Probabilistic Voting-based Filtering Scheme Research Articles

MODELING AND SIMULATION OF THRESHOLD ANALYSIS FOR PVFS IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) suffer serious damage from false positive and negative attacks due to their hardware restrictions. The sensor network causes both unnecessary energy consumption and information loss through false reports and normal reports, which include false message authentication codes (MACs). A probabilistic voting-based filtering scheme (PVFS) effectively detects the two types of attacks through a pre-defined threshold, which is the number of detected false MACs in a report. Since the threshold significantly influences the ability to detect attacks, the sensor network should be simulated to ensure proper function. In this paper, we describe the development and simulation of a PVFS-based WSN using a discrete event system specification. The experimental results showed that PVFS with a threshold of 2 reduced energy usage by about 16% and improved the detected false reports as compared with a PVFS with a threshold of 3.

GAFS: GENETIC ALGORITHM-BASED FILTERING SCHEME FOR IMPROVING DETECTION POWER IN SENSOR NETWORKS

Wireless sensor networks (WSNs) have stringent energy and computational requirements. Security has become very crucial issue with the widespread acceptance of the WSNs in numerous decision-critical and hostile environments. Since sensor nodes are left unattended, they can be compromised by adversaries to launch various application layer attacks. Effective countermeasures against these attacks can lead to improved security. A probabilistic voting-based filtering scheme (PVFS) uses probabilistic filtering based on the distance to counter attacks of fabricated reports with false votes and real reports with false votes. Genetic algorithm-based filtering scheme (GAFS) uses a genetic algorithm with a fuzzy rule-based system that considers remaining energy and number of filtered votes in addition to the distance. The analysis results of the current study demonstrate the effectiveness of our scheme against these attacks in comparison with PVFS. The results show increased detection power achieved through effective verification while maintaining energy consumption.

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.

ENSP: Energy Efficient Next Hop Selection in A Probabilistic Voting-Based Filtering Scheme Using Fuzzy Logic

ENSP: Energy Efficient Next Hop Selection in A Probabilistic Voting-Based Filtering Scheme Using Fuzzy Logic

Fuzzy-Based Energy Efficient Method for Multiple Attacks in Sensor Networks: Against False Vote and Report Injection Attacks

An adversary can easily compromise sensor nodes in wireless sensor networks, and generate multiple attacks through compromised nodes, such as false vote injection attacks and false report injection attacks. The false vote injection attack tries to drop legitimate reports in an intermediate node, and the false report injection attack tries to drain the energy consumption of each node. To prevent these attacks, a probabilistic voting-based filtering scheme (PVFS) has been proposed to select verification nodes, and to detect fabricated votes in the reports as they occur simultaneously. In this paper, we propose a method that improves the energy efficiency of each node and thesecurity level of the false report injection attack, while maintaining the detection power of the false vote injection attack. Our proposed method effectively selects verification node with considering the conditions of each node, based on a fuzzy rule -based system. The verification node is decided through the energy remaining level, distance level, and number of detected false votes in the fuzzy system. We evaluated the effectiveness of the proposed method, as compared to PVFS, when two attacks occur simul taneously in the sensor network. The experimental results show that our method saves energy by up to 8%, by improving and maintaining the defence against these multiple attacks.

PVFS: A Probabilistic Voting-based Filtering Scheme in Wireless Sensor Networks

In this paper, we study two important attacks in Wireless Sensor Networks (WSNs): the fabricated report with false votes attack; the false votes on real reports attack. Most of the existing works address the first attack while leaving an easy way for the attackers to launch the second. We draw our motivation from this and propose a Probabilistic Voting-based Filtering Scheme (PVFS) to deal with both of these attacks simultaneously. With the general en-route filtering scheme as the underlying model, PVFS combines cluster-based organisation, probabilistic key assignment, and voting methods to curtail these attacks. Through both analysis and simulation, we demonstrate that PVFS can achieve strong protection against both the aforementioned attacks while maintaining a sufficiently high filtering power.

AN EN-ROUTE FILTERING METHOD IN SENSOR NETWORKS USING DECISION FUNCTION

Security in sensor networks is a major issue. Sensor networks use symmetric cryptography protocol since sensor nodes have resource constrained hardware. Such netowrks are also deployed in hostile environments. Therefore, an attacker can get all information after any nodes get compromised. The adversary can inject false sensing reports or false Message Authentication Codes into real reports. A probabilistic voting-based filtering scheme is proposed but in several cases it is inefficient in terms of energy consumption and filtering effectiveness. We proposed a new method that uses a decision function regardless of whether each forwarding node executes a verification process. Through performance analysis and simulation, our result shows that the proposed method is much more efficient than the probabilistic voting-based scheme in many cases.