## 2 Background and Related Works
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Article** & **Details** \\ \hline [PERSON] et al. [18] & Parameters: IP and Port addresses of Source \& Destination \\  & Validation Technique: Simulation \\  & Dataset: No dataset used \\  & Detection Metrics: Entropy variation \\  & Limitations: When the traffic increases the performance degrades low efficiency \\ \hline [PERSON] et al. [22] & Parameters: Source IPs distribution, access intent, traffic rate \\  & Validation Technique: Simulation \\  & Dataset: \(DDoS\longrightarrow MITLincoln\&FC\longrightarrow HTTPlogs\) \\  & Detection Metrics: Correlation coefficient total variation \\  & Limitations: Degrade for high-rate DDoS attack \\  & Only flow similarity is not suitable \\ \hline [PERSON] et al. [11] & Parameters:Variation in the source addresses and traffic rate, packets scattering among source addresses \\  & Validation Technique: Real-time \\  & Dataset: \(DDoS\longrightarrow CAIDA2007\&FC\longrightarrow 1998 FIFAworld-cup\) \\  & Detection Metrics: Statistical calculation \\  & Limitations: Not have any accuracy \\ \hline [PERSON] et al. [8] & Parameters: Flow similarity, client legitimacy, page referred \\  & Validation Technique: Simulation \\  & Dataset: \(DDoS\longrightarrow CAIDA2007\&FC\longrightarrow 1998 FIFAworld-cup\) \\  & Detection Metrics: Hellinger distance \\  & Limitations: Accuracy is 91\%. \\  & Environment specific \\ \hline [PERSON] et al. [23] & Parameters: Source entropy \& traffic entropy \\  & Validation Technique: Simulation \\  & Dataset: No Dataset Used \\  & Detection Metrics: Entropy \\ \hline [PERSON] et al. [25] & Parameters: Source \& destination IPs, source \& destination port \\  & Validation Technique: Simulation \\  & Dataset: DDoS and FC datasets generate through Scapy tool \\  & Detection Metrics: General entropy \& generalized information distance \\ \hline [PERSON]. [PERSON] et al. [24] & Parameters: Time interval, source/destination IPs \\  & Validation Technique: Simulation \\  & Dataset: CAIDA 2007 for DDoS attack \& 1998 FIFA World Cup for FC \\  & Detection Metrics: Shannon entropy, Mahalanobis distance, kernel online anomaly detection \\ \hline [PERSON] et al. 2017 [15] & Parameters: Structural, such as temperature, humidity, light intensity, and voltage; Validation technique: Simulation; Dataset: IBRL dataset with manual entries; Target: Detection of abnormal structural events \\ \hline [PERSON] and [PERSON], 2020 [16] & Parameters: Packet size, port number, source address, destination address, and jitter; Validation Technique: Simulation; Dataset: None; Target: DDoS detection and mitigation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related articles. In the existing research, most of the mechanisms consider the data flow to detect abnormal traffic. Most articles consider the abnormal traffic as the DDoS attack, while the discrimination of DDoS from FC is limited in current literature. Some authors have used mechanisms that may overload the tiny sensor nodes in WSNs. Many have not used any procedure involving the testing and validation based on datasets. The proposed work introduces a novel technique of a probabilistic model based on naive classification, which not only detects the abnormal traffic but also identifies it as a DDoS or an FC. For validation of the work, two datasets having records for FC and DDoS are also used in the proposed mechanism.