# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 2 Literature Review  
A multi-layer architecture for intrusion detection in WSNs proposed in [13] improved network security. In this architecture, the sensors are positioned within the initial layer, situated at the periphery of the network. The subsequent layer is positioned at the network's core. The researchers employed the I Bayes method, known for its straightforward and efficient computational approach, as the core classifier. The initial layer's role involves categorizing observed data as normal or potentially harmful without delving into specific attack patterns. The intermediate detection layer resides within the cloud and exclusively processes anomalous activities. This positioning reduces resource constraints, allowing for the implementation of more intricate techniques and in-depth analysis. Subsequently, the identification of harmful traffic is accomplished using the random forest (RF) method alongside a multi-class classifier. This classification model can ascertain the nature of the attempted breach and offer recommendations for selecting an appropriate defense strategy. The results clearly indicate that their proposed multi-layer security approach led to improved true-positive rate (TPR), true-negative rate (TNR), false-positive rate (FPR), and false-negative rate (FNR) values. Additionally, a high level of accuracy was achieved. The obtained values were 100%, 90.4%, 99.5%, 97%, and 99.9% for normal, flooding, scheduling, gray-hole, and black-hole attacks, respectively. In comparison, previous research yielded values of 99.8%, 90.4%, 99.5%, 91.1%, and 73% for normal, flooding, scheduling, gray-hole, and black-hole attacks. The work done in [14] proposed a novel approach that combines an on-demand link and an energy-aware dynamic multipath (O-LEADM) routing system for mobile ad hoc networks (MANETs). This approach aimed to effectively detect black-hole nodes through the baiting strategy while also distinguishing between packet loss caused by congestion and packet loss caused by malicious nodes. The suggested technique demonstrates a notable capability to differentiate between packet drops that occur due to link failures and those triggered by malicious nodes. In their study, the researchers compared their O-LEADM-black-hole identification approach with the existing AODV-black-hole routing system. They then analyzed various metrics to evaluate the performance of both strategies. Specifically, with respect to packet-delivery ratio, the proposed O-LEADM approach surpasses the current AODV-black hole method. For instance, with 84% of nodes moving at a velocity of 20 m/s, the AODV-black hole strategy experiences a reduction to 83% as mobility velocity increases. In contrast, the O-LEADM-black-hole strategy maintains a delivery ratio of 87% even at 20 m/s. Furthermore, O-LEADM-black hole adjusts its delivery ratio as mobility speed increases, accounting for connectivity faults and channel accessibility in order to mitigate congestion and enhance route reliability. Examining average delay, the study revealed that the AODV-black hole approach exhibits an average delay of 0.083 M.Sec at higher mobility speeds. In comparison, the O-LEADM-black hole approach achieves a lower average delay of 0.050 M.Sec. The energy-consumption analysis (ECA) showrf that in the presence of an adversary, the OLEADM-black hole approach consumes an average of 6.0 Joul of energy when operating at higher speeds. On the other hand, the AODV-black hole approach expends 11.2 J of energy under similar conditions. The overhead of the O-LEADM-black hole strategy ranges between 4.1 and 11.1, whereas the AODV-black hole approach exhibits an overhead ranging between 5.9 J and 16.6 J. Notably, the O-LEADM-black-hole approach displays significant improvement in identifying other channels when a black-hole node is detected. This effectiveness is achieved with substantial movement and minimal expense.