Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices

Abstract

The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the cloud for analysis. Lately, the idea of moving intelligence to the IoT is becoming feasible, with machine learning (ML) moved to edge devices. The aim of this study is to provide an experimental analysis of processing a large imbalanced dataset (DS2OS), split into a training dataset (80%) and a test dataset (20%). The training dataset was reduced by randomly selecting a smaller number of samples to create new datasets Di (i = 1, 2, 5, 10, 15, 20, 40, 60, 80%). Afterwards, they were used with several machine learning algorithms to identify the size at which the performance metrics show saturation and classification results stop improving with an F1 score equal to 0.95 or higher, which happened at 20% of the training dataset. Further on, two solutions for the reduction of the number of samples to provide a balanced dataset are given. In the first, datasets DRi consist of all anomalous samples in seven classes and a reduced majority class (‘NL’) with i = 0.1, 0.2, 0.5, 1, 2, 5, 10, 15, 20 percent of randomly selected samples. In the second, datasets DCi are generated from the representative samples determined with clustering from the training dataset. All three dataset reduction methods showed comparable performance results. Further evaluation of training times and memory usage on Raspberry Pi 4 shows a possibility to run ML algorithms with limited sized datasets on edge devices.

Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

“…erefore, the parts that should belong to the background, such as the audience and the field line, are also mistaken for the moving target. In addition, a tennis video consists of multiple shots, the shots are frequently changed, and the number of frames in a single shot or in a scene is not enough, so the update cycle of the background is not long enough, and the situation shown in Figure 5(a) often occurs (the game scene appears before the close-up shot is updated), which is also one of the reasons for the inaccuracy of the background image [20].…”

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

“…In 2021, Huˇc et al [16] proposed an anomaly detection model for edge devices. In the present paper, authors have analyzed different machine learning algorithms and evaluated the performance of the proposed intrusion detection model on a largely imbalanced dataset 'DS2OS'.…”

“…Confusion matrix has been shown in figure 13 The proposed Ensemble-based model for IDS shows remarkable performance in terms of accuracy, TPR, and time efficiency. Only the models presented by Huˇc et al [16], Xu and Fan [20], and Mahamed et al [23] have calculated the time efficiency of their models. The model proposed in the present paper shows outstanding performance.…”

How to Effectively Collect and Process Network Data for Intrusion Detection?

“…As the name suggests, this algorithm consists of multiple decision trees that form a forest. This tree-based algorithm has been utilized in many IDS and research papers; some examples of its use and effectiveness can be found, e.g., in [62][63][64]. The model is trained using bagging (bootstrap aggregation) techniques.…”