Enhancing Cybersecurity with Machine Learning: Threat Detection, Challenges, Emerging Trends, and Future Directions
Keywords:
Artificial intelligence, Cybersecurity, Intrusion detection, Machine learning, Threat detectionAbstract
Cybersecurity threats are becoming increasingly sophisticated and persistent due to the rapid adoption of digital technologies, cloud computing, and interconnected network infrastructures. Organizations and institutions increasingly rely on these technologies to store sensitive information, conduct financial transactions, support critical operations, and facilitate secure communication. However, the continuous evolution of technology has also enabled cybercriminals to develop sophisticated techniques for bypassing security mechanisms across software, hardware, and network environments. Traditional security measures, including firewalls and antivirus solutions, are often insufficient to effectively address emerging and advanced cyber threats. To deal with these problems, organizations need security systems that can adapt and learn and can find unknown threats. Machine learning is a way to do this because it can look at a lot of data, find patterns that are not easy to see and find things that are not normal in network traffic. Machine learning also gets better at finding threats over time. It helps organizations respond to cyberattacks faster. This research is about making cybersecurity better using machine learning, the challenges that come with it and how it can be used in the future to keep security systems safe around the world. Cybersecurity is a big deal, and machine learning can help with cybersecurity. Cybersecurity threats are a problem, and machine learning can help with cybersecurity.
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