Description:Field of the Invention

The present disclosure generally relates to cybersecurity systems. Particularly, the present disclosure relates to a system for cyber warfare deception and surveillance.

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 realm of cybersecurity, protecting network assets from unauthorized access and cyber attacks has become paramount. With improvements in techniques, attackers have developed methods to breach security measures, making traditional defense mechanisms less effective. The deployment of intrusion detection units (IDU) marks a significant step in identifying unauthorized access attempts. The IDU monitor network traffic for suspicious activities, utilizing various techniques to detect anomalies which could signify a breach.
Furthermore, the concept of cyber deception has emerged as a strategic approach to cybersecurity. Cyber deception technique involves the creation of decoys or honeypots, considered to mimic genuine network assets. The purpose of decoys is to mislead attackers, making them believe they have gained access to valuable parts of the network, while in reality, attackers actions are being monitored and analyzed. Cyber deception method diverts attackers from real assets and aids in understanding their tactics, techniques, and procedures (TTPs).
Despite many improvements, the management of cybersecurity operations remains a tough task. Security Operation Centers (SOCs) play a vital role in the management of cybersecurity operations regard, acting as the command and control hub for cybersecurity activities. SOCs utilize information from threat intelligence platforms to coordinate responses to identified threats, ensuring timely and effective action is taken to mitigate risks.
In light of the above discussion, there exists an urgent need for solutions which overcome the problems associated with conventional systems and techniques for cyber warfare deception and surveillance.
All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, 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.
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.
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.
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 Markush groups used in the appended claims.
Summary

In an aspect, the present disclosure provides a system for cyber warfare deception and surveillance. The system comprises an AI-enabled Intrusion Detection unit (IDU) for analyzing incoming network traffic to detect potential threats, an AI cyber deception engine for creating and managing dynamic decoy units mimicking network assets to mislead and engage potential cyber attackers, a threat intelligence platform for processing and correlating potential threats from the IDU and decoy units to identify attack Tactics, Techniques, and Procedures (TTPs), and a Security Operation Center (SOC) interface for synthesizing potential threats and facilitating responses. The AI-enabled IDU is further configured to utilize a combination of signature-based and anomaly-based detection methods and prioritizes threats based on their potential impact, directing immediate attention to the most serious issues. The AI cyber deception engine incorporates machine learning techniques to evolve decoy unit tactics based on previous engagements, improving future deception efficacy. The threat intelligence platform employs data analytics techniques, comprising pattern recognition and predictive modeling, to refine attack TTP identification. Additionally, the SOC interface comprises interactive visualization tools for threat display and manual override capabilities for incident responses, utilizing an adaptive learning technique for recommending response actions based on past incident response effectiveness.
Furthermore, the present disclosure aims to provide a method for deceiving and surveilling cyber potential threats. The method comprises analyzing network traffic using an AI-enabled IDU, deploying dynamic decoy units via an AI cyber deception engine to simulate network assets, processing and correlating potential threats using a threat intelligence platform to determine attackers' TTPs, and presenting synthesized threat intelligence to a SOC interface to facilitate threat response actions. The method further comprises dynamically adjusting decoy unit behavior based on attacker interactions, offering predictive analytics and actionable insights for strategic defense planning, initiating automated response protocols to isolate threats, and employing a continuous learning process to adapt AI techniques based on historical attack patterns, thereby improving the system's defensive capabilities over time.
Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

Brief Description of the Drawings

The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 illustrates system for cyber warfare deception and surveillance against cyber threats, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates method for deceiving and surveilling cyber potential threats implemented on the system, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates working flow of AI-powered cyber warfare deception and surveillance, in accordance with the embodiments of the present disclosure.
In the accompanying drawings, a number in parentheses is employed to represent an item over which the number in parentheses is positioned or an item to which the number in parentheses is adjacent. A number not in parentheses relates to an item identified by a line linking the number not in parentheses to the item. When a number is not in parentheses and accompanied by an associated arrow, the number not in parentheses is used to identify a general item at which the arrow is pointing.

