CYBERSECURITY AND ANTI-THEFT SYSTEM FOR AUTOMOTIVES WITH TOUCH SENSORS, SEAT SCANNING, AND MACHINE LEARNING

Abstract

A cybersecurity and antitheft system for automotives featuring a comprehensive vehicle-wide touch sensor network to detect unauthorized access attempts, a seat occupant scanning system for verifying the identity of occupants using biometric data, and an integrated camera for continuous live monitoring of the vehicle's interior. The system utilizes an advanced machine learning model to predict, analyze, and respond to potential security threats in real time, providing proactive countermeasures to prevent unauthorized access. The invention also discloses a method for continuous monitoring, detailed threat analysis, and generating real-time alerts to the vehicle owner via a connected mobile device in the event of a security breach, ensuring enhanced safety and robust protection for both the vehicle and its occupants.

Specification

Description:[014] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[015] Referring now to the drawings, these are illustrated in FIG. 1, the cybersecurity system for automotives, as disclosed, integrates multiple layers of security through various sensors and machine learning to enhance the safety and protection of vehicles from unauthorized access and theft. The system comprises a touch sensor system, seat occupant scanning system, integrated camera, machine learning for threat prediction, and a method for continuous cybersecurity and antitheft monitoring.
[016] In accordance with another embodiment of the present invention, the Touch Sensor System consists of a plurality of touch sensors strategically placed on the vehicle's exterior, such as the doors, windows, and other entry points. These sensors detect touch intensity, duration, and location, allowing the system to identify unusual or forceful entry attempts. When an unusual touch is detected, the data is sent to the machine learning module to evaluate the threat level. The system can differentiate between authorized and unauthorized users by analyzing the stored touch pattern data of the vehicle owner, ensuring only authorized individuals gain access.
[017] In accordance with another embodiment of the present invention, the Seat Occupant Scanning System utilizes biometric sensors embedded within each vehicle seat. These sensors collect data on weight distribution, pressure points, and other physiological attributes, which is then used to verify the identity of the occupant. If the scanned occupant's data does not match that of an authorized user, the system triggers an alert and sends the information to the machine learning algorithm for further analysis. This helps prevent unauthorized use by ensuring that only verified occupants can operate the vehicle.
[018] In accordance with another embodiment of the present invention, The Integrated Camera for Live Monitoring is embedded in the vehicle cabin and provides continuous surveillance of the interior. The camera captures live footage, which is analyzed for any abnormal or suspicious activity and simultaneously streamed to the owner's connected mobile device, such as a smartphone. The camera is equipped with facial recognition technology, enabling it to distinguish between authorized occupants and intruders. This feature ensures that the vehicle owner can remotely monitor the interior in real time and be immediately notified of any unauthorized entry.
[020] The Machine Learning for Threat Prediction processes data from the touch sensors, seat occupant scanning, and integrated camera footage to assess potential security threats. The machine learning algorithm is trained using a diverse dataset of potential threat scenarios, which allows it to predict threats in real time with high accuracy. Upon detecting a threat, the system proactively initiates countermeasures such as locking the doors, immobilizing the vehicle, or triggering an alarm. By continuously learning from new data, the algorithm's predictive capabilities improve over time, providing robust protection against theft attempts.
[021] The Method for Antitheft and Cybersecurity involves continuously monitoring all external and internal sensors, analyzing occupant data, and evaluating touch sensor activity. When a potential unauthorized access or threat is detected, the system immediately sends a real-time alert to the vehicle owner via a connected mobile application, including live footage from the integrated camera. The predictive analysis performed by the machine learning component helps recognize suspicious behavior patterns before an actual theft attempt occurs, significantly enhancing the vehicle's security.
[022] The system also features cloud integration for storing historical data, which improves the machine learning model's learning capabilities. By leveraging cloud-based storage, the system can continually enhance the accuracy of threat predictions, making it more effective over time as discussed in figure 2.
