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Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine Learning for developing an IDS can make it more powerful. This work aims at evaluating the performance of machine learning models for Intrusion Detection System built based on CART Algorithm, ID3Decision tree algorithm, Random forest algorithm with Convolutional Neural Network. The model is built using KDD Cup99 Dataset. In this work we perform Linear Correlation and Mutual Information feature selection algorithms for ML algorithms and for Convolutional Neural Network algorithm being a deep learning technique feature selection happens automatically. Analysing various parameters including F1-score, ID3 has shown better results. [ABSTRACT FROM AUTHOR]

ISO 14001 is a set of standards to help organizations for constructing an environmentally building or rooms. In the campus environment, the ISO 14001 is used as the guide for creating an environmentally laboratory. Based on this ISO, the environmental condition of the laboratory need to be checked periodically. Recently, IoT devices were developed as the tools to monitor the ecological status of the laboratory. However, the recent study of IoT-based monitoring is used only for collecting the data. The data is being processed manually on the server. In our research, we try to implement machine learning for the IoT-based monitoring tools to improve the performance and gives the capability to respond to any condition of the laboratory. In this paper, we discussed our proposed design of IoT-based monitoring devices. We used a microcontroller module called NodeMCU ESP8266 to build an efficient monitoring system. ESP8266 is a type of microcontroller board equipped with a WiFi module to make it possible to design a system that can send data from multiple sensors (temperature, humidity, light intensity, CO2 concentration) to be displayed and sent to the database server using the WiFi module. The data collected in the database will be processed using machine learning by the Classification and Regression Tree (CART) algorithm and then implemented to the microcontroller as embedded machine learning to detect impending early threats and provide early warnings. With this method, it has been found that the CART algorithm provides a speedy processing time with training and testing time of 0.5 seconds and 0.06 seconds, the precision of 0.999154, recall of 0.999946, and f1-score of 1.0. We also design our device to be compact to be placed anywhere inside the laboratory and consume low-power. [ABSTRACT FROM AUTHOR]