors. The one or more factors comprises at least one of the user-assessed SZ threat score, the system generated threat score, the multi-sensors profile score, and a intrusion raw score.
[0027] In an aspect, the Perimeter Surveillance System (PSS) comprises at least one of a Radar, Pan-Tilt-Zoom (PTZ) cameras, and a fixed camera.
BRIEF DESCRIPTION OF THE DRAWINGS
[0028] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0029] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:
[0030] FIG. 1 illustrates schematic diagram of A-ETE Method Components, in accordance with an exemplary embodiment of the present disclosure.
[0031] FIG. 2A illustrates the schematic diagram of proposed threat score calculation, in accordance with an exemplary embodiment of the present disclosure.
[0032] FIG. 2B illustrates the schematic diagram of proposed threat score calculation, in accordance with an exemplary embodiment of the present disclosure.
[0033] FIG. 3 illustrates the proposed method for an user assessment of SZ threat score, in accordance with an exemplary embodiment of the present disclosure.
[0034] FIG. 4 illustrates the proposed method for a surveillance zone profile, in accordance with an exemplary embodiment of the present disclosure.
[0035] FIG. 5 illustrates the proposed method for a multi-sensors profile, in accordance with an exemplary embodiment of the present disclosure.
[0036] FIG. 6 illustrates the proposed method for an intrusion characteristics and behavior patterns score, in accordance with an exemplary embodiment of the present disclosure.
[0037] FIG. 7 illustrates the proposed method for an aggregation, in accordance with an exemplary embodiment of the present disclosure.
[0038] FIG. 8 illustrates the proposed method for an explainable outcome, in accordance with an exemplary embodiment of the present disclosure.
DETAILED DESCRIPTION
[0039] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0040] Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0041] In some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0042] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0043] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[0044] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all groups used in the appended claims.
[0045] The present disclosure relates to the field of security system. More particularly, the present disclosure relates to a method for adaptive event threat score estimation for Perimeter Surveillance System (PSS).
[0046] An aspect of the present disclosure pertains to a method for adaptive event threat score estimation for a Perimeter Surveillance System (PSS). The method comprises receiving one or more parameters pertaining to information from at least one user associated with at least one computing device. Further, computing, by the at least one computing device, an adaptive event threat score based on one or more parameters, to minimize the number of fewer critical alerts of threats.
[0047] In an aspect, the one or more parameters comprises at least one of a surveillance zone profile, a sensors profile, a intrusion behavior patterns.
[0048] In an aspect, a user assessment of SZ Threat Score comprises loading at least one of a past user assessments, a past system-generated Surveillance Zone (SZ) Threat Score, and a past events, and current anticipated events. Further, generating a log of user assessment of SZ threat score; and checking at least one surveillance zone to create a record of user assessment.
[0049] In an aspect, a surveillance zone profile comprises loading at least one of a SZ past event data, a surveillance zone sensor data, a terrain data, a location data, and a proximity with VAVP data. Further, processing the surveillance zone along with all the above data and parameters and generate SZ Threat Score.
[0050] In an aspect, a multi-sensors profile subsystem comprises loading one or more data, wherein the one or more data comprises at least one of a sensor deployment data, type, accuracy, and reliability. The selected surveillance zone processing of the one or more data along with sensor characteristics and behaviour parameters, multi-sensor profile threat score is generated.
[0051] In an aspect, an intrusion characteristics and behavior patterns score subsystem comprises: loading one or more elements, wherein one or more elements comprises at least one of a event type, a event location and time, a event life span, a distance from boundary/perimeter, a direction and speed of movement. Further, processing the event based on the input data and the one or more elements and generate the ICB score.
[0052] In an aspect, the aggregation subsystem comprises loading one or more factors, wherein the one or more factors comprises at least one of the user-assessed SZ Threat score, the System Generated SZ Threat score, a multi-sensors profile score, and a ICB Score. Further, processing the event as per the existing system threat score, aggregate and generate an adaptive event threat score.
[0053] In an aspect, a explainable outcome subsystem comprises displaying relationship between the adaptive event threat score and one or more factors. The one or more factors comprises at least one of the user-assessed SZ threat score, the system generated threat score, the multi-sensors profile score, and a intrusion raw score.
[0054] In an aspect, the Perimeter Surveillance System (PSS) comprises at least one of a Radar, Pan-Tilt-Zoom (PTZ) cameras, and a fixed camera.
[0055] FIG. 1 illustrates schematic diagram of A-ETE Method Components, in accordance with an exemplary embodiment of the present disclosure.