[023] The User-Friendly Mobile Application forms an integral part of the system, providing the owner with a dashboard that displays real-time vehicle status, sensor alerts, and live footage. The app also enables remote control features, such as locking or unlocking the vehicle, activating alarms, or immobilizing the vehicle in case of detected threats. The application's intuitive interface makes it easy for the vehicle owner to manage the security system, offering a seamless user experience that empowers the owner with full control over their vehicle's safety. Additionally, the app provides historical logs of alerts and system activities, giving the user insight into past security events and system performance.
[024] The Communication Interface of the system ensures seamless connectivity between the vehicle components and the owner's mobile device. This interface facilitates real-time data transmission, allowing the owner to receive alerts and view live footage on their mobile application. The communication can be based on cellular networks, Wi-Fi, or other suitable wireless technologies, ensuring that the owner is always aware of any security threats, regardless of their location. This connectivity also plays a critical role in transmitting updates to the machine learning model, which is housed in the cloud, ensuring that the system remains up-to-date with the latest threat recognition capabilities.
[025] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident.
[026] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention. , Claims:1. A cybersecurity and antitheft system for automotive vehicles, comprising:
a plurality of touch sensors distributed across the vehicle for detecting and analyzing touch patterns to identify unauthorized access attempts;
a seat occupant scanning system, including biometric sensors embedded in the seats to verify the identity of occupants and differentiate between authorized users and potential intruders;
an integrated camera system embedded within the vehicle cabin for capturing live footage of vehicle occupants, equipped with facial recognition technology to identify unauthorized individuals, wherein the footage is transmitted to a connected mobile device for remote monitoring;
a machine learning module configured to predict potential security threats by analyzing data from the touch sensors, seat occupant scanning system, and integrated camera system, wherein the module uses predictive analytics to initiate countermeasures in response to detected threats.
2. The cybersecurity and antitheft system as claimed in claim 1, wherein the touch sensors are strategically placed on vehicle entry points, including doors, windows, and other critical areas, to detect touch intensity, duration, and location for enhanced threat analysis.
3. The cybersecurity and antitheft system as claimed in claim 1, wherein the seat occupant scanning system further includes weight distribution and pressure sensors to accurately identify occupants based on physiological attributes.
4. The cybersecurity and antitheft system as claimed in claim 1, wherein the integrated camera system continuously streams live footage to the vehicle owner's connected mobile device and generates real-time alerts if abnormal or suspicious activity is detected.
5. The cybersecurity and antitheft system as claimed in claim 1, wherein the machine learning module is configured to initiate countermeasures, including locking the vehicle doors and immobilizing the vehicle, in response to detected threats.
6. A method for providing cybersecurity and antitheft protection for automotive vehicles, comprising the steps of:
a. continuously monitoring vehicle touch sensors distributed across the vehicle to detect and analyze unauthorized touch patterns;
b. scanning occupants using seat-embedded biometric sensors to verify their identity;
c. capturing and transmitting live footage from an integrated camera within the vehicle to a connected mobile device, wherein facial recognition technology is used to differentiate authorized users from intruders;
d. analyzing the data from the touch sensors, seat occupant scanning, and camera system using a machine learning algorithm to predict and respond to potential security threats;
e. initiating countermeasures, including locking the vehicle doors, immobilizing the vehicle, and generating real-time alerts, in response to detected threats.
7. The method as claimed in claim 6, wherein the machine learning algorithm continuously learns from historical data stored in a cloud-based storage system to improve threat prediction accuracy and enhance overall system security.
8. The cybersecurity and antitheft system as claimed in claim 1, wherein the system further includes cloud integration for storing historical data to improve the learning capabilities of the machine learning module.
9. The method as claimed in claim 6, wherein the system generates real-time alerts that include live footage and sends them to the owner's mobile device in response to abnormal activities detected within the vehicle.
10. The cybersecurity and antitheft system as claimed in claim 1, wherein the system uses predictive analysis to recognize suspicious behavior patterns before an actual theft attempt occurs, thereby enabling proactive measures to prevent unauthorized access.

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