Crime Risk Forecasting using Cyber Security and Artificial Intelligent


  • Mr. Umakant Dinkar Butkar, Manisha J Waghmare


Artificial intelligence, Cyber security, Cyberattack, Machine learning, Crime rate, number of crimes, regression algorithm


The topic of cybersecurity has rapidly advanced over the past ten years, making headlines frequently as threats increase and hackers try to elude law authorities. The techniques used by cybercriminals have improved over time, despite the fact that their fundamental motives for launching assaults have largely remained the same. Using conventional cybersecurity tools, it is getting harder to identify and stop evolving threats. Because of improvements in cryptographic and Artificial Intelligence (AI) techniques, particularly machine learning and deep learning, cybersecurity experts may soon be able to defeat the attackers' continually evolving threat. Here, we emphasize both the benefits and drawbacks of AI in order to analyze how it might improve cybersecurity solutions. Additionally, we discuss the possibilities for further study in the field of cybersecurity related to the advancement of AI methodologies across numerous application areas. One of our society's most significant and pervasive issues is crime. Numerous crimes are perpetrated often each day. The dataset in this instance consists of the date and the annual crime rate for the corresponding years. The crime rate used in this project is only based on robberies. Utilizing historical data, we employ the linear regression algorithm to forecast the percentage of crime rate in the coming years. The algorithm receives a date as input, and the result is the proportion of crime for that particular year.



How to Cite

Mr. Umakant Dinkar Butkar, Manisha J Waghmare. (2023). Crime Risk Forecasting using Cyber Security and Artificial Intelligent. Computer Integrated Manufacturing Systems, 29(2), 43–57. Retrieved from