Detailed Description

The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.
The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of a motor of an electric vehicle and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
The terms “comprise”, “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
The term "AI-enabled Intrusion Detection Unit (IDU)" as used throughout the present disclosure relates to a computational unit integrated with artificial intelligence capabilities, considered for the analysis of incoming network traffic. The function of the AI-enabled IDU is to scrutinize network traffic to detect potential cyber threats by employing progressive mechanism and machine learning techniques. Such scrutiny enables the early detection of cyber threats, thereby improving the overall security posture of the network.
The term "AI cyber deception engine" as used throughout the present disclosure relates to a software component which employs artificial intelligence to create and manage dynamic decoy units. The decoy units are considered to mimic authentic network assets with the intention of misleading and engaging potential cyber attackers. The AI cyber deception engine dynamically adjusts the characteristics and behaviors of the decoy units to make them more convincing and effective in trapping cyber attackers. The deployment of such decoy units serves as a security measure, deterring attackers and protecting genuine network assets from unauthorized access.
The term "threat intelligence platform" as used throughout the present disclosure relates to a unit that integrates, processes, and correlates information regarding potential threats identified by the AI-enabled IDU and the decoy units managed by the AI cyber deception engine. The threat intelligence platform is adept at analyzing the gathered threats to identify the Tactics, Techniques, and Procedures (TTPs) utilized by cyber attackers. The ability to correlate and analyze threat information from multiple sources enables the threat intelligence platform to provide a understanding of potential cyber threats, facilitating more effective defensive approaches.
The term "Security Operation Center (SOC) interface" as used throughout the present disclosure relates to an interface which consolidates synthesized potential threats identified and processed by the threat intelligence platform. The SOC interface facilitates the response to identified potential threats by providing a centralized platform for security operations teams to monitor, analyze, and respond to cyber threats. The integration of the SOC interface with the threat intelligence platform provides that security operations teams have access to threat intelligence, enabling timely and informed decision-making in response to cyber threats.
FIG. 1 illustrates, the system (100) considered for cyber warfare deception and surveillance, in accordance with the embodiments of the present disclosure. The system (100) comprises the AI-enabled IDU (102), the AI cyber deception engine (104), the threat intelligence platform (106), and the SOC interface (108).
The AI-enabled IDU (102) is tasked with analyzing incoming network traffic to detect potential threats. It uses advanced artificial intelligence algorithms to scrutinize network behavior and identify unusual or malicious patterns which may signify a security threat. The AI cyber deception engine (104) is configured to create and manage dynamic decoy units. The decoy units are virtual entities which mimic legitimate network assets with the intention of deceiving potential cyber attackers. By interacting with the decoy units, attackers reveal their methods and intentions without causing harm to actual network resources. The threat intelligence platform (106) processes and correlates information collected by both the AI-enabled IDU (102) and the dynamic decoy units managed by the AI cyber deception engine (104). The threat intelligence platform (106) is important for analyzing and identifying the Tactics, Techniques, and Procedures (TTPs) of the attackers. By doing so, the threat intelligence platform (106) helps in understanding the nature of the threats and aids in developing strategies to counteract attackers. Additionally, the SOC interface (108) is where the synthesized threat intelligence is presented. The SOC interface (108) is the central hub from which security operations are coordinated. The SOC interface (108) takes the processed and correlated threat data and makes it actionable, enabling security analysts to respond promptly and effectively to the identified threats.
The embodiment describes an improvement to the AI-enabled IDU (102) within a system (100) considered for cyber warfare deception and surveillance. The AI-enabled IDU (102) employs both signature-based and anomaly-based detection methods to identify potential cyber threats more effectively.
Signature-based detection is a method where incoming network traffic is compared against a database of known threat signatures. The threat signatures are patterns or characteristics known to be associated with malicious activities. The signature-based detection method is highly effective for identifying and mitigating known threats, as signature-based detection relies on a repository of previously identified threat data. Anomaly-based detection, in contrast, does not rely on known threat signatures. Instead, anomaly-based detection monitors network traffic for any deviations from established baselines of normal activity. The Anomaly-based detection method is particularly useful for identifying new or previously unknown threats which do not match any existing signatures. Anomaly-based detection works by understanding what constitutes normal network behavior and then flagging any activity which significantly deviates from the established baselines of normal activity norm as a potential threat.
By combining signature-based and anomaly-based detection methods, the AI-enabled IDU (102) improves threat detection capabilities. The combination of signature-based and anomaly-based detection allows broad coverage of known threats through signature-based detection while also providing the flexibility to identify new, emerging threats through anomaly-based detection.
Furthermore, the AI-enabled IDU (102) is configured to prioritize detected threats based on threat potential impact on the network. Thereby, threats deemed to have a higher potential for damage or disruption are flagged for immediate attention by the SOC interface (108). Such prioritization provides that the most critical threats are addressed foremost, optimizing the allocation of resources and response efforts to mitigate the most serious risks to the network.
In another embodiment, describes the functionality of the AI cyber deception engine (104) within the system (100) considered for cyber warfare deception and surveillance. Disclosure focuses on how the AI cyber deception engine (104) utilizes machine learning techniques to adapt and refine the tactics of decoy units based on past interactions with attackers.
Machine learning is a subset of artificial intelligence which enables units to learn and improve from experience without being explicitly programmed for each specific task. In the AI cyber deception engine (104), machine learning technique is applied to analyze the outcomes of previous engagements between decoy units and attackers. Previous engagements provide valuable information on how attackers respond to different decoy configurations and tactics.
