Description:Brief Description of the Drawings

Generally, the present disclosure relates to cybersecurity. Particularly, the present disclosure relates to methods for attracting and analyzing cyber-physical system threat actors.
Background
The 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.
In the domain of cybersecurity, safeguarding cyber-physical systems (CPS) from malicious actors is paramount. These systems, which include industrial control units, smart home devices, and medical equipment, integrate computational processes with physical processes. The integration facilitates the control of physical processes through computation, making these systems critical to the infrastructure of various sectors. However, the unique nature of CPS presents significant security challenges, particularly due to their accessibility through the internet and the potential for physical harm resulting from cyber-attacks.
To protect these systems, various security measures have been employed. One such measure is the deployment of honeypots, which are decoy systems designed to simulate the operations of CPS components. These honeypots aim to attract threat actors, allowing for their actions to be monitored and analyzed without putting actual systems at risk. Despite their effectiveness in identifying potential threats, traditional honeypot environments face limitations. For instance, they may not accurately replicate the complexity of real CPS environments, leading to a lower likelihood of interaction by sophisticated threat actors. Additionally, the deployment of honeypots in a manner that convincingly integrates with the target cyber-physical network remains a challenge.
Furthermore, the process of monitoring and analyzing interactions within honeypot environments has been largely manual and time-consuming. Security analysts are required to sift through vast amounts of data to identify potentially malicious activity. This approach not only demands significant resources but also introduces delays in the detection and mitigation of threats. Consequently, the effectiveness of honeypots in real-time threat detection and response has been limited.
Moreover, the classification of threat levels and the subsequent response to detected threats have traditionally lacked automation. Decisions regarding the severity of a threat and the necessary action often depend on human judgment, introducing the possibility of error or oversight. In scenarios where immediate response is critical to prevent damage, this reliance on manual processes can be particularly problematic.
Given these challenges, there is an evident need for improved methods to attract, monitor, and analyze interactions within honeypot environments specifically designed for cyber-physical systems. Such methods should not only enhance the realism of the simulated CPS environments but also automate the process of threat analysis and response. By addressing the limitations of existing honeypot deployments and threat detection methodologies, these improved methods would significantly contribute to the security of cyber-physical systems.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for protecting cyber-physical systems from malicious actors.
Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
In an aspect, the present disclosure aims to provide a method for attracting and analyzing cyber-physical system threat actors. This method involves the creation of a honeypot environment that simulates one or more components of cyber-physical systems such as industrial control units, smart home units, and medical devices. The environment is deployed within a cyber-physical network for the purpose of monitoring interactions. Behaviors of interacting entities are analyzed to determine threat levels, which are classified into high risk and low risk. Immediate action is taken upon classification of an interaction as high risk, while monitoring continues for interactions classified as low risk. The method further includes monitoring steps such as log analysis, intrusion detection, and issuing alerts. Additionally, the honeypot environment utilizes communication protocols like Distributed Network Protocol 3 (DNP3), Open Platform Communications Unified Architecture (OPC-UA), and Modbus protocol to enhance simulation authenticity.
In another aspect, the disclosure provides a cyber-physical system threat analysis system comprising several modules. A honeypot environment module simulates critical components of cyber-physical systems. A deployment module facilitates the environment's integration within a cyber-physical network. The monitoring module is tasked with observing interactions within this environment and analyzing the behaviors of entities to ascertain threat levels. A classification module then determines these levels, categorizing them into high or low risk. Finally, a response module initiates immediate action based on the risk classification of the interactions. Enhancements to this system include the use of machine learning to analyze interaction patterns, virtualization techniques for environment deployment, simulation of failures to attract threat actors, and an anomaly detection module that employs statistical models for early threat detection. The classification module assigns quantitative risk scores to interactions, aiding in the prioritization of responses. Additionally, the monitoring module employs deep packet inspection for a more detailed analysis of interaction data, facilitating advanced threat detection.

