Internet of Things (IoT) expansion led the market to find alternative communication technologies since existing protocols are insufficient in terms of coverage, energy consumption to fit IoT needs. Low Power Wide Area Networks (LPWAN) emerged as an alternative cost-effective communication technology for the IoT market. LoRaWAN is an open LPWAN standard developed by LoRa Alliance and has key features i.e., low energy consumption, long-range communication, builtin security, GPS-free positioning. In this paper, we will introduce LoRaWAN technology, the state of art studies in the literature and provide open opportunities.

In this study, an intrusion detection system (IDS) has been proposed to detect malicious in computer networks. The proposed system is studied on the CICIDS2017 dataset, which is the biggest dataset available online. In order to overcome the challenges big data created, it is aimed to determine the effects of the features on the data set and to find the most effective features that can differentiate the data in the most meaningful way. Therefore, recursive feature elimination is performed via random forest and the importance value of the features are calculated. Intrusions are detected with the accuracy of 91% by Deep Multilayer Perceptron (DMLP) structure using the obtained features.

Compared to the past, developments in computer and communication technologies have provided extensive and advanced changes. The usage of new technologies provide great benefits to individuals, companies, and governments, however, it causes some problems against them. For example, the privacy of important information, security of stored data platforms, availability of knowledge etc. Depending on these problems, cyber terrorism is one of the most important issues in todays world. Cyber terror, which caused a lot of problems to individuals and institutions, has reached a level that could threaten public and country security by various groups such as criminal organizations, professional persons and cyber activists. Thus, Intrusion Detection Systems (IDS) have been developed to avoid cyber attacks. In this study, deep learning and support vector machine (SVM) algorithms were used to detect port scan attempts based on the new CICIDS2017 dataset and 97.80%, 69.79% accuracy rates were achieved respectively.