Today, smartphones and Android devices are effectively in development like never before and have become the easiest cybercrime forum. It is necessary for security experts to investigate the vengeful programming composed for these frameworks if we closely observe the danger to security and defence. The main objective of this paper was to describe Mobile Sandbox, which is said to be a platform intended to periodically examine Android applications in new ways. First of all in the essence of the after-effects of static analysis that is used to handle the dynamic investigation, it incorporates static and dynamic examination and attempts to justify the introduction of executed code. On the other hand, to log calls to native APIs, it uses those techniques, and in the end, it combines the end results with machine learning techniques to collect the samples analysed into dangerous ones. We reviewed the platform for more than 69, 000 applications from multi-talented Asian international businesses sectors and found that about 21% of them officially use the local calls in their code.

Today, smartphones and Android devices are effectively in development like never before and have become the easiest cybercrime forum. It is necessary for security experts to investigate the vengeful programming composed for these frameworks if we closely observe the danger to security and defence. The main objective of this paper was to describe Mobile Sandbox, which is said to be a platform intended to periodically examine Android applications in new ways. First of all in the essence of the after-effects of static analysis that is used to handle the dynamic investigation, it incorporates static and dynamic examination and attempts to justify the introduction of executed code. On the other hand, to log calls to native APIs, it uses those techniques, and in the end, it combines the end results with machine learning techniques to collect the samples analysed into dangerous ones. We reviewed the platform for more than 69, 000 applications from multi-talented Asian international businesses sectors and found that about 21% of them officially use the local calls in their code

Phishing attacks are a common way for hackers to obtain sensitive and valuable information from unsuspecting users. These attacks often target critical data such as passwords and financial details. To combat this threat, cybersecurity professionals are constantly searching for reliable and effective techniques for detecting phishing websites. This project investigates the use of machine learning algorithms to identify phishing URLs by extracting and analyzing various features of both legitimate and phishing URLs. The goal is to create a blacklist of known phishing websites that can alert individuals when they browse or access a potentially dangerous site. The project will compare the performance of four machine learning algorithms such as Ensemble Adaboost Classifier, Multi-Layer Perceptron Classifier, Stochastic Gradient Descent classifier, and XGBoost - based on their accuracy, speed, and other factors.