An Enhanced Trust Mechanism with Consensus-Based False Information Filtering Algorithm against Bad-Mouthing Attacks and False-Praise Attacks in WSNs

Abstract

To defend against insider attacks in wireless sensor networks (WSNs), trust mechanisms (TMs) using the notion of trust in human society have been proposed and are still actively researched. In the WSN with a trust mechanism (TM), each sensor node evaluates the trustworthiness of its neighbor sensors based on their behaviors, for example packet forwarding, and collaborates only with trustworthy neighbors while removing untrustworthy neighbor from its neighbor list. The reputation system (RS) is an advanced type of trust mechanism that evaluates the trustworthiness of a node by additionally considering neighbor nodes’ observations or evaluations about it. However, intelligent inside attackers in WSNs can discover the security vulnerabilities of trust mechanisms by examining the operations of TM (or RS), because the software modules of the TM (or RS) are installed and operating in their local storage and memory, and thus, they can avoid detection by the trust mechanisms. Bad-mouthing attacks and false-praise attacks are well-known examples of such intelligent insider attacks. We observed that existing trust mechanisms do not have effective countermeasures to defend against such attacks. In this paper, we propose an enhanced trust mechanism with a consensus-based false information filtering algorithm (TM-CFIFA) that can effectively defend against bad-mouthing attacks and false-praise attacks. According to our experiment results, compared with an existing representative RS model, our TM-CFIFA shortened the detection time of a packet drop attacker, which is supported by a false-praise attacker by at least 83%, and also extended the lifetime of a victim sensor node that is under bad-mouthing attacks by at least 15.8%.

Machine Learning to Ensure Data Integrity in Power System Topological Network Database

“…The database manipulation attack can be performed as a form of insider attack [21]. Alternatively, similar to the 2015 Ukraine power grid cyber-attack [18], an attacker can seize SCADA under control and manipulate the energy system database to falsify the operational decisions.…”

“…During a topological database attack, the attacker may or may not have prior knowledge of the topological connectivity or associated configuration information. For example, an insider with the knowledge of the power system DB connectivity, structure of the database and electric parameter information [21] may launch an attack . In the case when the attacker is not an insider, the adversary may gain the access externally and launch structured (has domain knowledge) or unstructured attacks (lack of domain knowledge).…”

HyperLedger Fabric-Based Proactive Defense against Inside Attackers in the WSN With Trust Mechanism

“…In addition, there are three representative types of consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS) [41]. A consensus algorithm in the blockchain refers to a procedure through which all the peers in the network can reach a common agreement on the states of certain data among distributed nodes [37,41]. There are many studies on advancing existing consensus algorithms in terms of security, fault tolerance, or efficiency [43,44]; in [43], authors proposed a PoS-based consensus protocol in which a meta node can maintain a consistent sub-chain in the Bitcoin blockchain.…”

“…The evaluated trust rating is encrypted and broadcasted to their neighbor nodes. In [37], the authors proposed Consensus-based False Information Filtering Algorithm (CFIFA) to show better defense performance against bad-mouthing and false-praise attacks than an existing trust mechanism. In CFIFA, inaccurate or malicious observations information can be filtered out by a consensus algorithm based on a majority voting method, and thus overall trust evaluation process can be guaranteed.…”

“…As a promising defense mechanism against inside attackers in WSNs, the Trust Mechanism (TM) using the notion of trust in human society has been actively proposed [5,22,33,34]. In general, TM works in the following three phases as follows [11,20,33,[35][36][37] [11,35,39]. For example, when the beta trust model [33] is used to evaluate the trust value (T) based on packet forwarding behaviors, T can be measured as…”