Description:Artificial Intelligence Based Attack Prediction in Vehicular Ad-Hoc Network (VANET)

The present invention relates to a system and method for predicting potential security attacks in Vehicular Ad-Hoc Networks (VANETs) using artificial intelligence techniques. The system employs machine learning algorithms to analyze network traffic patterns, vehicle behaviors, and communication anomalies, enabling the early detection and prediction of potential security threats within a VANET environment.

BACKGROUND
[0001] 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.
[0002] In Vehicular Ad-Hoc Networks (VANETs) represent a revolutionary paradigm in modern transportation systems by integrating wireless communication and information exchange capabilities into vehicles and roadside infrastructure.
[0003] VANET networks facilitate real-time data sharing among vehicles, enabling enhanced road safety, traffic management, and passenger experience. However, the dynamic and open nature of VANETs exposes them to a wide array of security challenges, necessitating innovative solutions to ensure their robustness and reliability.
[0004] VANETs operate within a complex ecosystem where vehicles communicate with each other (V2V), as well as with infrastructure elements like traffic lights and road sensors (V2I), forming a highly interconnected network. This connectivity empowers various applications, such as collision avoidance, traffic signal optimization, and cooperative platooning, which rely on accurate and timely information exchange. Nevertheless, this connectivity also introduces vulnerabilities that malicious actors can exploit to compromise the network's integrity, confidentiality, and availability.
[0005] Traditional security mechanisms, like cryptography and authentication protocols, have been devised to address specific security challenges within VANETs. While these mechanisms play a crucial role in safeguarding communication, they do not necessarily anticipate or predict attacks. This lack of proactive defense leaves VANETs vulnerable to emerging threats and sophisticated attacks that may bypass existing security measures.
[0006] To enhance the security landscape of VANETs, there is a growing need for predictive and proactive solutions that can identify potential security breaches before they materialize. This invention proposes an innovative approach by integrating artificial intelligence and machine learning techniques into VANET security.
[0007] By analyzing vast amounts of network data, vehicle behaviors, and communication patterns, the proposed system can discern subtle anomalies and predict potential security threats. Through continuous learning and adaptation, the system can provide early warnings, enabling network administrators and vehicles to take preventive actions and mitigate risks effectively.
[0008] 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.
[0009] 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.

OBJECTS OF THE INVENTION
[0010] It is an object of the present disclosure to provide a system and method that can proactively identify potential security threats and attacks within a Vehicular Ad-Hoc Network (VANET) before they escalate into disruptive or harmful incidents. By leveraging artificial intelligence and machine learning techniques, the invention aims to detect subtle anomalies and deviations from normal network behavior, enabling timely intervention.
[0011] It is an object of the present disclosure to enhance the overall security posture of VANETs by offering a predictive security layer. Traditional security mechanisms focus on reactive measures after a threat is detected, whereas this invention aims to anticipate and predict potential attacks, thereby reducing the network's susceptibility to various security breaches such as data manipulation, eavesdropping, and denial-of-service attacks.
[0012] It is an object of the present disclosure to provide real-time risk assessment and analysis within the VANET environment. By continuously monitoring network traffic patterns, vehicle interactions, and communication anomalies, the system will be able to assess the potential severity and impact of predicted attacks, allowing for appropriate countermeasures and mitigation strategies to be employed promptly.
[0013] It is an object of the present disclosure to create an intelligent system that can adapt and evolve over time. By utilizing machine learning algorithms, the system can learn from historical data and past attacks, improving its prediction accuracy and response capabilities as it encounters new and evolving security threats.
SUMMARY
[0001] The present invention proposes artificial intelligence based attack prediction in vehicular ad-hoc network (VANET).
[0002] The present invention introduces a groundbreaking solution to enhance the security and reliability of modern intelligent transportation systems. By leveraging the power of artificial intelligence and machine learning, the invention aims to proactively predict and mitigate potential security attacks within VANETs, ensuring a safer and more efficient driving experience for individuals and communities.
[0003] VANETs have emerged as a transformative technology, enabling vehicles to communicate with each other and with roadside infrastructure in real-time. This connectivity opens up opportunities for advanced applications such as collision avoidance, traffic optimization, and cooperative driving. However, the dynamic and open nature of VANETs exposes them to various security challenges, including data manipulation, eavesdropping, and denial-of-service attacks, which can compromise safety and disrupt network operations.
[0004] One should appreciate that although the present disclosure has been explained with respect to a defined set of functional modules, any other module or set of modules can be added/deleted/modified/combined and any such changes in architecture/construction of the proposed method are completely within the scope of the present disclosure. Each module can also be fragmented into one or more functional sub-modules, all of which also completely within the scope of the present disclosure.
[0005] 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
[0014] 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 analysis of the present disclosure.
[0015] Figure 1: Attack Prediction in Vehicular Ad-Hoc Network.

