Wireless sensor networks (WSNs) are being used to facilitate monitoring of patients in hospital and home environments. These systems consist of a variety of different components/sensors and many processes like clustering, routing, security, and self-organization. Routing is necessary for medical-based WSNs because it allows remote data delivery and it facilitates network scalability in large hospitals. However, routing entails several problems, mainly due to the open nature of wireless networks, and these need to be addressed. This paper looks at two of the problems that arise due to wireless routing between the nodes and access points of a medical WSN (for IoT use): black hole and selective forwarding (SF) attacks. A solution to the former can readily be provided through the use of cryptographic hashes, while the latter makes use of a neighbourhood watch and threshold-based analysis to detect and correct SF attacks. The scheme proposed here is capable of detecting a selective forwarding attack with over 96% accuracy and successfully identifying the malicious node with 83% accuracy.
This work presents a detailed study, characterization, and measurement of video latency in a real-time video streaming application. The target application consists of an automatic control system in the form of a control station and the mini Remotely Operated Vehicle (ROV) equipped with a camera, which is controllable over local area network (LAN) and the Internet. Control signal transmission and feedback measurements to the operator usually impose real-time constraints on the network channel. Similarly, the video stream, which is required for the normal system control and maneuvering, imposes further strict requirements on the network in terms of bandwidth and latency. Based on these requirements, controlling the system in real time through a standard Internet connection is a challenging task. The measurement of important network parameters like availability, bandwidth, and latency has become mandatory for remotely controlling the system in real time. It is necessary to establish a methodology for the measurement of video and network latency to improve the real-time controllability and safety of the system as such measurement is not possible using existing solutions due to the following reasons: insufficient accuracy, relying on the Internet resources such as generic Network Time Protocol (NTP) servers, inability to obtain one-way delay measurement, and many solutions only having support for web cameras. Here, an efficient, reliable, and cost-effective methodology for the measurement of latency of a video stream over a LAN and the Internet is proposed. A dedicated stratum-1 NTP server is used and the necessary software needed for acquiring and measuring the latency of a video stream from a generic IP camera as well as integration into the existing ROV control software was developed. Here, by using the software and dedicated clock synchronization equipment (NTP server), it was found that normal video latencies in a LAN were in the range of 488ms -850ms, while latencies over the Internet were measured to be in the range of 558ms -1211ms. It is important to note that the values were obtained by using a generic (off-the-shelf) IP camera and they represent the actual latencies which might be experienced during control over long range and across international territory borders.
The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed.
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