Traditional reactive approach of blacklisting botnets fails to adapt to the rapidly evolving landscape of cyberattacks. An automated and proactive approach to detect and block botnet hosts will immensely benefit the industry. Behavioral analysis of attackers is shown to be effective against a wide variety of attack types. Previous works, however, focus solely on anomalies in network traffic to detect bots and botnet. In this work we take a more robust approach of analyzing the heterogeneous events including network traffic, file download events, SSH logins and chain of commands input by attackers in a compromised host. We have deployed several honeypots to simulate Linux shells and allowed attackers access to the shells. We have collected a large dataset of heterogeneous threat events from the honeypots. We have then combined and modeled the heterogeneous threat data to analyze attacker behavior. Then we have used a deep learning architecture called a Temporal Convolutional Network (TCN) to do sequential and predictive analysis on the data. A prediction accuracy of $85-97\%$ validates our data model as well as our analysis methodology. In this work, we have also developed an automated mechanism to collect and analyze these data. For the automation we have used CYbersecurity information Exchange (CYBEX). Finally, we have compared TCN with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and have showed that TCN outperforms LSTM and GRU for the task at hand.