Paper Title
Cyber Security Threat Detection for Image Object Detection with Artificial Intelligence and Machine Learning Classification Process Using Cryptography Techniques

Abstract
The great challenge in cybersecurity is to develop an automatic and effective method for detecting cyber threats. Traditional approaches often rely on predefined rules or signatures, which can cause sophisticated attacks or struggle with real-time detection. It is essential to note that AI-based techniques have been introduced in the study to detect cyber threats, thereby integrating an "Image Object Detection process." Cryptography and image processing have been combined in the proposed technique to detect cyber threats. Advanced Encryption Standard (AES) and decryption algorithms ensure data security. Notably, the image processing technique is very important for object detection. The image processing steps (pre-processing, restoration, segmentation) help to structure and clean the data for more straightforward analysis. Support Vector Machine (SVM) classifier is then applied to classify this processed data. It uses a learned model (based on past examples of regular and malicious activity) to detect whether the data contains a threat. For instance, if a suspicious pattern or object is found after segmentation, the SVM will categorize it as a "threat-detected object". In contrast, harmless patterns will be classified as "normal objects". The CSTD algorithm has been included very effectively in the proposed work to identify threats that may arise during the encryption and decryption of sensitive data. Keywords - Cyber security attacks; AES encryption decryption; Artificial Intelligence; Image processing techniques; Object detection; SVM classification; Machine learning.