Published Paper Details:

With the rapid development of the Internet, the methods of cyber attack have become more complex and the damage to the world has become increasingly greater. Therefore, timely detection of malicious behavior on the Internet has become an important security issue today. This paper proposes an intrusion detection system based on deep learning, applies bidirectional long short term memory architecture to the system, and uses the MALWARE(numerical) data set for training and testing. Experimental tests show that the intrusion detection system can effectively detect the known or unknown malicious behavior of the network under the current network environment. Malware, short for "malicious software," refers to any intrusive software developed by cybercriminals (often called "hackers") to steal data and damage or destroy computers and computer systems. Examples of common malware include viruses, worms, Trojan viruses, spyware, adware, and ransom ware. Recent malware attacks have exfiltrated data in mass amounts. A Bidirectional LSTM, or bi-LSTM, is a sequence processing model that consists of two LSTMs one taking the input in a forward direction, and the other in a backwards directionIn bidirectional, our input flows in two directions, making a bi-LSTM different from the regular LSTM. With the regular LSTM, we can make input flow in one direction, either backwards or forward. However, in bi-directional, we can make the input flow in both directions.