# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 2 Related Literature
### Watchdog and Pathrater Schemes  
[PERSON] et al. [6] used an intrusion detection system AODV and a watchdog AODV scheme to simulate a blackhole attack in MANETs. They saw security as a combination of procedures, processes, and systems used to ensure authentication, integrity, access control, nonrepudiation, and confidentiality. Their focus on security was based on the growing demand to support live streaming traffic military and civilian applications. AODV had a decreased performance in simulation with a blackhole attack. Their result shows that a watchdog IDS trust management solution for preventing blackhole attack was better than other proposed solutions. The watchdog had the advantage of listening to the nodes within the network transmission range and constantly monitoring packet delivery amongst the nodes. If a packet is not delivered within a specific time range or is forwarded but now altered by its neighbor, then the neighbor is deemed as misbehaving and the watchdog will declare the node as a gray hole node and exclude it from the path of sending packets [6]. In contrast to the IDS AODV, the authors in [7] proposed an infiltrator detection system for node isolation attacks (DoS) against OLSR. Their solution is based on identifying the false information with a HELLO message using the infiltrator identification system. The performance of the proposed algorithm is tested in terms of network throughput and packet delivery ratio. The proposed algorithm outperformed the standard version of OLSR on the tested parameters. Their algorithm was only tested for a 3 mobile node-scenario and two QoS measures, thus its effectiveness cannot be judged based on 3 nodes analysis in a network scenario. Our research is evaluated in different node density environments to validate its robustness. [PERSON] et al. [8] proposed a multi-attribute trust framework for MANETs (MATF). The framework was designed to minimize bootstrapping time and also to tackle selective behavior. It was also designed to enhance the security of MANETs by enabling a node to identify and then quarantine malicious nodes from the routing paths. The proposed security framework was not only designed to detect malicious nodes and in time, but also to decrease the number of false positives. They used multi-watchdogs as a measure to achieve this [8]. Their work was based on a reactive routing protocol, AODV, for the detection of wormhole attacks and not OLSR. This aspect of QoS was not a factor in their evaluation process and it is central to this research work. In their work, [PERSON] et al. [9] analyzed, simulated, and performed a comparative study of three different attacks based on many parameters. These attacks were denial of service (DoS), black hole attacks, and wormhole for wireless ad hoc networks based on performance. Their comparison was based on packet delivery ratio (PDR), end-to-end delay, and throughput. The attacks had different properties, different behavioral patterns of attacks in different environments primarily because of their different designs. Their experiment concluded that the rate of data loss is less in wormhole than compared to the other two attacks. The best way to counterattack is to first know the behavioral patterns and properties of the attack itself, and this is what the authors did. This work, however, was not evaluated for sinkhole attacks. In their work, [PERSON] et al. [10] proposed a network routing and intrusion monitoring scheme for IoT-WSNs through a secured routing and intrusion detection models using OLSR and AOMDV protocols. Their scheme works periodically and reactively. [PERSON] et al. [11] developed an AI framework for IoT-based sensor networks that is capable of detecting DOS, misrouting, and identity theft using special space raising devices. In their research, [PERSON] et al. [12] developed a linear and static trust-based routing scheme that can detect varieties of attack using sensor nodes. Table 1 shows a summary of related work, their contribution and limitations of their study in comparison to our research.