Name
Abstract | ||
|---|---|---|
Host based intrusion detection systems monitor operations for significant deviations from normal and healthy behavior. Anomalies are patterns in data that do not conform to the expected normal behavior. System call analysis has been conclusively established as the best method to reveal details about the program behavior. Therefore, attackers create new exploits that makes major impact at the system call level. In this research, we developed an enhanced and optimized deep learning LSTM (Long Short Term Memory) network, for anomaly detection, trained on sequences of system calls. Our model detects any anomalous behavior in the system calls with 80% accuracy.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3317549.3326308 | Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks |
Keywords | Field | DocType |
LSTM, anomaly detection, deep learning, system call | Computer science,Computer security,Artificial intelligence,Deep learning,System level | Conference |
ISBN | Citations | PageRank |
978-1-4503-6726-4 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jayesh Soni | 1 | 0 | 1.01 |
| Nagarajan Prabakar | 2 | 62 | 6.28 |
| Himanshu Upadhyay | 3 | 1 | 1.50 |