DETAILED DESCRIPTION
[0016] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0017] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0018] Exemplary embodiments will now be described more fully hereinafter with reference to the drawings, in which exemplary embodiments are shown. This disclosure, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure.
[0019] 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.
[0020] The "Artificial Intelligence-Based Attack Prediction in Vehicular Ad-Hoc Network (VANET)" invention encompasses a comprehensive system and method that leverage advanced artificial intelligence (AI) and machine learning (ML) techniques to predict, identify, and mitigate potential security attacks within the context of Vehicular Ad-Hoc Networks (VANETs). The invention's architecture consists of multiple interconnected modules, each playing a crucial role in achieving its objectives.
[0021] The process begins with the Data Collection Module, which gathers diverse data inputs from various sources within the VANET environment. These sources include communication messages exchanged between vehicles (V2V) and between vehicles and infrastructure elements (V2I), as well as vehicle trajectories, network traffic patterns, and other relevant parameters. This module ensures a comprehensive data collection process that captures the dynamic and real-world behavior of the VANET.
[0022] The collected raw data is then pre-processed and transformed into meaningful and relevant features. The Feature Extraction Module utilizes data preprocessing techniques, signal processing, and feature engineering to extract attributes that encapsulate the behavior of vehicles and the network. These features serve as input for the subsequent machine learning stages.
[0023] The heart of the invention lies in the Machine Learning Module, where predictive models are trained to analyze the extracted features and discern patterns associated with potential security attacks. Various AI and ML algorithms are employed, such as deep learning, neural networks, decision trees, and anomaly detection, to learn the normal behavior of the VANET and to identify deviations indicative of attacks. The models continuously evolve and improve through adaptive learning, incorporating new data and adjusting to emerging attack strategies.
[0024] The trained predictive models form the foundation of the Attack Prediction Module. In real-time, this module analyzes incoming data streams, including ongoing network traffic, vehicle interactions, and communication patterns. By comparing the observed behavior with the learned patterns, the module can predict potential security threats before they materialize. The predictions are accompanied by risk assessment scores that quantify the severity and potential impact of each predicted attack.
[0025] When a potential security threat is predicted, the Alert and Mitigation Module comes into play. This module generates alerts and notifications to relevant stakeholders, such as network administrators, vehicle operators, and traffic management centers. The alerts provide early warnings about the impending attack, allowing stakeholders to take proactive measures.

, Claims:I/We Claim
1. Claim An Artificial Intelligence-Based Attack Prediction system for Vehicular Ad-Hoc Networks (VANETs), comprising:
• A Data Collection Module configured to collect and aggregate real-time network traffic data, vehicle trajectories, and communication messages exchanged within the VANET.
• A Feature Extraction Module operatively connected to the Data Collection Module, the Feature Extraction Module configured to preprocess the collected data, extracting a diverse range of relevant features including.
• A Machine Learning Module interlinked with the Feature Extraction Module, the Machine Learning Module employing advanced machine learning algorithms including decision trees to acquire an adaptive understanding of VANET normal behavior and deviations, thereby enabling the identification of potential security threats.
• An Attack Prediction Module communicatively linked with the Machine Learning Module, the Attack Prediction Module executing real-time analysis of incoming data streams.
• An Alert and Mitigation Module in communication with the Attack Prediction Module, the Alert and Mitigation Module configured to generate timely alerts, notifications, and visualizations upon prediction of security threats.
2. The Artificial Intelligence-Based Attack Prediction system of claim 1, wherein the potential security threats encompass a spectrum of attack vectors, including data integrity breaches, confidentiality violations, availability disruptions, Sybil attacks, and replay attacks, the system's predictive capabilities adaptable to various evolving threats.
3. The Artificial Intelligence-Based Attack Prediction system of claim 1, wherein the Machine Learning Module incorporates historical data and continuously adapts to emerging attack strategies, the system's predictive accuracy improving over time.
4. The Artificial Intelligence-Based Attack Prediction system of claim 1, wherein the Alert and Mitigation Module is equipped to recommend a range of dynamic mitigation strategies, comprising rerouting affected vehicles, isolating compromised nodes, adjusting communication protocols, and implementing traffic diversion measures, thereby minimizing the impact of predicted security incidents.