Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

Synoptic analysis techniques for intrusion detection in wireless networks

Current system administrators are missing intrusion alerts hidden by large numbers of false positives. Rather than accumulation more data to identify true alerts, we propose an intrusion detection tool that e?ectively uses select data to provide a picture of ?network health?. Our hypothesis is that by utilizing the data available at both the node and cooperative network levels we can create a synoptic picture of the network providing indications of many intrusions or other network issues. Our major contribution is to provide a revolutionary way to analyze node and network data for patterns, dependence, and e?ects that indicate network issues. We collect node and network data, combine and manipulate it, and tease out information about the state of the network. We present a method based on utilizing the number of packets sent, number of packets received, node reliability, route reliability, and entropy to develop a synoptic picture of the network health in the presence of a sinkhole and a HELLO Flood attacker. This method conserves network throughput and node energy by requiring no additional control messages to be sent between the nodes unless an attacker is suspected. We intend to show that, although the concept of an intrusion detection system is not revolutionary, the method in which we analyze the data for clues about network intrusion and performance is highly innovative

DNA-based client puzzle for WLAN association protocol against connection request flooding

In recent past, Wireless Local Area Network (WLAN) has become more popular because of its flexibility. However, WLANs are subjected to different types of vulnerabilities. To strengthen WLAN security, many high security protocols have been developed. But those solutions are found to be ineffective in preventing Denial of Service (DoS) attacks. A ‘Connection Request Flooding’ DoS (CRF-DoS) attack is launched when an access point (AP) encounters a sudden explosion of connection requests. Among other existing anti CRF-DoS methods, a client puzzle protocol has been noted as a promising and secure potential solution. Nonetheless, so far none of the proposed puzzles satisfy the security requirement of resource-limited and highly heterogeneous WLANs. The CPU disparity, imposing unbearable loads on legitimate users, inefficient puzzle generation and verification algorithms; the susceptibility of puzzle to secondary attacks on legitimate users by embedding fake puzzle parameters; and a notable delay in modifying the puzzle difficulty – these are some drawbacks of currently existing puzzles. To deal with such problems, a secure model of puzzle based on DNA and queuing theory is proposed, which eliminates the above defects while satisfying the Chen puzzle security model. The proposed puzzle (OROD puzzle) is a multifaceted technology that incorporates five main components include DoS detector, queue manager, puzzle generation, puzzle verification, and puzzle solver. To test and evaluate the security and performance, OROD puzzle is developed and implemented in real-world environment. The experimental results showed that the solution verification time of OROD puzzle is up to 289, 160, 9, 3.2, and 2.3 times faster than the Karame-Capkun puzzle, the Rivest time-lock puzzle, the Rangasamy puzzle, the Kuppusamy DLPuz puzzle, and Chen's efficient hash-based puzzle respectively. The results also showed a substantial reduction in puzzle generation time, making the OROD puzzle from 3.7 to 24 times faster than the above puzzles. Moreover, by asking to solve an easy and cost-effective puzzle in OROD puzzle, legitimate users do not suffer from resource exhaustion during puzzle solving, even when under severe DoS attack (high puzzle difficulty)

Interference and intrusion in wireless sensor networks

Wireless sensor network (WSN) systems for safety-critical, space and Internet of Things applications have recently begun to adopt open standards and commercial-off-the-shelf equipment, and persistently face challenges of malicious intrusion and spectrum coexistence. These threats are explored through Monte-Carlo simulation and benchtop testing, including matched protocol interference and sophisticated, interactive intrusion attacks. The need for expanding intrusion detection via a more holistic approach, whilst simultaneously improving WSN security, is illustrated. Discussions on WSN security, vulnerabilities, and attacks are also provided

Development of a Drone-Mounted Wireless Attack Platform

The commercial drone market has grown rapidly due to the increasing utility and capabilities of drones. This new found popularity has made it possible for inexpensive drones capable of impressive carry capacities and flight times to reach the consumer market. These new features also offer an invaluable resource to wireless hackers. Capitalizing on their mobility, a wireless hacker can equip a drone with hacking tools to surpass physical security (e.g. fences) with relative ease and reach wireless networks. This research seeks to experimentally evaluate the ability of a drone-mounted wireless attack platform equipped with a directional antenna to conduct wireless attacks effectively at distances greater than 800 meters. To test this hypothesis, the “skypie v2” prototype conducts computer network attacks against a target network and captured data is used to evaluate the effectiveness of the platform. Results showed that capture of a WPA2 handshake was possible at a RSSI of -72 dBm or 2400 meters from a network located in a open field. Additionally, nmap scans were conducted with a RSSI value of -74 dBm or nearly 3000 meters from the target network

Implementation and Evaluation of A Low-Cost Intrusion Detection System For Community Wireless Mesh Networks

Rural Community Wireless Mesh Networks (WMN) can be great assets to rural communities, helping them connect to the rest of their region and beyond. However, they can be a liability in terms of security. Due to the ad-hoc nature of a WMN, and the wide variety of applications and systems that can be found in such a heterogeneous environment there are multiple points of intrusion for an attacker. An unsecured WMN can lead to privacy and legal problems for the users of the network. Due to the resource constrained environment, traditional Intrusion Detection Systems (IDS) have not been as successful in defending these wireless network environments, as they were in wired network deployments. This thesis proposes that an IDS made up of low cost, low power devices can be an acceptable base for a Wireless Mesh Network Intrusion Detection System. Because of the device's low power, cost and ease of use, such a device could be easily deployed and maintained in a rural setting such as a Community WMN. The proposed system was compared to a standard IDS solution that would not cover the entire network, but had much more computing power but also a higher capital cost as well as maintenance costs. By comparing the low cost low power IDS to a standard deployment of an open source IDS, based on network coverage and deployment costs, a determination can be made that a low power solution can be feasible in a rural deployment of a WMN