Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. Due to the dynamic nature of malware with continuously changing attacking methods in the computer network, the malware datasets available publicly are to be updated systematically and benchmarked with the proper security. The enhancement needs to rule in identifying the intrusions in the system so as to provide the secure data accessibility through computer network using various intelligence techniques with the help of multiple sensors in the communication link.