Based on machine learning technique analysis, the AI cyber deception engine (104) adjusts the operational tactics of the decoy units to make them more convincing and effective in future engagements. The AI cyber deception engine (104) might comprise changes in how the decoy units mimic real network assets, respond to unauthorized access attempts, or how the decoy simulates normal network traffic patterns. The objective is to make the decoy units more difficult for attackers to distinguish from genuine network assets..
The adaptive approach described in the disclosure provides that deception capabilities of the unit are not static. Instead, the AI cyber deception engine (104) evolve over time, becoming increasingly sophisticated in response to the changing tactics and techniques used by cyber attackers. Thereby continuous improvement cycle, improves ability of the AI cyber deception engine (104) to deceive attackers, waste attackers resources, and ultimately deter attackers from pursuing their malicious objectives. Moreover, by engaging attackers with the AI cyber deception engine (104) evolving decoy units, the system (100) can gather more detailed intelligence about the attackers' methods, further informing and improving the AI cyber deception engine (104) defensive strategies.
In a further embodiment, elaborates on the operational improvements of the threat intelligence platform (106) within a cyber warfare deception and surveillance system (100). The threat intelligence platform (106) specifically details how the platform utilizes data analytics techniques, notably pattern recognition and predictive modeling, to refine the process of identifying the Tactics, Techniques, and Procedures (TTPs) employed by cyber attackers.
Pattern recognition is a method used to identify repeating sequences, behaviors, or regularities within large datasets. In the context of cyber security, pattern recognition allows the threat intelligence platform (106) to discern consistent patterns in how attackers orchestrate their cyber-attacks, as well as the tools attackers use, the vulnerabilities attackers exploit, and the sequences of actions attackers typically follow. Predictive modeling goes a step further by using statistical techniques to make inferences about future behavior based on historical data. For the threat intelligence platform (106), means analyzing past attack patterns to forecast potential future attack strategies. Predictive modeling capability is important for preemptively strengthening defenses of the system (100) against anticipated attack vectors.
The threat intelligence platform (106) accomplishes patterns and trends in attacker behavior by aggregating and analyzing threat data from multiple sources within the system (100), comprising the AI-enabled IDU (102) and the decoy units overseen by the AI cyber deception engine (104). The AI-enabled IDU (102) contributes threat data related to detected threats and anomalies in network traffic, while the decoy units provide insights into attackers' interactions with what they perceive as legitimate network assets.
Through analysis of threat data, the threat intelligence platform (106) identifies emerging trends and patterns in cyber-attack strategies and predict future attacks more accurately. The threat intelligence platform (106) predictive capability allows the system (100) to adapt defenses in early and improving the system (100) ability to thwart potential cyber threats.
Overall, by employing data analytics techniques for pattern recognition and predictive modeling, the threat intelligence platform (106) significantly bolsters defense mechanisms of the system (100). The threat intelligence platform (106) aids in the more accurate identification of attack TTPs and improves the system (100) overall capacity to predict, detect, and respond to cyber threats in a timely and effective manner.
In yet another embodiment describes the functionalities and structures of the SOC interface (108) within the system (100) considered for cyber warfare deception and surveillance. The SOC interface (108) is equipped with interactive visualization tools and manual override capabilities to aid in the management of incident response actions. Additionally, the SOC interface (108) incorporates an adaptive learning technique to refine the SOC interface (108) recommendations for response actions over time.
Interactive visualization tools are important components which enable the security operations teams to view and understand potential threats in a more accessible and visually engaging manner. By presenting threat data through graphs, charts, and maps, interactive visualization tools make complex information easier to comprehend, allowing for faster assessment of the situation. Visual representation of threat data helps in identifying patterns, pinpointing anomalies, and understanding the scope and scale of threats more effectively. As a result, security teams can make quicker and more informed decisions regarding the appropriate response to various cyber threats.
The manual override capabilities provided by the SOC interface (108) allow security personnel to intervene and make judgment-based decisions on incident response actions. The manual override feature is important in situations where automated responses may not be sufficient or appropriate, offering the flexibility to tailor responses to the specific nuances of a threat. The manual override provides that human expertise and situational awareness can be applied directly, providing an essential layer of adaptability and control.
Furthermore, the SOC interface (108) employs an adaptive learning technique, which is a method which allows the unit to learn from the outcomes of past incident response actions. By analyzing the effectiveness of previous responses, the SOC interface (108) can recommend more effective response strategies for future incidents. The adaptive learning capability is grounded in the use of historical data to continuously refine and improve incident response protocols of the unit, providing that the system (100) becomes more proficient over time at mitigating threats.
FIG. 2 illustrates, a method for deceiving and surveilling cyber potential threats implemented on the system, in accordance with the embodiments of the present disclosure. In step (202), the AI-enabled IDU (102) detect anomalies and patterns indicative of potential cyber threats by applying analysis techniques. The step (202) is important for identifying possible security breaches or attacks at an early stage, thereby enabling active measures to be taken. Step (204), deploying dynamic decoy units within a network via an AI cyber deception engine pertains to the process of creating and positioning simulated network assets by artificial intelligence. The decoy units are considered to mimic genuine network resources with the aim of attracting and engaging cyber attackers. The use of dynamic decoys serves as a strategic measure to divert attackers from real assets, thus reducing the risk of actual breaches while gathering intelligence on the attackers' methods and intentions.
