Applying an Ontology to a Patrol Intrusion Detection System for Wireless Sensor Networks

Abstract

With the increasing application of wireless sensor networks (WSN), the security requirements for wireless sensor network communications have become critical. However, the detection mechanisms of such systems impact the effectiveness of the entire network. In this paper, we propose a lightweight ontology-based wireless intrusion detection system (OWIDS). The system applies an ontology to a patrol intrusion detection system (PIDS). A PIDS is used to detect anomalies via detection knowledge. The system constructs the relationship of the sensor nodes in an ontology to enhance PIDS robustness. The sensor nodes preload comparison methods without detection knowledge. The system transfers a portion of the detection knowledge to detect anomalies. The memory requirement of a PIDS is lower than that of other methods which preload entire IDS. Finally, the isolation tables prevent repeated detection of an anomaly. The system adjusts detection knowledge until it converges. The experimental results show that OWIDS can reduce IDS (intrusion detection system) energy consumption.

An Ontology Development for Anomaly Detection in File Integration Domain

“…Hsieh ve diğerlerinin sunduğu çalışmada ise [13], kablosuz sensor ağları (wireless sensor networks -WSN) için ontoloji tabanlı bir saldırı tespit sistemi önerilmektedir. Çalışmada, bir saldırı tespit bilgi tabanı oluşturularak WSN tabanlı iletişim sistemlerindeki anomalilerin tespit edilmesi hedeflenmektedir.…”

Quantal Response Equilibrium-Based Strategies for Intrusion Detection in WSNs

“…(3) Do UNTIL the end of interactions between players Attacker and the corresponding IDS agent (4) Woken by Monitored Events; (5) Judge whether Monitored Events are normal or malicious with the known intrusion detection techniques; (6) IF the output of detection is malicious THEN 7IF the RIDG is not existed THEN (8) Construct the first stage RIDG with game parameters including N, A, and U; (9) ELSE (10) Obtain the current stage RIDG from the Stored Game Data; (11) ENDIF (12) Compute according to (6); (13) Compute ( ) according to (4) and store it into the Stored Game Data for the next stage RIDG; (14) Combine IDS results and , and send them to Administrator; (15) ENDIF (16) ← + 1; (17) ENDDO Algorithm 1: QRE-based intrusion detection algorithm for IDS agents.…”

“…One of the major issues that we must face is how to guarantee security of WSNs before they are widely applied. Similar to traditional networks, there are to realize secure WSNs and prevention-and detection-based mechanisms [2][3][4][5][6][7][8]. The prevention-based mechanism, which aims to prevent any attack before it occurs, includes cryptography, key management, and authentication.…”

Using Ontologies to Model Attacks in an Internet based Mobile Ad-hoc Network (iMANET)

“…Hseih [7] used ontologies in a patrol detection system. Their proposed system has lower energy consumption than traditional Intrusion Detection Systems (IDS) and requires less memory.…”