Field of the Invention

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a method (100) for attracting and analyzing cyber-physical system threat actors, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a block diagram of a cyber-physical system threat analysis system (200), in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates the operational workflow of a deceptive honeypot environment, in accordance with the embodiments of the present disclosure.
FIG. 4 illustrates the architectural components that support the workflow, in accordance with the embodiments of the present disclosure.

Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate 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 herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
FIG. 1 illustrates a method (100) for attracting and analyzing cyber-physical system threat actors, in accordance with the embodiments of the present disclosure. The term "honeypot environment" as used throughout the present disclosure relates to a simulated network setup that mimics one or more aspects of cyber-physical systems, such as industrial control units, smart home units, and medical devices. In step (102), the creation of such an environment serves to attract threat actors by presenting what appears to be vulnerabilities within a cyber-physical system. This simulation is designed to be sufficiently convincing to entice interactions from malicious entities aiming to exploit the cyber-physical systems. Following the establishment of the honeypot environment, the deployment of this environment within a cyber-physical network is executed. Such deployment in step (104), ensures that the honeypot environment is integrated seamlessly into the network, presenting as a legitimate component of the system and thereby increasing the likelihood of engagement by threat actors. The system further comprises a monitoring step (106) that involves log analysis, intrusion detection, and issuing alerts through an alert unisty. This comprehensive monitoring of interactions within the honeypot environment allows for real-time surveillance and data collection, essential for the subsequent analysis of behavior. Upon the monitoring of interactions, an analysis of the behavior of interacting entities step (108) is conducted to ascertain the nature of the threat posed. This analysis involves evaluating the actions of the entities within the environment to determine their intentions and capabilities. Subsequently, the threat levels identified are classified into high risk and low risk in step (110). This classification facilitates the prioritization of responses to the interactions, ensuring that the most severe threats are addressed with urgency. In response to the classification of interactions as high risk, immediate action is taken in step (112). Such actions may include the isolation of the threat, notification to relevant authorities, or initiation of countermeasures to mitigate potential damage. For interactions classified as low risk, the monitoring of the environment continues in step (114). This persistent surveillance allows for the ongoing collection of data and the potential to identify evolving threats over time.
In an embodiment, the method (100) is further characterized by an enhanced monitoring step (106) that comprises log analysis, intrusion detection, and the issuance of alerts through an alert unit. The log analysis involves the systematic examination of system logs to identify patterns or anomalies indicative of potential security threats within the honeypot environment (102). This analysis is critical for understanding the nature of the interactions and identifying potential threats early. Intrusion detection refers to the process of monitoring the activities within the honeypot environment to identify malicious activities or policy violations. This component is pivotal in recognizing when the honeypot environment is being targeted by threat actors, enabling timely responses to such activities. Additionally, the issuance of alerts through an alert unit signifies the method’s capability to automatically generate notifications upon detection of suspicious activities. These alerts are essential for ensuring that relevant personnel are informed of potential threats, facilitating rapid decision-making and response. This comprehensive approach to monitoring within the honeypot environment not only enhances the detection of cyber-physical system threat actors but also significantly improves the method’s effectiveness in analyzing and mitigating potential risks.
In another embodiment, the method (100) includes the utilization of specific communication protocols within the honeypot environment (102), enhancing its simulation of cyber-physical systems. The chosen protocols, Distributed Network Protocol 3 (DNP3), Open Platform Communications Unified Architecture (OPC-UA), and Modbus protocol, are among the most commonly used in industrial control systems, smart home units, and medical devices. The inclusion of DNP3, a protocol widely employed in utilities and infrastructure for reliable data communication, ensures that the honeypot environment can simulate industrial control systems accurately. OPC-UA is recognized for its security and flexibility in data exchange across different industrial platforms, making the honeypot environment more realistic in the context of smart manufacturing and smart home applications. Modbus protocol, known for its simplicity and broad application in industrial electronic devices, allows the honeypot environment to effectively mimic various industrial and medical devices. By incorporating these specific protocols, the honeypot environment's ability to attract sophisticated threat actors is significantly enhanced, as it more closely mirrors the operational and communication patterns of real-world cyber-physical systems. This strategic choice of protocols not only improves the quality of interactions but also enriches the data collected for analysis, thereby increasing the efficacy of the method in identifying and mitigating threats to cyber-physical systems.