In step (206), processing and correlating potential threat data from the AI-enabled IDU (102) and the AI cyber deception engine (104) using a threat intelligence platform (106) relates the analysis and integration of threat information. By correlating threat data, the threat intelligence platform (106) identifies patterns and behaviors associated with attacker tactics, techniques, and procedures (TTPs). The step (206) is fundamental in understanding the modus operandi of attackers, which in turn, aids in devising more effective defense strategies. The step (208), presenting synthesized threat intelligence to the SOC interface (208) and facilitating threat response actions are the important step of conveying the analyzed threat intelligence to the SOC interface (108). The SOC interface (108) serves as a centralized point for monitoring, assessing, and responding to identified cyber threats. The facilitation of threat response actions in step (208) through the SOC interface (108) enables security teams to take informed and timely measures to mitigate potential risks.
In an embodiment, the method (200) further comprises dynamically adjusting the behavior and configuration of the decoy units based on analysis of attacker interactions. Dynamic adjustment aims to improve engagement with and information gathering about potential attackers. By analyzing the interactions attackers have with the decoy units, the method enables the AI cyber deception engine (104) to modify the decoys' behavior and configuration to make the decoy units more appealing and convincing to attackers. The said dynamic adjustment improves the efficacy of the decoy units in misleading attackers.
In another embodiment, the step (208) comprises offering predictive analytics and actionable insights to the SOC, enabling the formulation of security measures and strategic defense planning. The predictive analytic aspect of the method empowers SOC personnel with the information necessary to preemptively strengthen the network's defenses and to plan strategic responses to emerging cyber threats.
In a further embodiment, the step (204) comprises automated response protocols initiated by the AI cyber deception engine (104) to isolate potential threats and protect important network assets from attackers. The automated response protocols are considered to respond swiftly to detected threats, isolating them to prevent any harm to the network's important assets. By the AI cyber deception engine (104) for initiating automated response protocols, the method provides that potential threats are managed efficiently and with minimal human intervention, improving the speed and effectiveness of the network's defensive actions.
In yet another embodiment, the step (202) and the step (204) comprise a continuous learning process wherein the AI techniques adapt based on historical attack patterns and successful deception tactics. The continuous learning process comprises the AI-enabled components of the system (100)—such as the IDU and the cyber deception engine—learning from past experiences to improve their detection, deception, and response strategies.
FIG. 3 illustrates, working flow of AI-powered cyber warfare deception and surveillance, in accordance with the embodiments of the present disclosure. The outlines a cybersecurity system featuring an external network linked to an Intrusion Detection System (IDS), indicating the process of monitoring potential external threats. Additionally, it shows an AI Cyber Deception Engine, represented by a CCTV warning, which leads to a more complex symbol combining a mousetrap and technology elements, suggesting the use of AI to create traps or deceptive tactics to confuse and catch cyber attackers. Lastly, a Threat Intelligence Platform depicted by a globe with symbols of threats connects to a Security Operation Center (SOC). This symbolizes the flow of gathered intelligence about potential cyber threats being processed and managed by a centralized unit responsible for coordinating a comprehensive defense strategy, highlighting a sophisticated and proactive cybersecurity infrastructure..
The system (100) includes an AI-enabled IDU (102), which is responsible for analyzing incoming network traffic. Network traffic analysis is aimed at detecting potential threats by scrutinizing data packets which traverse the network. The AI-enabled IDU (102) utilizes machine learning techniques to effectively identify unusual patterns or signatures that may indicate malicious activity. In conjunction with the AI-enabled IDU (102), the system (100) comprises an AI cyber deception engine (104). The AI cyber deception engine is configured to create and manage dynamic decoy units. The decoy units are considered to mimic authentic network assets, thereby serving as bait to mislead and engage cyber attackers. The dynamic nature of the decoy units means that they can adapt to the changing tactics of cyber attackers, making the deception more convincing and thereby increasing the likelihood of misleading potential attackers.
Furthermore, the system (100) includes a threat intelligence platform (106) which processes and correlates the data regarding potential threats identified by both the AI-enabled IDU (102) and the decoy units from the AI cyber deception engine (104). The threat intelligence platform (106) employs various techniques to analyze the threat data and identify the Tactics, Techniques, and Procedures (TTPs) of the attackers. The TTPs information is important for understanding the modus operandi of the attackers and aids in developing strategies to counteract them.
Finally, the system (100) incorporates a Security Operation Center (SOC) interface (108). The SOC interface (108) synthesizes the potential threats that have been processed and correlated by the threat intelligence platform (106). It serves as the centralized unit through which the synthesized threat intelligence is presented, and it facilitates the coordination and execution of appropriate response actions to the identified potential threats. The SOC interface (108) ensures that the responses to cyber threats are prompt, organized, and effective.
Further, the depicted workflow illustrates the seamless integration of various AI-powered components within the system (100) to create a dynamic and adaptive defense mechanism against cyber threats.
Throughout the present disclosure, the term “computing device” relates to an electronic device, including but are not limited to, a cellular phone, a smart phone, a personal digital assistant (PDA), a handheld device, a wireless modem, a laptop, a computer, a server, a personal computer, a work station, a mobile terminal, a subscriber station, a remote station, a user terminal, a terminal, a subscriber unit, an access terminal, a wearable computer, a wearable computing device, a smart watch, a server etc. The computing device may include a casing, a memory, a processor, a network interface card, a microphone, a speaker, a keypad, and a display.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit.
In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K-Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering – Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
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.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Modifications to embodiments and combination of different embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, “is” used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.

