AI and Crime Prevention With Image and Video Analytics Using IoT

1. Introduction

AI solutions also enable businesses to uncover potentially worrisome patterns or links that are hidden even from the eyes of professionals. For instance, artificial neural networks may provide workers with the ability to anticipate the future steps of even unnamed criminals who have found out methods to circumvent alarm triggers in security systems that are based on binary rules. In order to do this, hundreds of photographs taken at crime scenes are uploaded into the computer and processed so that the machine learning algorithms can understand what it is they are supposed to look for. This covers the potential patterns utilised by offenders in a variety of settings, which might link all of the crimes to a single perpetrator. The use of machine learning algorithms is one of the key ways that artificial intelligence may improve the security of the internet of things.

These algorithms are able to analyse huge volumes of data produced in real time by internet of things devices, discovering patterns and abnormalities that may suggest a possible danger to network security. The Internet of Things (IoT) is responsible for collecting the data, while artificial intelligence (AI) is responsible for analysing it to mimic intelligent behaviour and assist decision-making processes with little participation from humans. The use of AI in cybersecurity removes time-consuming operations that were previously performed manually by specialists. It analyses huge amounts of data, detects possible dangers, and decreases the number of false positives by filtering out actions that are not dangerous. This allows human security specialists to concentrate their efforts on more important duties. The identification of dangers and abnormalities in the behaviour of devices, network traffic, or data patterns is one of the primary uses of artificial intelligence and machine learning in IoT security. This may assist in the detection and prevention of prospective assaults, such as denial-of-service attacks, malware infections, or data breaches.