Sequential Frequency Vector Based System Call Anomaly Detection

2010 ◽  
Author(s):  
Ying Wu ◽  
Jianhui Jiang ◽  
Liangliang Kong
Keyword(s):  
Anomaly Detection ◽  
System Call ◽  
Frequency Vector ◽  
Sequential Frequency

scholarly journals A Host-Based Anomaly Detection Framework Using XGBoost and LSTM for IoT Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiali Wang ◽  
Xiang Lu
Keyword(s):  
Anomaly Detection ◽  
Detection System ◽  
Gradient Boosting ◽  
System Call ◽  
Iot Security ◽  
Extreme Gradient Boosting ◽  
Ip Camera ◽  
Iot Devices ◽  
System Call Sequences ◽  
Abnormal Behaviors

The Internet of Things (IoT) is rapidly spreading in various application scenarios through its salient features in ubiquitous device connections, ranging from agriculture and industry to transportation and other fields. As the increasing spread of IoT applications, IoT security is gradually becoming one of the most significant issues to guard IoT devices against various cybersecurity threats. Usually, IoT devices are the main components responsible for sensing, computing, and transmitting; in this case, how to efficiently protect the IoT device itself away from cyber attacks, like malware, virus, and worm, becomes the vital point in IoT security. This paper presents a brand new architecture of intrusion detection system (IDS) for IoT devices, which is designed to identify device- or host-oriented attacks in a lightweight manner in consideration of limited computation resources on IoT devices. To this end, in this paper, we propose a stacking model to couple the Extreme Gradient Boosting (XGBoost) model and the Long Short-Term Memory (LSTM) model together for the abnormal state analysis on the IoT devices. More specifically, we adopt the system call sequence as the indicators of abnormal behaviors. The collected system call sequences are firstly processed by the famous n-gram model, which is a common method used for host-based intrusion detections. Then, the proposed stacking model is used to identify abnormal behaviors hidden in the system call sequences. To evaluate the performance of the proposed model, we establish a real-setting IP camera system and place several typical IoT attacks on the victim IP camera. Extensive experimental evaluations show that the stacking model has outperformed other existing anomaly detection solutions, and we are able to achieve a 0.983 AUC score in real-world data. Numerical testing demonstrates that the XGBoost-LSTM stacking model has excellent performance, stability, and the ability of generalization.

Anomaly Detection Using System Call Sequence Sets

2007 ◽  
Vol 2 (6) ◽  
Author(s):  
Surekha Mariam Varghese ◽  
K.Poulose Jacob
Keyword(s):  
Anomaly Detection ◽  
System Call ◽  
Call Sequence

A statistical pattern based feature extraction method on system call traces for anomaly detection

2020 ◽  
Vol 126 ◽  
pp. 106348 ◽  
Author(s):  
Zhen Liu ◽  
Nathalie Japkowicz ◽  
Ruoyu Wang ◽  
Yongming Cai ◽  
Deyu Tang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Extraction Method ◽  
System Call ◽  
Feature Extraction Method ◽  
Statistical Pattern
Sign in / Sign up

Export Citation Format

Share Document