Su Man Nam

A Security Method for Multiple Attacks in Sensor Networks: Against False-Report Injection, False-Vote Injection, and Wormhole Attacks

In a large-scale wireless sensor network, damage spreads rapidly in the network when under false report injection, false votes injection, or wormhole attacks. These attacks cause finite energy resources to be drained, legitimate reports to be dropped, and data to be intercepted by adversary nodes. A probabilistic voting-based filtering scheme (PVFS) and localized encryption and authentication protocol (LEAP) can be used to cope with these attacks. When multiple attacks occur simultaneously, PVFS and LEAP should be operated together. But the concurrent application of PVFS and LEAP provides inefficient duplications of operations in the sensor network. In this paper, we propose a security method which improves the energy efficiency while maintaining the security level of applying PVFS and LEAP simultaneously. The proposed method was designed by identifying and eliminating the redundancies of employing both methods together and providing more efficient functionalities. Four types of new keys were also designed for simultaneous detection of multiple attacks. We evaluated the effectiveness of the proposed method compared to simply applying PVFS and LEAP simultaneously when under multiple attacks. The experimental results demonstrate that our proposed method saves energy by up to 11% while maintaining detection power.

A Method to Select Next Hop Node for Improving Energy Efficiency in LEAP-Based WSNs

In wireless sensor networks, sensors have stringent energy and computation requirements as they must function unattended. The sensor nodes can be compromised by adversaries who attack network layers such as in sinkhole attacks. Sinkhole attacks have the goal of changing routing paths and snatching data surrounding the compromised node. A localized encryption and authentication protocol (LEAP) observes different types of messages exchanged between sensors that have different security requirements to cope with the attack. Even though this original method excels in security communication using multiple keys, the data is transmitted without optimal selection of the next nodes. In this paper, our proposed method selects the optimal next node based on a fuzzy logic system. We evaluated the energy and security performances of our method against sinkhole attack. Our focus is to improve energy efficiency and maintain the same security level as compared to LEAP. Experimental results indicated that the proposed method saves up to 5 % of the energy while maintaining the security level against the attack as compared to LEAP.

Improvement of energy consumption and detection power for PVFS in wireless sensor networks

In wireless sensor networks, adversaries can harm the sensor nodes by launching attacks such as the false report injection and the false vote injection. These attacks drain finite energy resources and drop legitimate event information in the sensor network. A probabilistic voting-based filtering scheme (PVFS) detects fabricated votes at the verification cluster-heads (CHs) while forwarding event reports. This paper presents a genetic algorithm-based PVFS to select effective verification CHs before transmitting the reports from a source CH. The proposed method determines the verification CHs based on the remaining energy level, the number of filtered votes, and the hop counts. The effectiveness of the proposed method is evaluated against the original PVFS in case of simultaneous multiple attacks. Our experimentation results indicated that the method results in an energy saving of approximately 10% and improves security against false report injection attacks and false vote injection attacks by 7% and 20%, respectively.

Fuzzy Logic based Next Hop Node Selection Method for Energy Efficient PVFS in WSN

ABSTRACTSensor nodes are easily compromised by attacker when which are divided in open environment. The attacker may inject false report and false vote attack through compromised sensor node. These attacks interrupt to transmission legitimate report or the energy of sensor node is exhausted. PVFS are proposed by Li and Wu for countermeasure in two attacks. The scheme use inefficiency to energy of sensor node as fixed report threshold and verification node. In this paper, our propose the next neighbor node selection scheme based on fuzzy logic system for energy improvement of PVFS. The parameter of fuzzy logic system are energy, hops, verification success count, CH select high the next neighbor node among neighbor nodes of two as deduction based on fuzzy logic system. In the experimental, our proposed scheme was improvement to energy of about 9% compare to PVFS. Key words : Wireless sensor network, Probabilistic voting-based filtering scheme, Fuzzy logic system요 약무선 센서 네트워크에서 센서 노드들은 개방된 환경에 배치되기 때문에 공격자들을 통해 쉽게 훼손된다 . 공격자는 훼손된 노드를 통해 허위 보고서 및 허위 투표 주입 공격을 할 수 있다 . 이러한 공격은 센서 노드의 에너지를 고갈시키거나 정상 보고서의 전송을 막는다 . 이 두 가지 공격에 대응하기 위해 Li와 Wu는 확률적 투표 기반 여과 기법을 제안하였다 . 이 기법은 보고서 임계값과 검증 노드를 고정적으로 사용하기 때문에 센서 노드의 에너지를 비효율적으로 사용한다 . 본 논문에서는 PVFS의 에너지 향상을 위해 퍼지 로직 시스템을 기반으로 다음 이웃 노드 선택 방법을 제안한다 . 퍼지 로직 시스템의 매개변수들은 에너지, 홉의 수, 검증 성공 횟수이며, CH는 퍼지 로직 시스템을 기반으로 도출된 2개의 이웃 노드 중에서 상태 정보가 높은 다음 이웃 노드를 선택한다 . 실험을 통해 제안 기법은 기존 기법과 비교하여 약 9%의 에너지가 향상되었고, 센서 노드들의 에너지 절감을 통해 전체 네트워크의 수명 연장을 기대한다 .주요어

A Method for the Enhancement of the Detection Power and Energy Savings against False Data Injection Attacks in Wireless Sensor Networks

Malicious attackers spread various attacks to destroy the system of the sensor network. False report injection attacks occur on the application layer and drain the energy resources of each node. Statistical en-route filtering (SEF) is proposed to detect and drop false reports in intermediate nodes during the forwarding process. In this work, we propose a security method to improve the detection power and energy savings using four types of keys. The performance of the proposed method was evaluated and compared to that of SEF against the attack. Our experimental results reveal that our method improves detection power and energy savings by up to 25% and 9%, respectively.