Artificial intelligence semi supervised-based network intrusion detection system detects and identifies various types of attacks on network data using several steps, such as: data preprocessing, feature extraction, and classification. In this detection, the feature extraction is used for identifying features of attacks from the data; meanwhile the classification is applied for determining the type of attacks. Increasing the network data directly causes slow response time and low accuracy of the IDS. This research studies the implementation of wrapped-based and several classification algorithms to shorten the time of detection and increase accuracy. The wrapper is expected to select the best features of attacks in order to shorten the detection time while increasing the accuracy of detection. In line with this goal, this research also studies the effect of parameters used in the classification algorithms of the IDS. The experiment results show that wrapper is 81.275%. The result is higher than the method without wrapping which is 46.027%.
Nowadays, the technology development makes a human can create a tool which is used to recognize an object and it becomes a popular technology device. It happens because this tool has an important role for interaction between a human and a computer. One example of this technology usage is to recognize a hand gesture for controlling a home automation system. The existing of this technology creates the change related to how the human controls any tools in a house and it also reduces the complexity including an effort when it is used for controlling. This feature is very useful, especially for elderly people who stay in independent living. This study is going to develop a controller prototype by using FAST (Features from Accelerated Segment Test) algorithm to detect hand gesture for operating the connected home devices. This controller uses an embedded system to translate a command which is created by using the hand gesture of senior captured by the cam for controlling the lamps. The lamps itself are represented as several tools in the house. The observation gives a result that the hand gesture is potential to be implemented as a command for controlling the proposed system prototype in the range which is not far than 1 meter with the percentage average recognition accuracy is almost 80%.
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