The cyber-physical system threat analysis system (200) is a security mechanism designed to simulate, detect, analyze, classify, and respond to threats within interconnected digital and physical infrastructures.
The term "honeypot environment module" (202) as used throughout the present disclosure relates to a component of the cyber-physical system threat analysis system (200) designed to create a simulated environment. This module is capable of mimicking the operational characteristics of an industrial control system, a smart home system, and medical devices. The simulation aims to attract cyber-physical system threat actors by presenting an environment that appears vulnerable to exploits. The honeypot environment module's ability to replicate these systems is crucial for the effective engagement of threat actors, providing a basis for further analysis of their behavior.
The term "deployment module" (204) as used throughout the present disclosure relates to the component responsible for integrating the honeypot environment created by the honeypot environment module (202) into a cyber-physical system network. This module ensures that the simulated environment is deployed in a manner that is indistinguishable from the actual network components, thereby enhancing the authenticity of the simulation. The deployment module's role is essential for the seamless operation of the honeypot environment within the target network, facilitating the attraction of threat actors.
The term "monitoring module" (206) as used throughout the present disclosure pertains to a component configured to observe and record interactions within the honeypot environment. In addition to monitoring, this module analyzes the behavior of the interacting entities to assess potential threats. Through the collection and analysis of interaction data, the monitoring module plays a pivotal role in identifying malicious activities and understanding the tactics employed by cyber-physical system threat actors.
The term "classification module" (208) as used throughout the present disclosure describes the component tasked with evaluating the threat levels associated with the interactions monitored by the monitoring module (206). This module differentiates the threats into high risk and low risk categories based on the severity of the potential impact. The classification process is vital for prioritizing responses to the detected threats, ensuring that resources are allocated efficiently to counteract the most critical threats first.
The term "response module" (210) as used throughout the present disclosure refers to the component responsible for executing actions in response to the classification outcomes determined by the classification module (208). When an interaction is classified as high risk, the response module is tasked with taking immediate measures to mitigate the threat. These actions may include isolating the affected components, alerting security personnel, or implementing countermeasures to prevent further exploitation. The response module's capability to react promptly to threats is critical for minimizing the potential damage caused by cyber-physical system threat actors.
FIG. 2 illustrates a block diagram of a cyber-physical system threat analysis system (200), in accordance with the embodiments of the present disclosure. The system comprises several modules: a honeypot environment module (202), a deployment module (204), a monitoring module (206), a classification module (208), and a response module (210). Each module is configured to perform distinct functions within the system. The honeypot environment module (202) is configured to simulate one or more aspects of cyber-physical systems such as industrial controls, smart home systems, or medical devices. Such simulation is intended to attract threat actors by presenting what appears to be vulnerabilities within a cyber-physical system. The deployment module (204) is responsible for integrating the simulated honeypot environment into a cyber-physical system network. Said integration is performed in a manner that aims to be indistinguishable from actual network components to entice threat actor interaction. Monitoring of interactions within the simulated environment is conducted by the monitoring module (206). Said interactions are surveilled and analyzed to assess the behavior and potential threats posed by entities engaging with the honeypot environment. The classification module (208) processes the information gathered by the monitoring module (206) and categorizes detected threats into high risk and low risk levels. Based on the classification of the threats, the response module (210) is configured to take appropriate actions. For interactions deemed high risk, immediate response measures are activated, whereas for low-risk interactions, the system continues monitoring to maintain security vigilance.