[0056] In an embodiment, the proposed invention aims at at reducing the number of less critical alerts of threats presented to security operators, by using Adaptive Event Threat Score Estimation Method for Perimeter Surveillance System (PSS)) also known as A-ETA. The present invention provides threat scores of different events, and operators need to focus on only high-threat score events. The present invention includes a combination of information from the user, surveillance zone profile, sensors profile, and intrusion behaviour patterns to calculate the adaptive threat score.
[0057] As illustrated in FIG. 1, the main components of an A-ETE system 100 can include a network 102, one or more sensors 104, a database 106, a user 110 associated with a computing device 108, and a server 112. A network communicates among all other components, the user 110, the server 112, a database 108. Database106 is a repository for information gathered by sensors, users, and servers. Client Console. A console is a program that provides an interface for the A-ETE system’s users and administrators. Console software is typically installed onto standard desktop or laptop computers. Some consoles are used only for A-ETE administration, such as configuring sensors or users and applying software updates, while others are used strictly for monitoring and analysis. Some A-ETE consoles provide both administration and monitoring capabilities. The user 110 can monitor and analyze the activities and provide feedback to the system 100. They use the experience and expertise to enhance system performance. The server 112 can receives information from the one or more sensors 104 or users 110 and processes and manages the activities. Some server components perform on the received data. All the algorithmic work is performed on servers. In larger A-ETE deployments, there are often multiple servers.
[0058] FIG. 2A illustrates the schematic diagram of proposed threat score calculation, in accordance with an exemplary embodiment of the present disclosure.
[0059] In an embodiment, one or more subsystems of the A-ETE for a threat score calculation 202 can include a user assessment 204, an intrusion characteristics and behavior patterns 206, a surveillance zone profile 208, and a multi-sensors profile 210, an intrusion behaviour patterns subsystems, an aggregation subsystems, an explainable outcome subsystems.
[0060] FIG. 2B illustrates the schematic diagram of proposed threat score calculation, in accordance with an exemplary embodiment of the present disclosure.
[0061] As illustrated in FIG. 2, the threat score 202 can include a suggestion 212, an explanation 214, an intruder characteristics 216, and a resource allocation 218.
[0062] FIG. 3 illustrates the proposed method for an user assessment of SZ threat score, in accordance with an exemplary embodiment of the present disclosure.
[0063] In an embodiment, an input can include past user assessment, past system generated SZ threat score, past events, anticipated event, location of the surveillance zone. An output can include a user-assessed SZ threat score. Further, the processing of the subsystem is implemented by the system 100 is iniatlized at 302, loading past user assessments 304, past system-generated Surveillance Zone (SZ) Threat Score 306, past events 308, and current anticipated events 310, Load the Surveillance zone data 312 and select the surveillance zone 314 by user/system and the system shall generate a log of user assessment of SZ threat score 316 and check 318 for further more surveillance zone if it is queue then the system process and creates a record of user assessment till the surveillance zone count becomes NIL.
[0064] FIG. 4 illustrates the proposed method for a surveillance zone profile, in accordance with an exemplary embodiment of the present disclosure.
[0065] In an embodiment, an input can include a surveillance zone past event data surveillance zone sensor data, a surveillance zone terrain data surveillance zone location data, and a surveillance zone proximity with VAVP data. The output provided pertains to a system-generated SZ Threat score 400. The system 100 shall load SZ past event data 404 & Surveillance zone sensor data 406, Terrain data 408, location data 410, and proximity with VAVP Data 412. The system shall process the surveillance zone 414 along with all the above data and parameters and generate SZ Threat Score 416. The process continues till the surveillance zone present in the queue for processing becomes NIL.
[0066] FIG. 5 illustrates the proposed method for a multi-sensors profile, in accordance with an exemplary embodiment of the present disclosure.
[0067] In an embodiment, an input can include a Sensor Deployment Data, a Sensor Type Data, a Sensor Accuracy Data, a Sensor Reliability Data. The output relates to a multi-Sensors Profile Score 500. The processing can include the system 100 which loads sensor deployment data 504, type 506, accuracy 508, and reliability 510. For the selected surveillance zone 512 processing of the above data along with sensor characteristics & behaviour parameters, multi-sensor profile Threat score is generated 514. The process repeats till the surveillance zone present in the queue for processing becomes NIL.
[0068] FIG. 6 illustrates the proposed method for an intrusion characteristics and behavior patterns score, in accordance with an exemplary embodiment of the present disclosure.
[0069] In an embodiment, an input can include an event Type 604/ Event Location and Event Time 606, Life Span 608, Distance from Boundary 610, load Type, Direction, and Speed of Movement 612, [Type of Movement: Roaming /Passing]. The output of the following subsystem shall produce the following output Intrusion Raw Score Event Threat Score 600. The processing of the subsystem includes loading the Event Type 604, Event Location and Time 606, Event life span 608, Distance from Boundary/Perimeter 610, Direction & Speed of Movement 612, and the system shall process the event based on the input data and parameters and generate the ICB score, and the process of generating ICB score continues till the events present in the queue becomes NIL.