I/We claims:
1. A system (100) for cyber warfare deception and surveillance, comprising:
an AI-enabled Intrusion Detection unit (IDU) (102) which analyzes incoming network traffic to detect potential threats;
an AI cyber deception engine (104) to create and manage the decoy units which mimic network assets to mislead and engage potential cyber attackers;
a threat intelligence platform (106) which processes and correlates threats from the AI-enabled IDU (102) and the decoy units from the AI cyber deception engine (104) to identify attack tactics, techniques, and procedures (TTPs);
a Security Operation Center (SOC) interface (108) synthesized threats, processed and correlated from the threat intelligence platform (106) and facilitates responses to the identified potential threats.

2. The system of claim 1, wherein the AI-enabled IDU (102) is further configured to:
utilize a combination of signature-based and anomaly-based detection method for potential threats detection; and
prioritizes detected potential threats based on their potential impact to the network, directing the SOC interface (108) to pay immediate attention to the most serious issues.

3. The system of claim 1, wherein the AI cyber deception engine (104) incorporates machine learning techniques, which evolve decoy unit tactics based on previous engagements with attackers to improve future deception efficacy.

4. The system of claim 1, wherein the threat intelligence platform (106) employs data analytics techniques, comprising pattern recognition and predictive modeling, to refine the identification of attack TTPs.

5. The system of claim 1, wherein the SOC interface (108):
-comprises interactive visualization tools for displaying potential threat and enables manual override capabilities for incident response actions; and
utilizes an adaptive learning technique to recommend response actions based on the effectiveness of past incident responses.