In an embodiment, the system (200) includes a monitoring module (206) that incorporates a machine learning unit designed to enhance threat detection capabilities within the honeypot environment module (202). This machine learning unit analyzes interaction patterns, enabling the system to recognize and adapt to emerging threat behaviors. Through continuous learning, the unit refines its threat detection algorithms based on previously encountered interaction patterns, thereby improving its accuracy and efficiency over time. Furthermore, the machine learning unit dynamically adjusts the settings of the honeypot environment, optimizing its configurations to increase its attractiveness to potential threat actors. This adaptability ensures that the honeypot environment remains effective in enticing sophisticated cyber-physical system threat actors, thereby enhancing the system’s ability to identify and analyze potential security risks.
In another embodiment, the system (200) of claim 5 further benefits from the deployment module (204) utilizing a virtualization technique. This technique enables the rapid deployment of multiple instances of the honeypot environment module (202) across different segments of the cyber-physical system network. By creating isolated instances of the honeypot environments, the deployment module ensures that these simulated environments are segregated from the actual production network. This isolation protects the integrity of the production network while allowing for extensive and risk-free engagement with threat actors. The virtualization technique not only facilitates the scalability of the honeypot deployment but also enhances the system's resilience to attacks by preventing direct access to critical network resources.
In a further embodiment, the honeypot environment module (202) of the system (200) is enhanced to simulate failures within the honeypot environment. This functionality is designed to attract threat actors by presenting apparent vulnerabilities, thereby making the honeypot environment more appealing to those seeking to exploit cyber-physical systems. Additionally, the simulation of failures enables the collection of valuable data regarding the behavior and tactics of threat actors in response to perceived system weaknesses. By analyzing the reactions to these simulated failures, the system gains insights into the attack methodologies employed by cyber-physical threat actors, aiding in the development of more effective defense mechanisms.
In another embodiment, the system (200) is equipped with an anomaly detection module that leverages statistical models to identify deviations from normal interaction patterns within the honeypot environment module (202). This module serves as an early warning system, detecting potential new threats based on unusual activity. By analyzing interaction data for anomalies, the anomaly detection module provides timely alerts regarding emerging threats, enabling proactive measures to mitigate risks. This capability significantly enhances the system's overall security posture by ensuring that potential threats are identified and addressed before they can escalate.
In an embodiment, the classification module (208) of the system (200) assigns quantitative risk scores to interactions observed by the monitoring module (206). This scoring system enables a prioritized response mechanism, where actions are taken based on the risk score of an interaction. High-risk interactions trigger immediate and aggressive countermeasures, while lower-risk interactions may be monitored or addressed through less urgent responses. This methodical approach to threat classification and response ensures that resources are allocated efficiently, focusing on the most severe threats to the cyber-physical system network.
In yet another embodiment, the monitoring module is enhanced to perform deep packet inspection, allowing for a detailed analysis of interaction data. This detailed analysis facilitates the detection of advanced threats by examining the content and context of network traffic, leveraging the extracted information to identify sophisticated attack strategies.
FIG. 3 illustrates the operational workflow of a deceptive honeypot environment, in accordance with the embodiments of the present disclosure. The deceptive honeypot environment can be configured for Cyber-Physical Systems (CPS). The process begins with the design of the honeypot environment, which is a deliberately vulnerable system intended to attract cyber threats, thereby allowing for their detection and analysis. The honeypot is deployed within the CPS network. Post-deployment, the system enters a monitoring phase to detect interactions. If no interaction is observed, the honeypot settings may be adjusted to make it more appealing to threat actors. When an interaction is detected, the system proceeds to analyze the behavior of the entity engaging with the honeypot. The analysis leads to the classification of the threat level into high risk or low risk. High-risk interactions trigger immediate actions, which could include isolating the threat and implementing defensive measures, followed by reporting the incident to authorities. In contrast, low-risk interactions result in the system continuing its monitoring activities. For instance, within a CPS network managing a city's traffic lights, a honeypot could be designed to mimic the network components that control traffic signals. If a threat actor attempts to disrupt the traffic system by accessing the honeypot, their methods and behavior could be studied without real-world consequences. Upon detection of such an attempt, if the threat is deemed high-risk due to potential implications on traffic safety and flow, immediate action would be taken, and authorities would be alerted to prevent an actual attack on the city’s infrastructure.
