Reputation-based Security model for detecting biased attacks in BigData

Abstract

As internet of things (IoT) devices are increasing since the emergence of these devices in 2010, the data stored by these devices should have a proper security measure so that it can be stored without getting in hands of an attacker. The data stored has to be analyzed whether the data is safe or malicious, as the malicious data can corrupt the whole information. The security model in BigData has many challenges such as vulnerability to fake data generation, troubles with cryptographic protection, and absent security audits. As cyberattacks are increasing the main objective of each organization is to secure the data efficiently. This paper presents a model of reputation security for the detection of biased attacks on BigData. The proposed model provides various evaluation models to identify biased attack in malicious IoT devices and provide a secure communication metric for BigData. The results show better rates in terms of attack detection rate, attack detection failure rata, system throughput and number of dead nodes when the attack rate is increased when compared with the existing reputation-based security (ERS) model. Moreover, this model reputation-based biased attack detection (RBAD) increases the security of the IoT devices in the BigData and reduces the biased attack coming from various malicious nodes.

A secure framework for effective workload resource management

“…Further, the base station maintains a buffer to keep the past and present reputation value of all the IoT devices. In similar manner to [25], the work uses a local i.e., direct association reputation and global i.e., indirect association reputation and compare the local and global reputation to assure accuracy and reliability by eliminating IoT device that provide wrong reputation for personal workflow execution benefit. In this work malicious activities are introduced into the network by creating attacks like DDOS and on-off attacks using NSL-KDD dataset [30] on certain IoT device in random manner.…”

“…Where, ℂ o u (x, y) is the present reputation, 𝕃 o u (x, y) is the past reputation of the IoT nodes and α is used for denoting how the reputation for a IoT node can be attained using the past reputation. The ℂ o u (x, y), 𝕃 o u (x, y) and α have been evaluated using [25].…”