Many computer-based devices are now connected to the internet technology. These devices are widely used to manage critical infrastructure such energy, aviation, mining, banking and transportation. The strategic value of the data and the information transmitted over the Internet infrastructure has a very high economic value. With the increasing value of the data and the information, the higher the threats and attacks on such data and information. Statistical data shows a significant increase in threats to cyber security. The Government is aware of the threats to cyber security and respond to cyber security system that can perform early detection of threats and attacks the internet. The success of a nation's cyber security system depends on the extent to which it is able to produce independently their cyber defense system. Independence is manifested in the form of the ability to process, analyze and create an action to prevent threats or attacks originating from within and outside the country. One of the systems can be developed independently is Intrusion Detection System (IDS) which is very useful for early detection of cyber threats and attacks. The advantages of an IDS is determined by its ability to detect cyber attacks with little false. This study learn how to implement a combination of various methods of machine-learning to the IDS to improve the accuracy in detecting attacks. This study is expected to produce a prototype IDS. This prototype IDS, will be equipped with a combination of machine-learning methods to improve the accuracy in detecting various attacks. The addition of machine-learning feature is expected to identify the specific characteristics of the attacks occurred in the Indonesian Internet network. Novel methods used and techniques in implementation and the national strategic value are becoming the unique value and advantages of this research.

TOR (The Onion Router) TOR (The Onion Router) has been a very popular anonymous proxy service. Since its first usage, TOR has been improved and become a very big network consisting more than 7000 relays. Beside used by journalist, activist, and writer as a tool for their freedom of speech, its highly anonymous service is also used by bot-nets, malware, distributed denial of service attacks, hidden services that sells illegal things, spams, and many more. This paper will explain about a TOR usage detection system by using deep packet inspection to extract and analyze its network traffic. The result of the analysis will be used as a parameter for a proxy server to block TOR traffic in the network. Using this network, detection, and blocking design, we can block the Onion Router Traffic originated from TOR browser.

Wireless Body Area Network (WBAN) implements real time health monitoring by outfitting patients with wireless vital sign sensors. One of the parts of WBAN is glucometer sensor which determining the approximate concentration of glucose in the blood. In this paper, we develop glucometer sensor monitoring system based on WBAN by using e-Health sensor (glucometer sensor), arduino uno board, and ZigBee module. The experiment results show that the desktop-based, web-based, mobile-based applications can be used well bu using doctor/nurse to monitor the patient's glucose levels without having to see the patient directly.

Nowadays, the use of Electrocardiogram (ECG) as a detection tool of the condition of human heartbeat is still considered difficult. So, the idea of implementing this sensor is to enhance the quality of service and to minimize the cost of using and maintaining by determining the best way in implementing and monitoring ECG sensor. This research proposes a novel approach to observe the condition of human heartbeat using ECG sensor with e-Health sensor and Raspberry Pi, mini single-board computer with robust capability, which acts as central processing unit. Those hardware sets were assembled to collect ECG voltage signals in human body. The signals are transmitted into the device and the data are stored in MySQL database for long-term monitoring access. The experimental results show effectiveness of this approach as it is successfully fulfill the idea of Body Area Network (BAN).

Wireless Sensor Network (WSN) is a promising technology for many applications, such as industrial, environment, and health-care application. The first requirement of WSN is energy efficiency in order to increase the network node lifetime. The second is reliability because packet drop cannot be tolerated in critical time applications. In this paper, we use NS2 simulation to evaluate and analyze the IEEE 802.15.4 standard in multi-hop WSN with a tree topology base on IEEE 802.15.4. Beacon enabled mode is used slotted CSMA/CA and non-beacon enabled mode is used unslotted CSMA/CA. Performance of beacon enabled and non-beacon enabled were evaluated and analyzed to understand the impact of protocol parameters such as Superframe Order (SO), Beacon Order (BO), and Traffic Load. The performance of the network which have been evaluated in detail are the throughput, energy consumption, end to end delay, packet success probability, drop packets and the percentage of the energy efficiency. The simulation result show that of beacon enabled is better than non beacon enabled in terms of the throughput, drop packet, energy consumption, and energy efficiency.

Snort is a well-known, signature-based network intrusion detection system (NIDS). The Snort sensor must be placed within the same physical network, and the defense centers in the typical NIDS architecture offer limited network coverage, especially for remote networks with a restricted bandwidth and network policy. Additionally, the growing number of sensor instances, followed by a quick increase in log data volume, has caused the present system to face big data challenges. This research paper proposes a novel design for a cloud-based Snort NIDS using containers and implementing big data in the defense center to overcome these problems. Our design consists of Docker as the sensor’s platform, Apache Kafka, as the distributed messaging system, and big data technology orchestrated on lambda architecture. We conducted experiments to measure sensor deployment, optimum message delivery from the sensors to the defense center, aggregation speed, and efficiency in the data-processing performance of the defense center. We successfully developed a cloud-based Snort NIDS and found the optimum method for message-delivery from the sensor to the defense center. We also succeeded in developing the dashboard and attack maps to display the attack statistics and visualize the attacks. Our first design is reported to implement the big data architecture, namely, lambda architecture, as the defense center and utilize rapid deployment of Snort NIDS using Docker technology as the network security monitoring platform.