Network traffic analysis for threats detection in the Internet of Things

As the prevalence of the Internet of Things (IoT) continues to increase, cyber criminals are quick to exploit the security gaps that many devices are inherently designed with. Users cannot be expected to tackle this threat alone, and many current solutions available for network monitoring are simply not accessible or can be difficult to implement for the average user, which is a gap that needs to be addressed. This article presents an effective signature-based solution to monitor, analyze, and detect potentially malicious traffic for IoT ecosystems in the typical home network environment by utilizing passive network sniffing techniques and a cloud application to monitor anomalous activity. The proposed solution focuses on two attack and propagation vectors leveraged by the infamous Mirai botnet, namely DNS and Telnet. Experimental evaluation demonstrates the proposed solution can detect 98.35 percent of malicious DNS traffic and 99.33 percent of Telnet traffic for an overall detection accuracy of 98.84 percent

Unmanned Ground Vehicle for Data Collection in Wireless Sensor Networks: Mobility-aware Sink Selection

Several recent studies have demonstrated the benefits of using the Wireless Sensor Network (WSN) technology in large-scale monitoring applications, such as planetary exploration and battlefield surveillance. Sensor nodes generate continuous stream of data, which must be processed and delivered to end users in a timely manner. This is a very challenging task due to constraints in sensor node’s hardware resources. Mobile Unmanned Ground Vehicles (UGV) has been put forward as a solution to increase network lifetime and to improve system's Quality of Service (QoS). UGV are mobile devices that can move closer to data sources to reduce the bridging distance to the sink. They gather and process sensory data before they transmit it over a long-range communication technology. In large-scale monitored physical environments, the deployment of multiple-UGV is essential to deliver consistent QoS across different parts of the network. However, data sink mobility causes intermittent connectivity and high re-connection overhead, which may introduce considerable data delivery delay. Consequently, frequent network reconfigurations in multiple data sink networks must be managed in an effective way. In this paper, we contribute an algorithm to allow nodes to choose between multiple available UGVs, with the primary objective of reducing the network reconfiguration and signalling overhead. This is realised by assigning each node to the mobile sink that offers the longest connectivity time. The proposed algorithm takes into account the UGV’s mobility parameters, including its movement direction and velocity, to achieve longer connectivity period. Experimental results show that the proposed algorithm can reduce end-to-end delay and improve packet delivery ratio, while maintaining low sink discovery and handover overhead. When compared to its best rivals in the literature, the proposed approach improves the packet delivery ratio by up to 22%, end-to-end delay by up to 28%, energy consumption by up to 58%, and doubles the network lifetime

Network Traffic Analysis for Threats Detection in the Internet of Things

As the prevalence of the Internet of Things (IoT) continues to increase, cyber criminals are quick to exploit the security gaps that many devices are inherently designed with. Whilst users can not be expected to tackle this threat alone, many current solutions available for network monitoring are simply not accessible or can be difficult to implement for the average user and is a gap that needs to be addressed. This paper presents an effective signature-based solution to monitor, analyse and detect potentially malicious traffic for IoT ecosystems in the typical home network environment by utilising passive network sniffing techniques and a cloud-application to monitor anomalous activity. The proposed solution focuses on two attack and propagation vectors leveraged by the infamous Mirai botnet, namely DNS and Telnet. Experimental evaluation demonstrates the proposed solution can detect 98.35% of malicious DNS traffic and 99.33% of Telnet traffic respectively; for an overall detection accuracy of 98.84%

Dynamic Clustering and Management of Mobile Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), routing data towards the sink leads to unbalanced energy consumption among intermediate nodes resulting in high data loss rate. The use of multiple Mobile Data Collectors (MDCs) has been proposed in the literature to mitigate such problems. MDCs help to achieve uniform energy-consumption across the network, fill coverage gaps, and reduce end-to-end communication delays, amongst others. However, mechanisms to support MDCs such as location advertisement and route maintenance introduce significant overhead in terms of energy consumption and packet delays. In this paper, we propose a self-organizing and adaptive Dynamic Clustering (DCMDC) solution to maintain MDC-relay networks. This solution is based on dividing the network into well-delimited clusters called Service Zones (SZs). Localizing mobility management traffic to a SZ reduces signaling overhead, route setup delay and bandwidth utilization. Network clustering also helps to achieve scalability and load balancing. Smaller network clusters make buffer overflows and energy depletion less of a problem. These performance gains are expected to support achieving higher information completeness and availability as well as maximizing the network lifetime. Moreover, maintaining continuous connectivity between the MDC and sensor nodes increases information availability and validity. Performance experiments show that DCMDC outperforms its rival in the literature. Besides the improved quality of information, the proposed approach improves the packet delivery ratio by up to 10%, end-to-end delay by up to 15%, energy consumption by up to 53%, energy balancing by up to 51%, and prolongs the network lifetime by up to 53%

A Wireless Sensor Network Border Monitoring System: Deployment Issues and Routing Protocols

