Traditional attack detection approaches utilize predefined databases of known signatures about already-seen tools and malicious activities observed in past cyber-attacks to detect future attacks. More sophisticated approaches apply machine learning to detect abnormal behavior. Nevertheless, a growing number of successful attacks and the increasing ingenuity of attackers prove that these approaches are insufficient. This paper introduces an approach for digital forensics-based early detection of ongoing cyber-attacks called Fronesis. Fronesis can be implemented via different ways, such as a program code, machine learning and web application based django. Here, we use both the machine learning and django framework to identify the attack in digital forensics. Finally, the proposed approach is demonstrated through an different types of attack related to cyber in digital forensics like brute force attack and web crawling attack and port scan attack and normal. Experimental results showed the better performance of the system.