Performance Evaluation of Machine Learning for Prediction of Network Traffic in a Smart Home

A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior

“…The results demonstrate that the system, for the first tier experiment CSE-CIC-IDS2018 in Figure 5, the K-nearest neighbors was recognized as the most successful algorithm with an average accuracy rate of 95.9% [26]. Random forest was identified as the second most accurate with an average rate of 95.7% [26]. Other algorithms also earned strong accuracy relative to K-nearest neighbors and random forest.…”

“…They are random forest, Xgboost, decision tree, and K-nearest neighbors on The results show that our models for each algorithm can effectively achieve seemingly satisfactory classification accuracy with the lowest false positive [26].…”

“…In the context of a sensor network, the smart home as a distributed environment shows the generic features of unreliability, which creates problems for behavior prediction. Security methods that rely heavily on encryption are not standard on these resource-constrained devices because encryption and decryption are complicated operations that require several computations [1] [26]. Even if activated correctly, the malfunctioning condition of sensors may not produce a trigger event.…”

“…The first tier is for intrusion detection systems using machine learning algorithm. The machine learning algorithm is an efficient data mining algorithm that can be used for real-time network intrusion detection [1] [26]. The second tier is the misuse detection technique that applies a known set of user activity patterns.…”

“…This phase aims to examine all requests coming to the smart home system using machine learning. We used and compared four types of machine learning algorithms [26].…”