Prototype Penerapan Internet of Things pada Sistem Informasi Penggunaan Air Rumah Tangga Di BLUD UPT SPAM Kabupaten Musi Rawas

Abstract—Water is an important element that becomes the need of every human being, in the service provider company Clean water control of water usage is still a problem because there is no system that provides information on the use of water on the customer side, especially in the BLUD UPT SPAM Musi Rawas. Customers also find it difficult to see the amount of bills in realtime so that if the billing information has been presented then the customer can adjust the use of the water. This system will use a Waterflow Sensor to read the water flow which will then be converted to Digital data in the form of computer bits that will be processed by Arduino and then sent to the Server to be seen by the Clean Water Service Provider in this case the BLUD UPT SPAM Musi Rawas and customers. Intisari—Air merupakan unsur penting yang menjadi kebutuhan setiap manusia, pada perusahaan penyedia layanan Air bersih kontrol penggunaan air masih menjadi masalah karena belum ada sistem yang menyediakan informasi penggunaan Air pada sisi Pelanggan khususnya di BLUD UPT SPAM Kabupaten Musi Rawas. Pelanggan juga kesulitan untuk melihat jumlah tagihan secara realtime sehingga jika informasi tagihan tersebut sudah tersaji maka pelanggan dapat mengatur penggunaan Airnya. Sistem ini akan menggunakan Waterflow Sensor untuk membaca aliran air yang kemudian akan di konversikan ke data Digital  berupa bit komputer yang akan di proses oleh Arduino kemudian di kirimkan ke Server untuk dapat di lihat oleh Penyedia Jasa Air bersih dalam Hal ini BLUD UPT SPAM Kabupaten Musi Rawas dan Pelanggan

IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results

Effective and efficient approach in IoT Botnet detection

Internet of Things (IoT) technology presents an advantage to daily life, but this advantage is not a guarantee of security. This is because cyber-attacks, such as botnets, remain a threat to the user. Detection systems are one of the alternatives to maintain the security of IoT network. A reliable detection system should effectively detect botnets with high accuracy levels and low positive rate. It should be efficient to perform detection quickly. However, data generated by IoT networks have high dimensions and high scalability, so they need to be minimized. In network security analysis process, high-dimension data pose challenges, such as the dimension curse correlation between different dimensions, which causes features that are hard to define, datasets that are mostly unordered, cluster combination, and exponential growth. In this study, we applied feature reduction using the Linear Discriminant Analysis (LDA) method to minimize features on the IoT network to detect botnet. The reduction process is carried out on the N-BaIoT dataset which has 115 features reduced to 2 features. Performing feature reduction with detection systems has become more effective and efficient. Experimental result showed that the application of LDA combined with machine learning on the classification Decision Tree method was able to detect with accuracy that reached 100% in 98.58s with only two features

IoT Botnet malware classification using Weka tool and scikit-learn machine learning

Botnet is one of the threats to internet network security—Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results