With the advancement of technology over the last decade, there has been a rapid increase in the number and types of malware attacks such as worms whose primary function is to self-replicate and infect systems and ransomware that corrupts and encrypts data. Developing proactive cyber defense techniques is essential for effectively detecting network anomalies that are evolving and becoming more challenging to identify. In this paper, we consider intrusion detection techniques using fast machine learning algorithms. We investigate Echo and Deep Echo State Networks machine learning structures for detecting worm and ransomware anomalies. We demonstrate, analyze, and compare merits of this approach using Slammer worm, WannaCrypt ransomware, and WestRock ransomware attack datasets.