[0070] FIG. 7 illustrates the proposed method for an aggregation, in accordance with an exemplary embodiment of the present disclosure.
[0071] In an embodiment, aaggregating individual factors can assess the combined effect of multiple factors. Decision-making is one of the most widely used management processes in dealing with real-world problems, typically characterised by complex and challenging tasks. Multiple criteria decisions making (MCDM) has been one of the fastest-growing knowledge areas in decision sciences and has been used extensively in many disciplines. MCDM deals with the problem of helping the decision-maker to choose the best alternative according to several criteria.
[0072] Further, there are several fixed rules for combining decisions that do not use explicit probabilistic models to adapt to data. The inputs to these functions are subsystem decisions in the form of scores for each decision. The product and sum combination rules are significant because they approximate more sophisticated combination methods.
[0073] In an embodiment, the product rule can be derived from Bayesian theory, assuming subsystems are conditionally independent and supply unbiased, calibrated scores of the correct decisions. Mis-calibrated scores are those that over or under-exaggerate certainty, potentially placing too much or too little emphasis on a particular piece of evidence. The product rule combines independent evidence so everyone can check for a specific feature or sub-pattern in the data. The combination of these sub-patterns indicates the most probable score. The distinctive feature of the product rule is that any agent with strong beliefs (close to zero or one) can significantly affect the combined decision.
[0074] In contrast, the sum rule averages over the individual subsystem score. The combined probability is estimated by taking the mean. Therefore, sum rule decisions are less affected by a single subsystem with a robust estimation, so they rely less on all subsystems submitting trustworthy score values. Therefore, sum rule decisions are less affected by a single agent with a strong belief, so they rely less on all agents submitting reliable score values. However, the sum rule typically does not allow such sharp decision boundaries as the product rule, as it mixes the individual models and thus decreases their precision.
[0075] In an embodiment, according to the sum rule to aggregate information from different subsystems. The classical weighted aggregation is usually known by the weighted average (WA) or simple additive weighting method. Input: Following input shall be used by this subsystem User-Assessed SZ Threat Score /System-Generated SZ Threat Score/Multi-Sensors Profile Score ICB Score Output: This subsystem shall produce the following output.
[0076] Further, Adaptive Event Threat Score Processing of the subsystem includes loading the user-assessed SZ Threat score 704, System Generated SZ Threat score 706, multi-sensors profile score 708, and ICB Score 710. The system 100 shall process the event as per the existing system threat score, aggregate and generate an adaptive event threat score. This process of creating an adaptive threat score continues till no more event is present in the queue for processing.
[0077] FIG. 8 illustrates the proposed method for an explainable outcome, in accordance with an exemplary embodiment of the present disclosure.
[0078] In an embodiment, the A-ETE system shall generate explanations to improve user trust. The input can include Historical data of User assessed SZ Threat score, Historical data of System generated SZ Threat score, Historical data of Multi-Sensors Profile Score, Historical data of intruder characteristics and behaviour Score, and Historical data of Adaptive Event Threat Score. The output can include the chart which displays the relationship between Adaptive Event Threat Score and other factors like user-assessed SZ threat score, system generated SZ Threat score, multi-sensors profile score, and intrusion raw score. further, system 100 shall load user assessed SZ threat score 804 historical data 806, multi-sensors profile score 808, historical data 810, load intruder characteristics and behaviour score historical data, and adaptive event threat score historical data
[0079] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGS OF THE INVENTION
[0080] The proposed invention monitors the perimeter to find intrusion activities.
[0081] The proposed invention reduces the number of less critical alerts of threats presented to security operators.
[0082] The proposed invention provides threat scores of different events, and operators need to focus on only high-threat score events.
[0083] The proposed invention enhances the security of a physical location by providing improved detection, constant monitoring, cost-effective surveillance, deterrence, and integration with other security systems. , Claims:1. An method for adaptive event threat score estimation for a Perimeter Surveillance System (PSS) (100), the method comprises:
receiving one or more parameters pertaining to information from at least one user (110) associated with at least one computing device (108); and
computing, by the at least one computing device (108), an adaptive event threat score based on one or more parameters, to minimize the number of fewer critical alerts of threats.