6. A method for deceiving and surveilling cyber potential threats implemented on a system (100), comprising the steps of:
analyzing network traffic using an AI-enabled IDU (102) to detect potential cyber threats;
deploying dynamic decoy units within a network via an AI cyber deception engine (104), which simulate network assets to engage and mislead potential attackers;
processing and correlating potential threat from the IDU and the deception engine using a threat intelligence platform (106) to determine attacker’s TTPs;
presenting synthesized threat intelligence to a SOC interface (108) and facilitating threat response actions.

7. The method of claim 6, further comprising dynamically adjusting the behavior and configuration of the decoy units based on analysis of attacker interactions to improve engagement and information gathering.

8. The method of claim 6, wherein the presenting step comprises offering predictive analytics and actionable insights to the SOC, enabling security measures and strategic defense planning.

9. The method of claim 6, further comprising automated response protocols initiated by the AI cyber deception engine (104) to isolate potential threats and protect critical network assets from attackers.

10. The method of claim 6, further comprising a continuous learning process wherein the AI techniques adapt based on historical attack patterns and successful deception tactics, thereby improving the system's defensive capabilities over time.

The present disclosure provides a system for cyber warfare deception and surveillance, comprising: an AI-enabled Intrusion Detection unit (IDU) which analyzes incoming network traffic to detect potential threats; an AI cyber deception engine configured to create and manage dynamic decoy units which mimic network assets to mislead and engage potential cyber attackers; a threat intelligence platform which processes and correlates potential threats from the AI-enabled IDU and decoy units from the AI cyber deception engine to identify attack Tactics, Techniques, and Procedures (TTPs); a Security Operation Center (SOC) interface synthesized potential threats, processed and correlated from the threat intelligence platform and facilitates responses to the identified potential threats. , Claims:I/We claims:
1. A system (100) for cyber warfare deception and surveillance, comprising:
an AI-enabled Intrusion Detection unit (IDU) (102) which analyzes incoming network traffic to detect potential threats;
an AI cyber deception engine (104) to create and manage the decoy units which mimic network assets to mislead and engage potential cyber attackers;
a threat intelligence platform (106) which processes and correlates threats from the AI-enabled IDU (102) and the decoy units from the AI cyber deception engine (104) to identify attack tactics, techniques, and procedures (TTPs);
a Security Operation Center (SOC) interface (108) synthesized threats, processed and correlated from the threat intelligence platform (106) and facilitates responses to the identified potential threats.

2. The system of claim 1, wherein the AI-enabled IDU (102) is further configured to:
utilize a combination of signature-based and anomaly-based detection method for potential threats detection; and
prioritizes detected potential threats based on their potential impact to the network, directing the SOC interface (108) to pay immediate attention to the most serious issues.

3. The system of claim 1, wherein the AI cyber deception engine (104) incorporates machine learning techniques, which evolve decoy unit tactics based on previous engagements with attackers to improve future deception efficacy.

4. The system of claim 1, wherein the threat intelligence platform (106) employs data analytics techniques, comprising pattern recognition and predictive modeling, to refine the identification of attack TTPs.

5. The system of claim 1, wherein the SOC interface (108):
-comprises interactive visualization tools for displaying potential threat and enables manual override capabilities for incident response actions; and
utilizes an adaptive learning technique to recommend response actions based on the effectiveness of past incident responses.

6. A method for deceiving and surveilling cyber potential threats implemented on a system (100), comprising the steps of:
analyzing network traffic using an AI-enabled IDU (102) to detect potential cyber threats;
deploying dynamic decoy units within a network via an AI cyber deception engine (104), which simulate network assets to engage and mislead potential attackers;
processing and correlating potential threat from the IDU and the deception engine using a threat intelligence platform (106) to determine attacker’s TTPs;
presenting synthesized threat intelligence to a SOC interface (108) and facilitating threat response actions.

7. The method of claim 6, further comprising dynamically adjusting the behavior and configuration of the decoy units based on analysis of attacker interactions to improve engagement and information gathering.

8. The method of claim 6, wherein the presenting step comprises offering predictive analytics and actionable insights to the SOC, enabling security measures and strategic defense planning.

9. The method of claim 6, further comprising automated response protocols initiated by the AI cyber deception engine (104) to isolate potential threats and protect critical network assets from attackers.

10. The method of claim 6, further comprising a continuous learning process wherein the AI techniques adapt based on historical attack patterns and successful deception tactics, thereby improving the system's defensive capabilities over time.