Collaborative intrusion detection networks with multi-hop clustering for internet of things

Internet of things (IoT) is an emerging topic in so many aspects nowadays. The integration between devices and human itself is currently in large scale development. With the continuous applications of the IoT, the hidden problems such as security threats become one of the key considerations. Furthermore, limited power and computational capability of the devices in the system make it more challenging.Therefore, the needs of reliable and effective security system throughout the networks are highly needed. This research proposed a collaborative system based on JADE that consists of 3 types of agent, which are IoT server, controller, and node. Every agents will collaborate each other in terms of exchanging the intrusion detection results. The collaboration between the agents will provide more efficient and good performance. Four classification algorithms were used to model IDS functions. Then, the performance evaluation was done on the system with several parameters such as cost loss expectation, energy consumption, and metric of IDS efficiency. The result shows that the number of reports sent by IoT controller were decreased up to 80% while preserving the security aspect

Traffic Characterization of an Internet of Things(IOT) Network Architecture

Internet of things (IoT) is an evolving paradigm that is currently getting more attention and rapidly gaining importance. The basic idea of IoT is to connect everyone and everything to the Internet for information exchange. It is essential to develop a clear understanding of characteristics of IoT traffic sources as well as to find a traffic model that efficiently characterizes the statistical behavior of IoT traffic. Since many IoT devices generate relatively small sized data, we are particularly interested in an IoT network architecture where data from a number of different IoT devices are aggregated at an IoT gateway. We focus on characterizing the IoT aggregated traffic pattern for three common IoT applications with real-time and non-real-time quality of service (QoS) requirements. These applications include healthcare, smart cities, and video surveillance. Our study is based on generating a real IoT traffic trace in a lab by using various sensors and devices in the aforementioned applications. The generated traffic trace is transmitted wirelessly over the air using Wi-Fi technology to an IoT gateway. The input network traffic to this gateway is characterized. In the experiments, the amount of input traffic to the gateway is varied and different traffic patterns for each of the selected applications are examined. Statistical tests and parameters are used to determine the best matching packet inter-arrival time distribution for different traffic penetrations. Moreover, we also examine packet size distributions. Based on our empirical data, the experimental results indicate that IoT packet inter-arrival time follows a Pareto distribution. However, it can be better modeled as a Weibull distribution in some traffic patterns. Our experimental results also reveal that the packet size distribution of different penetrations of the studied IoT applications is not in a good match with the commonly used Geometric distribution. Furthermore, we investigate the impact of traffic characterization on the performance of the considered IoT network architecture for a certain availability of network resources using computer simulations

Traffic modeling for aggregated periodic IoT data

The Internet of Things (IoT) is emerging in the telecommunication sector, and will bring a very large number of devices that connect to the Internet in the near future. The expected growth in such IoT nodes necessitates appropriate traffic models in order to evaluate their impact on different aspects of networking, e.g., on signaling load in the networks, or on processing load of the data in a cloud. In this paper we analyze the characteristics of aggregated periodic IoT data based on related work, and compare them with a Poisson process as approximation for the superposed traffic as assumed in standardization. Such an approximation is crucial in order to investigate the scalability of an IoT network, as it may be impossible in practice to measure or to simulate large-scale IoT deployments. The accuracy and applicability of the Poisson process is investigated for the use case “IoT cloud”. The results show that the Poisson process may induce large errors depending on the performance metric of interest. This error must be considered by standardization and requires more sophisticated traffic models. As key contributions, we provide realistic traffic models for periodic IoT data, introduce performance metrics for quantifying the bias, and derive reference values as to when the Poisson process can be assumed for aggregated periodic IoT data

Traffic modeling for aggregated periodic IoT data

The Internet of Things (IoT) is emerging in the telecommunication sector, and will bring a very large number of devices that connect to the Internet in the near future. The expected growth in such IoT nodes necessitates appropriate traffic models in order to evaluate their impact on different aspects of networking, e.g., on signaling load in the networks, or on processing load of the data in a cloud. In this paper we analyze the characteristics of aggregated periodic IoT data based on related work, and compare them with a Poisson process as approximation for the superposed traffic as assumed in standardization. Such an approximation is crucial in order to investigate the scalability of an IoT network, as it may be impossible in practice to measure or to simulate large-scale IoT deployments. The accuracy and applicability of the Poisson process is investigated for the use case “IoT cloud”. The results show that the Poisson process may induce large errors depending on the performance metric of interest. This error must be considered by standardization and requires more sophisticated traffic models. As key contributions, we provide realistic traffic models for periodic IoT data, introduce performance metrics for quantifying the bias, and derive reference values as to when the Poisson process can be assumed for aggregated periodic IoT data.submittedVersion© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works