External border surveillance is critical to the security of every state and the challenges it poses are changing and likely to intensify. Wireless Sensor Networks (WSN) are a low cost technology that provide an intelligence-led solution to effective continuous monitoring of large, busy and complex landscapes. The linear network topology resulting from the structure of the monitored area raises challenges that have not been adequately addressed in the literature to date. In this paper, we identify an appropriate metric to measure the quality of WSN border crossing detection. Furthermore, we propose a method to calculate the required number of sensor nodes to deploy in order to achieve a specified level of coverage according to the chosen metric in a given belt region, while maintaining radio connectivity within the network. Then, we contribute a novel cross layer routing protocol, called Levels Division Graph (LDG), designed specifically to address the communication needs and link reliability for topologically linear WSN applications. The performance of the proposed protocol is extensively evaluated in simulations using realistic conditions and parameters. LDG simulation results show significant performance gains when compared to its best rival in the literature, Dynamic Source Routing (DSR). Compared to DSR, LDG improves the average end-to-end delays by up to 95%, packet delivery ratio by up to 20%, and throughput by up to 60%, while maintaining comparable performance in terms of normalized routing load and energy consumption

A Flexible Encryption Technique for the Internet of Things Environment

IoT promises a new era of connectivity that goes beyond laptops and smart connected devices to connected vehicles, smart homes, smart cities and connected healthcare. The huge volume of data that is collected from millions of IoT devices raises information security and privacy concerns for users. This paper presents a new scalable encryption technique, called Flexible encryption Technique (FlexenTech), to protect IoT data during storage and in transit. FlexenTech is suitable for resource constrained devices and networks. It offers a low encryption time, defends against common attacks such as replay attacks and defines a configurable mode, where any number of rounds or key sizes may be used. Experimental analysis of FlexenTech shows its robustness in terms of its multiple configurable confidentiality levels by allowing various configurations. This configurability provides several advantages for resource constrained devices, including reducing the encryption computation time by up to 9.7% when compared to its best rivals in the literature

Unraveling the forcings controlling the vegetation and climate of the best orbital analogues for the present interglacial in SW Europe

The suitability of MIS 11c and MIS 19c as analogues of our present interglacial and its natural evolution is still debated. Here we examine the regional expression of the Holocene and its orbital analogues over SW Iberia using a model-data comparison approach. Regional tree fraction and climate based on snapshot and transient experiments using the LOVECLIM model are evaluated against the terrestrial-marine profiles from Site U1385 documenting the regional vegetation and climatic changes. The pollen-based reconstructions show a larger forest optimum during the Holocene compared to MIS 11c and MIS 19c, putting into question their analogy in SW Europe. Pollen-based and model results indicate reduced MIS 11c forest cover compared to the Holocene primarily driven by lower winter precipitation, which is critical for Mediterranean forest development. Decreased precipitation was possibly induced by the amplified MIS 11c latitudinal insolation and temperature gradient that shifted the westerlies northwards. In contrast, the reconstructed lower forest optimum at MIS 19c is not reproduced by the simulations probably due to the lack of Eurasian ice sheets and its related feedbacks in the model. Transient experiments with time-varying insolation and CO2 reveal that the SW Iberian forest dynamics over the interglacials are mostly coupled to changes in winter precipitation mainly controlled by precession, CO2 playing a negligible role. Model simulations reproduce the observed persistent vegetation changes at millennial time scales in SW Iberia and the strong forest reductions marking the end of the interglacial "optimum".SFRH/BD/9079/2012, SFRH/BPD/108712/2015, SFRH/BPD/108600/2015info:eu-repo/semantics/publishedVersio

A simple rule to determine which insolation cycles lead to interglacials

The pacing of glacial–interglacial cycles during the Quaternary period (the past 2.6 million years) is attributed to astronomically driven changes in high-latitude insolation. However, it has not been clear how astronomical forcing translates into the observed sequence of interglacials. Here we show that before one million years ago interglacials occurred when the energy related to summer insolation exceeded a simple threshold, about every 41,000 years. Over the past one million years, fewer of these insolation peaks resulted in deglaciation (that is, more insolation peaks were ‘skipped’), implying that the energy threshold for deglaciation had risen, which led to longer glacials. However, as a glacial lengthens, the energy needed for deglaciation decreases. A statistical model that combines these observations correctly predicts every complete deglaciation of the past million years and shows that the sequence of interglacials that has occurred is one of a small set of possibilities. The model accounts for the dominance of obliquity-paced glacial–interglacial cycles early in the Quaternary and for the change in their frequency about one million years ago. We propose that the appearance of larger ice sheets over the past million years was a consequence of an increase in the deglaciation threshold and in the number of skipped insolation peaks.P.C.T. acknowledges funding from a Leverhulme Trust Research Project Grant (RPG-2014-417). M.C. and T.M. acknowledge support from the Belgian Policy Office under contract BR/121/A2/STOCHCLIM. E.W.W. is funded under a Royal Society Research Professorship and M.C. is a senior research scientist with the Belgian National Fund of Scientific Research

Regression analysis for energy and lifetime prediction in large wireless sensor networks

Wireless communication technologies are used to collect information from sensitive, hostile and inaccessible environments. They are used in both military and civil applications that include environmental, medical, military and industrial fields. Routing data to a processing center or a base station requires mechanisms for energy conservation at the end of the prolonged lifetime of the network. The simulation in this case is very constrained by the high density of the network. Existing tools cannot simulate large networks with millions of sensors. In this paper, we propose a new method using statistical regression analysis in order to predict the energy consumption and the lifetime of a wireless sensor network with hundreds or thousands of sensors by simulating smaller networks. We have validated the proposed method using a Revised LEACH protocol. Indeed, this method can be used for other protocols and other kind of simulations with the purpose of evaluating a specific parameter