FIG. 4 illustrates the architectural components that support the workflow, in accordance with the embodiments of the present disclosure. FIG. 4 delineates the architectural components that support the workflow described in the FIG. 3. The architecture is divided into three main sections: Honeypot Environments, Monitoring, and Data Storage. The Honeypot Environments section specifies the types of systems that can be emulated to attract threat actors, such as medical devices, industrial control systems, and smart home systems. The monitoring section outlines the mechanisms used to assess the honeypot's interactions, which include log analysis, intrusion detection systems, and alert systems. The data storage section details the repository for documentation stemming from the honeypot's operation, encompassing incident reports and profiles of the actors involved. An example of this architectural framework in action could involve a power grid control system, where the honeypot mimics the network and machinery controlling electricity distribution. As threat actors engage with the honeypot, their interactions would be logged and analyzed by the intrusion detection system. If an actor’s behavior indicates a high-risk threat, such as an attempt to overload the grid, the alert system would initiate protocols to counteract the threat, and detailed incident reports would be generated and stored. Concurrently, actor profiles would be created and updated, facilitating the development of a knowledge base that could inform future security measures.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We Claims

A method (100) for attracting and analyzing cyber-physical system (100) threat actors, comprising:
a) creating a honeypot environment that simulates at least one of an industrial control unit, a smart home unit, and medical devices;
b) deploying said environment within a cyber-physical network;
c) monitoring interactions within said environment;
d) analyzing behavior of interacting entities to determine threat levels;
e) classifying the threat levels into high risk and low risk;
f) taking immediate action upon classification of an interaction as high risk;
g) continuing monitoring upon classification of an interaction as low risk.
The method (100) of claim 1, wherein the monitoring step comprises:
a) log analysis;
b) intrusion detection;
c) issuing alerts through an alert unisty.
The method (100) of claim 1, wherein the honeypot environment utilizes communication protocols selected from the group consisting of:
a) Distributed Network Protocol 3 (DNP3);
b) Open Platform Communications Unified Architecture (OPC-UA);
c) Modbus protocol.
4. A cyber-physical system threat analysis system (200), comprising:
a) a honeypot environment module (202) to simulate at least one of an industrial control system, a smart home system, and medical devices;
b) a deployment module (204) for deploying the honeypot environment within a cyber-physical system network;
c) a monitoring module (206) configured to monitor interactions within the honeypot environment and analyze behavior of interacting entities;
d) a classification module (208) for determining threat levels of the interactions and categorizing them into high risk and low risk;
e) a response module (210) for taking immediate action based on the classification of the interactions.
The system (200) of claim 4, wherein the monitoring module (206) utilizes a machine learning unit configured to:
a) analyze the interaction patterns within the honeypot environment;
b) adaptively improve threat detection based on learned interaction patterns.
c) dynamically adjust honeypot environment settings for enhanced enticement of threat actors.
The system (200) of claim 5, wherein the deployment module (204) utilizes virtualization technique to:
a) rapidly deploy multiple instances of the honeypot environment;
b) isolate the honeypot environments from the actual production network.
The system (200) of claim 4, wherein the honeypot environment module (202) is further configured to simulate failures within the honeypot environment to:
a) attract threat actors by presenting apparent vulnerabilities;
b) collect data on the response of threat actors to system failures.
The system (200) of claim 4, further comprising an anomaly detection module that:
a) utilizes statistical models to detect deviations from normal interaction patterns;
b) provides early warning signals for potential new threats.
