• Avinash Sonule
• Mukesh Kalla

Cybersecurity attacks are growing both in frequency and sophistication over the years. This increasing sophistication and complexity call for more advancement and continuous innovation in defensive strategies. Traditional methods of intrusion detection and deep packet inspection, while still largely used and recommended, are no longer sufficient to meet the demands of growing security threats. As computing power increases and cost drops, Machine Learning is seen as an alternative method or an additional mechanism to defend against malwares, botnets, and other attacks. This paper explores Machine Learning as a viable solution by examining its capabilities to classify malicious websites as well as detecting keyloggers in a network. First, a strong data analysis is performed resulting in multiple extracted features from the initial datasets. All these features are then compared with one another through a feature selection process. Then, our approach analyzes machine learning algorithms against a dataset containing common characteristics. By leaving your computer unlocked while you are away for seconds can give hackers all the time, they need to obtain your personal information from your computer.

Machine learning, Phishing attack, Keystroke, Keylogger.

"Detecting Phishing, Keystroke and Keylogger Attacks on Computing Resources ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.q16-q23, April-2024, Available :http://www.jetir.org/papers/JETIR2404H03.pdf

"Detecting Phishing, Keystroke and Keylogger Attacks on Computing Resources ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppq16-q23, April-2024, Available at : http://www.jetir.org/papers/JETIR2404H03.pdf