Virtual Twin Analytics: Advancing IoT Security through AI-Powered Software Informatics

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems—including desktops, laptops, smartphones, and the Internet of Things (IoT)—has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This planned effort’s work represents an advance in that direction. Critical online security issues have been the focus of this proposed work’s study, which aims to address these issues using AI. Web security issues for desktop and mobile devices have been addressed in the proposed work. The planned work’s contributions to online security are as follows: The ‘MalCrawler’ web crawler is a targeted tool for finding and exploring the web. This crawler makes it easy to gather websites, particularly ones that are harmful. It does a better job of collecting dangerous websites than a typical crawler. Additionally, it is built to circumvent the evasion strategies used by rogue websites. The crawler’s ability to gather webpages—particularly dangerous ones—in order to provide datasets for ML-based analysis and solutions makes it a substantial addition to online security. Provides an in-depth examination of characteristics that may be used to categorize harmful websites. Data gain, pre-processing resource consumption, and prediction performance with various Conventional ML methods are some of the metrics used to assess these qualities. In addition, it suggests other conventional ML-based algorithms to better accurately anticipate dangerous websites by using these properties.