2. The method as claimed as claim 1, wherein the one or more parameters comprises at least one of a surveillance zone profile, a sensors profile, a intrusion behavior patterns.

3. The method as claimed as claim 1, wherein a user assessment of SZ threat score comprises:
loading, by the system (100), at least one of a past user assessments, a past system-generated Surveillance Zone (SZ) Threat Score, and a past events, and current anticipated events;
generating, by the system (100), a log of user assessment of SZ threat score; and
checking, by the system (100), at least one surveillance zone to create a record of user assessment.

4. The method as claimed as claim 1, wherein a surveillance zone profile comprises:
loading, by the system (100), at least one of a SZ past event data, a surveillance zone sensor data, a terrain data, a location data, and a proximity with VAVP data; and
processing, by the system (100), the surveillance zone along with all the above data and parameters and generate SZ Threat Score.

5. The method as claimed as claim 1, wherein a multi-sensors profile subsystem comprises:
loading, by the system (100), one or more data, wherein the one or more data comprises at least one of a sensor deployment data, type, accuracy, and reliability, and the selected surveillance zone processing of the one or more data along with sensor characteristics and behaviour parameters, multi-sensor profile threat score is generated.

6. The method as claimed in claim 1, wherein an intrusion characteristics and behavior patterns score subsystem comprises:
loading, by the system (100), one or more elements, wherein one or more elements comprises at least one of a event type, a event location and time, a event life span, a distance from boundary/perimeter, a direction and speed of movement; and
processing the event based on the input data and the one or more elements and generate the ICB score.

7. The method as claimed in claim 1, wherein the aggregation subsystem comprises:
loading, by the system (100), one or more factors, wherein the one or more factors comprises comprises at least one of the user-assessed SZ Threat score, the System Generated SZ Threat score, a multi-sensors profile score, and a ICB Score; and
processing the event as per the existing system threat score, aggregate and generate an adaptive event threat score.

8. The method as claimed in claim 1, wherein a explainable outcome subsystem comprises:
displaying, by the system (100), relationship between the adaptive event threat score and one or more factors, wherein the one or more factors comprises at least one of the user-assessed SZ threat score, the system generated threat score, the multi-sensors profile score, and a intrusion raw score.

9. The method as claimed in claim 1, wherein the Perimeter Surveillance System (PSS) (100) comprises at least one of a Radar, Pan-Tilt-Zoom (PTZ) cameras, and a fixed camera.