Cloud computing has become very popular and extremely demanding in the market. Several emerging technologies such as Industrial Internet of Things (IIoT), microservices and Bigdata analytics etc. are adopting cloud computing due to the availability of the high-end computing servers. However, security breaches have also started to grow along with its popularity. The advanced malware can target virtualization-based infrastructure and can harm virtual resources and thereby becoming threat to industrial applications & data hosted in cloud. The modern malware are difficult to be detected by using traditional security tools. In this paper, an introspection-assisted evolutionary bag-of-ngram approach is proposed, named as vServiceInspector for doing process monitoring from both inside the virtual machine (In-VM) & outside virtual machine (Out-VM). It employs advanced memory introspection to extract the system call sequences at Out-VM location (i.e. hypervisor). Genetic Algorithm (GA) is employed to find the most discriminating sequences of system calls and extract optimal feature set. Convolutional Neural Network (CNN), a deep learning algorithm is then used to learn and detect the malicious program execution patterns. An accuracy of 83.13%–99.63% is achieved by using University of New Mexico (UNM) dataset and an accuracy of 97.8%–99% is achieved by using University of California (Barecloud) dataset. The vServiceInspector is more accurate and more attack resilient when compared to previously proposed techniques.