Ballot-stuffing Attacks Research Articles

A Design of Improved Trust-based Clustering Approach to Enhance the Energy Computation in Secured Internet of Things (IoT)

The Internet of Things (IoT) is the trending area that occupies maximum of the applications of intelligent communication. The nodes in the IoT system are located in the monitoring environment to attain stable and reliable communication. In between the nodes and the sink, similar data is sensed and transmitted at maximum of the period and that leads to high energy consumption. On the other hand, it increases the security issues of the network. So the main objective is reducing power utilization and increasing the IoT network security. In this paper, we suggest an improved trust-based clustering approach to enhance energy computation and security (ITCES-IoT) in IoT networks which can able to reduce energy consumption and increase the security among the nodes in the network. We first introduced an effective system model that can able to reduce the transmitting and receiving power during communication. Secondly, the initial cluster setup phase, LEACH-based cluster head (CH) selection phase, and re-clustering principles are discussed. Then, the trust calculation is established for both the cluster members (CM) and the CH through direct and indirect trust calculation processes followed by the best solution of path selection is performed between the CM and the sink node. We constructed Bad mouthing attacks, Ballot stuffing attacks, and detection mechanisms with a Fast Entropy algorithm. Finally, intra-cluster and inter-cluster communication are elaborated in the communication phase. Through this process, the nodes and CHs can able to perform effective communication with minimum power utilization, and the nodes with the transmitted information are secured from the external environment. At the end stage, we validate our proposed ITCES-IoT approach in certain scenarios in network simulator NS2 and their performance analysis includes the parameters of packet delivery ratio, network throughput, and packet loss and energy consumption. Then the final outcomes are compared with the base methods such as ECET-IoT, DRTP-IoT, and RCDA-IoT. From the results it is understood that the proposed ITCES-IoT outperforms the baseline methods in terms of delivery ratio and throughput and as well it consumes less power at the time of data transmission.

TR-SDTN: Trust Based Efficient and Scalable Routing in Hostile Social DTNs

A delay tolerant network (DTN) presents a new communication model, which uses a store-carry-forward approach to deliver messages due to the nature of the network. With the development of the mobile internet, the research on DTNs becomes a hot topic. One critical issue in the DTNs with social characteristics is that there could exist selfish and malicious nodes which block important data and as a result intentionally disturb the communication. Although there are many research works which address the issue and adopt various trust models to encourage nodes to forward information, most of them have not considered the social characteristics of the mobile nodes and they have not handled the selfish behaviors and attacks in a systematic way. In view of these defects, we propose a novel solution to address the selfish and security issues in social DTNs. Based on nodes’ social characteristics, a dynamic trust model is proposed to prevent bad-mouthing and ballot stuffing attacks and the Shannon entropy function is introduced to avoid blackhole and greyhole attacks. The simulation results show that our proposed scheme can significantly improve the performance of social DTNs. And the simulation is proved to be consistent with the results from theoretical analysis.