The system (200) of claim 1, wherein the classification module (208):
a) assigns quantitative risk scores to interactions;
b) prioritizes responses based on the risk score.
The system (200) of claim 1, wherein the monitoring module (206) is further configured to:
a) perform deep packet inspection for detailed analysis of interaction data;
b) utilize the extracted information for advanced threat detection.

STRATEGIC DECEPTIVE ENVIRONMENTS FOR ATTRACTING AND ANALYZING CYBER-PHYSICAL SYSTEM ADVERSARIES

Disclosed is a method for attracting and analyzing cyber-physical system threat actors. The method comprises creating a honeypot environment that simulates at least one of an industrial control unit, a smart home unit, and medical devices. Further, the environment is deployed within a cyber-physical network. Interactions within the environment are monitored. The behavior of interacting entities is analyzed to determine threat levels. The threat levels are classified into high risk and low risk. Immediate action is taken upon classification of an interaction as high risk, and monitoring continues upon classification of an interaction as low risk.

, Claims:I/We Claims

A method (100) for attracting and analyzing cyber-physical system (100) threat actors, comprising:
a) creating a honeypot environment that simulates at least one of an industrial control unit, a smart home unit, and medical devices;
b) deploying said environment within a cyber-physical network;
c) monitoring interactions within said environment;
d) analyzing behavior of interacting entities to determine threat levels;
e) classifying the threat levels into high risk and low risk;
f) taking immediate action upon classification of an interaction as high risk;
g) continuing monitoring upon classification of an interaction as low risk.
The method (100) of claim 1, wherein the monitoring step comprises:
a) log analysis;
b) intrusion detection;
c) issuing alerts through an alert unisty.
The method (100) of claim 1, wherein the honeypot environment utilizes communication protocols selected from the group consisting of:
a) Distributed Network Protocol 3 (DNP3);
b) Open Platform Communications Unified Architecture (OPC-UA);
c) Modbus protocol.
4. A cyber-physical system threat analysis system (200), comprising:
a) a honeypot environment module (202) to simulate at least one of an industrial control system, a smart home system, and medical devices;
b) a deployment module (204) for deploying the honeypot environment within a cyber-physical system network;
c) a monitoring module (206) configured to monitor interactions within the honeypot environment and analyze behavior of interacting entities;
d) a classification module (208) for determining threat levels of the interactions and categorizing them into high risk and low risk;
e) a response module (210) for taking immediate action based on the classification of the interactions.
The system (200) of claim 4, wherein the monitoring module (206) utilizes a machine learning unit configured to:
a) analyze the interaction patterns within the honeypot environment;
b) adaptively improve threat detection based on learned interaction patterns.
c) dynamically adjust honeypot environment settings for enhanced enticement of threat actors.
The system (200) of claim 5, wherein the deployment module (204) utilizes virtualization technique to:
a) rapidly deploy multiple instances of the honeypot environment;
b) isolate the honeypot environments from the actual production network.
The system (200) of claim 4, wherein the honeypot environment module (202) is further configured to simulate failures within the honeypot environment to:
a) attract threat actors by presenting apparent vulnerabilities;
b) collect data on the response of threat actors to system failures.
The system (200) of claim 4, further comprising an anomaly detection module that:
a) utilizes statistical models to detect deviations from normal interaction patterns;
b) provides early warning signals for potential new threats.
The system (200) of claim 1, wherein the classification module (208):
a) assigns quantitative risk scores to interactions;
b) prioritizes responses based on the risk score.
The system (200) of claim 1, wherein the monitoring module (206) is further configured to:
a) perform deep packet inspection for detailed analysis of interaction data;
b) utilize the extracted information for advanced threat detection.

STRATEGIC DECEPTIVE ENVIRONMENTS FOR ATTRACTING AND ANALYZING CYBER-PHYSICAL SYSTEM ADVERSARIES