# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 2 Related Work  
Cyberattacks against the IoT occur at many IoT stack levels, making the IoT a target for new assaults. In response, researchers have concentrated their efforts on creating different mechanisms to increase the security, productivity, and effectiveness of the IoT. To protect communications in the IoT network layer, ref. [14] suggested a trust-based routing protocol for identifying and segregating routing attacks in IoT networks. Similarly, an intrusion detection system based on a deep neural network was proposed in [15]. It identifies internal assaults on medical IoT networks. A multi-agent model was proposed using deep learning in [16]. Many researchers have extensively studied the vulnerabilities of RPL, including various internal attacks, for instance, Sybil, rank, sinkhole, version number, blackhole, neighbor, DIS, DAO, and selective-forwarding attacks [14; 17; 18; 19]. (DIS and DAO are explained in Section 3.1.2). The authors in [20] designed a cryptography-based security measure to mitigate rank and version-number assaults using a hash chain, and malicious nodes were removed from the network using this rank chain. In the approach suggested by [21], all network nodes are registered with a security organization. The frequent demise of nodes was addressed by introducing a mobile sink. To mitigate rank attacks, before any grid member node can send data to the grid head, it must be authenticated. A different mechanism was proposed in [22] based on layering to defend RPL against a rank assault. The suggested approach has three stages: (1) Nodes are classified into layers. (2) Route trust values are computed. (3) Rank attacks are identified and isolated. A sinkhole attack against RPL was detected using ranking and rating techniques in [23]. They calculated distances to determine the rank and rate of each node. Additionally, the average packet transmission route request (APT-RREQ) value identifies nodes in the IoT network behaving oddly. [PERSON] and [PERSON] [24] proposed a collective trust mechanism for secure routing in IoT-based infrastructures. Their mechanism protects against rank and Sybil attacks. Although the proposed mechanism minimized energy consumption and increased detection accuracy and throughput, it incurred a high computational and message overhead. In [18], a time-based trust-aware routing protocol called SecTrust-RPL was proposed to mitigate rank and Sybil attacks. The proposed mechanism improved network performance and isolated internal attacks successfully. However, it ignored the energy overhead. [PERSON] et al. [23] proposed a novel protocol, SoS-RPL, for mitigating sinkhole attacks. It has two phases: (1) nodes are rated and ranked based on distance measurements and (2) misbehaving nodes and sources are identified based on APT-RREQ. However, the proposed mechanism did not reduce the message overhead and ignored the mobility of the network. [PERSON] et al. [25] proposed SybM to mitigate mobile Sybil attacks and safeguard RPL. They allowed for node mobility and handled flooding fake control messages. However, the proposed mechanism has a lower accuracy and detection rate than other state-of-the-art systems. [PERSON] et al. [26] proposed a trust-based mechanism for detecting rank and sinkhole attacks in multi-sink networks. It is an energy-efficient mechanism. However, they did not consider the message and computational overhead. Similarly, in [27], a robust hybrid and encryption-based mechanism was proposed to secure nodes in RPL-based infrastructures. The security mechanism has four phases. However, the computational and energy overhead was high. [PERSON] et al. [5] presented a mechanism using a Gini index to mitigate Sybil attacks in RPL-based LLNs. However, the proposed mechanism did not reduce the message overhead. [PERSON] and [PERSON] [28] proposed a dynamic trust-based mechanism called DCTM-IoT and an RPL-based Sybil attack mitigation system called DCTM-RPL. However, these proposed mechanisms have high message and energy overheads.