Improved temporal IoT device identification using robust statistical features.

The Internet of Things (IoT) is becoming more prevalent in our daily lives. A recent industry report projected the global IoT market to be worth more than USD 4 trillion by 2032. To cope with the ever-increasing IoT devices in use, identifying and securing IoT devices has become highly crucial for network administrators. In that regard, network traffic classification offers a promising solution by precisely identifying IoT devices to enhance network visibility, allowing better network security. Currently, most IoT device identification solutions revolve around machine learning, outperforming prior solutions like port and behavioural-based. Although performant, these solutions often experience performance degradation over time due to statistical changes in the data. As a result, they require frequent retraining, which is computationally expensive. Therefore, this article aims to improve the model performance through a robust alternative feature set. The improved feature set leverages payload lengths to model the unique characteristics of IoT devices and remains stable over time. Besides that, this article utilizes the proposed feature set with Random Forest and OneVSRest to optimize the learning process, particularly concerning the easier addition of new IoT devices. On the other hand, this article introduces weekly dataset segmentation to ensure fair evaluation over different time frames. Evaluation on two datasets, a public dataset, IoT Traffic Traces, and a self-collected dataset, IoT-FSCIT, show that the proposed feature set maintained above 80% accuracy throughout all weeks on the IoT Traffic Traces dataset, outperforming selected benchmark studies while improving accuracy over time by +10.13% on the IoT-FSCIT dataset.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability.

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).