Defending the state is not only the obligation of the TNI and Polri but the responsibility of all people, especially the millennial generation. As the nation's next generation, millennial youth have a great responsibility to determine the nation's future. State defense is an attitude and behavior that must be possessed by millennial youth to ensure the survival of the nation and state. Awareness and inculcation of the values of defending the country are considered the best preventive concepts to fortify millennial youth to always have a sense of love, pride and loyalty to NKRI in the midst of today's developments. The purpose of this study includes how to strengthen awareness of defending the country in the millennial generation. This research is a qualitative research with a descriptive approach. The results of this study are that it is necessary to inculcate the values of defending the state for millennial youth through formal and informal school facilities, lectures or examples by involving religious leaders or fighters, state defense training, seminars and FGDs, communication and information media/social media/internet media. by using struggle films, short videos, national songs, educational programs. In addition, awareness of defending the country can be raised by building national insight.
Abstract-DataMining algorithm which is applied as an anomaly detection system has been considered as one of the essential techniques in malicious behaviour detection. Unfortunately, such detection system is known for its inclination in detecting a cyber-malicious activity more accurately (i.e. maximizing malicious and non-malicious behaviours detection) and has become a persistent limitation in the deployment of intrusion detection systems. Consequently, these constraints will affect a number of important performance factors such as the accuracy, detection rate and false alarms. In this research, KMDT proposed as an anomaly detection model that utilized kmeans clustering and decision tree classifier to maximize the detection of malicious behaviours by scrutinizing packet headers. The k-means clustering employed for labelling and plots the whole behaviours into identical cluster, which characterized the behaviours into suspicious or non-suspicious composition. Subsequently, these dissimilar clustered behaviours are reordered within two classes of types such as malicious and nonmalicious via decision tree classifier. KMDT is a profitable finding which improved the anomaly detection performance in identifying suspicious and non-suspicious behaviours as well as characterizes it into malicious and non-malicious behaviours more accurately. These criteria have been validated by the result from the experiments throughout banking system environment dataset 2016. KMDT have detected more malicious behaviours accurately as contrast to discrete and diversely combined methods.
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