Biblio

A new method for the detection of ransomware in an infected host is described and evaluated. The method utilizes data streams from on-board sensors to fingerprint the initiation of a ransomware infection. These sensor streams, which are common in modern computing systems, are used as a side channel for understanding the state of the system. It is shown that ransomware detection can be achieved in a rapid manner and that the use of slight, yet distinguishable changes in the physical state of a system as derived from a machine learning predictive model is an effective technique. A feature vector, consisting of various sensor outputs, is coupled with a detection criteria to predict the binary state of ransomware present versus normal operation. An advantage of this approach is that previously unknown or zero-day version s of ransomware are vulnerable to this detection method since no apriori knowledge of the malware characteristics are required. Experiments are carried out with a variety of different system loads and with different encryption methods used during a ransomware attack. Two test systems were utilized with one having a relatively low amount of available sensor data and the other having a relatively high amount of available sensor data. The average time for attack detection in the "sensor-rich" system was 7.79 seconds with an average Matthews correlation coefficient of 0.8905 for binary system state predictions regardless of encryption method and system load. The model flagged all attacks tested.