# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 2 Literature Review
The investigation also evaluated patterns in network traffic and node behavior, aiming to quantify the impact of these attacks. To support digital forensic (DF) inquiries, the vulnerabilities inherent in the protocol were systematically scrutinized. The research involved the reconstruction of networks across various opera tional modes and parameters. This reconstruction served to validate the analysis conducted and to propose recommendations for the development of robust routing protocols. The overarching goal was to enhance understanding of IoT network performance in the face of black-hole attacks and to thereby contribute to the enhancement of more secure and resilient routing protocols. A study discussed in [24] introduced a robust path-selection mechanism tailored to enhance the security and reliability of WSNs. The core purpose was to proactively identify and counter black-hole attacks, thus averting the compromise of routing nodes. By optimizing the routing of data packets through more efficient paths, the primary aim was to extend the operational lifespan of sensor nodes within WSNs. To fulfill this objective, the research proposed a trust-based route-selection mechanism that ensures the integrity of data transmission by identifying dependable routes between source and destination nodes. The process involves generating a source node and facilitating secure data transmission to the intended recipient. A trust-based assessment detects malicious nodes by evaluating their trust values. Once a malicious node is detected and surpasses a predefined threshold, a different, secure routing path is established for data transmission. Throughout the data-transmission process, the trust-based path-selection technique is used, guiding the selection of secure routing paths. This strategy effectively safeguards the network against threats like black-hole attacks and selective forwarding, bolstering overall security and data integrity. The mechanism effectively integrates the trust system across various dimensions, ensuring the security of routing paths and the identification of optimal threshold values grounded in trust considerations. These aspects have a twofold impact: they influence the trust system's performance and shape the threshold-based trust parameters. To realize these objectives, the mechanism computes a distinct threshold value for each potential path. This computation aims to pinpoint the path with the lowest threshold value, denoting the most secure route for data transmission. Consequently, at each step of detection and data-packet transmission, sensor nodes can gauge their level of trust, mitigating potential threats like black-hole attacks and selective forwarding. In [25], an approach to enhance network security against potential attacks from internet traffic intruders was presented. The proposed solution aimed to strike a balance between the imperative for protection and the constraints posed by available resources and trust factors. To achieve this, the research introduced a ranking-based route mutation mechanism that leverages the bafflement technique for selecting optimal routes for flows within the network. These routes are chosen based on diverse considerations, ensuring robust security at the base station level by incorporating factors such as route overlap, energy consumption, and link cost. Moreover, this strategy supports multiple altered pathways that can confuse potential attackers, along with scrutiny algorithms tailored for fully centered WSNs. The technique involves strategically selecting multiple intruder sink nodes, which are designed to mislead attackers attempting to locate the true sink node. A suitable parameter is employed to account for the residual energy of neighboring nodes associated with the chosen intruder sink nodes. This parameter takes into consideration the expected additional communication cost within the corresponding region. Notably, this solution remains cost-effective and suitable for deployment in extensive sensor networks without imposing excessive expenses.