Software-defined networking (SDN) is a promising technology to overcome many challenges in wireless sensor networks (WSN), particularly with respect to flexibility and reuse. Conversely, the centralization and the planes' separation turn SDNs vulnerable to new security threats in the general context of distributed denial of service (DDoS) attacks. Stateof-the-art approaches to identify DDoS do not always take into consideration restrictions in typical WSNs e.g., computational complexity and power constraints, while further performance improvement is always a target. The objective of this work is to propose a lightweight but very efficient DDoS attack detection approach using change point analysis. Our approach has a high detection rate and linear complexity, so that it is suitable for WSNs. We demonstrate the performance of our detector in software-defined WSNs of 36 and 100 nodes with varying attack intensity (the number of attackers ranges from 5% to 20% of nodes). We use change point detectors to monitor anomalies in two metrics: the data packets delivery rate and the control packets overhead. Our results show that with increasing intensity of attack, our approach can achieve a detection rate close to 100% and that the type of attack can also be inferred.

Software-Defined Networking is a promising paradigm for providing flexibility and programmability to computer networks. Our goal is to assess the performance of this paradigm applied to Wireless Sensor Networks. Previous evaluations are not complete, since they study small networks, do not explore crucial performance metrics, or solely examine light traffic conditions. For this, we execute simulations and a testbed experiment. The testbed shows Software-Defined Networking successfully operates in a real network. We study simulated networks up to 289 data-transmitting nodes, while assessing all the main networks metrics: data delivery, delay, control overhead, and energy consumption. We investigate important parameters for Software-Defined Wireless Sensor Networks, such as controller positioning, radio duty cycling, number of data sinks, and use of source routed control messages. The results indicate that Software-Defined Networking is feasible for Wireless Sensor Networks, presenting competitive data delivery ratio while saving energy in comparison to RPL, the Routing Protocol for Low-power and lossy networks.INDEX TERMS Software-defined networking, Internet of Things, wireless sensor networks, performance analysis.

Software-defined wireless sensor networks have attracted considerable attention in recent years as they simplify the network management and provide the framework to automate infrastructure sharing. On the other hand, the centralization and planes' separation can turn SDNs vulnerable to new types of denial of service attacks. Existing intrusion detection approaches are not in general suitable for restricted networks or do not achieve optimal detection rates. This work aims at fulfilling both requirements by using a new lightweight, multimetric, online change point detector to monitor performance metrics that are impacted when the network is under attack. There are two major novelties in the proposed detector referring to previous works: first, we move to a purely online detector, secondly, we monitor in parallel multiple metrics, increasing the detection vector space to different types of attack. Our tests show that intrusion detection monitoring control overhead and data packets delivery rate in a SDWSN results in enhanced detection rates over 96% in all topologies and levels of attacks. We finally show that with a high probability (exceeding 89% in all cases) it is possible to identify the "type" of the attack.

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BackgroundAnxiety disorders are characterized by specific emotions, thoughts and physiological responses. Little is known, however, about the relationship between psychological/personality indices of anxiety responses to fear stimuli.MethodsWe studied this relationship in healthy subjects by comparing scores on psychological and personality questionnaires with results of an experimental fear conditioning paradigm using a visual conditioned stimulus (CS). We measured skin conductance response (SCR) during habituation, conditioning, and extinction; subsequently testing for recall and renewal of fear 24 hours later.ResultsWe found that multiple regression models explained 45% of the variance during conditioning to the CS+, and 24% of the variance during renewal of fear to the CS+. Factors that explained conditioning included lower levels of conscientiousness, increased baseline reactivity (SCL), and response to the shock (UCR). Low levels of extraversion correlated with greater renewal. No model could be found to explain extinction learning or extinction recall to the CS+.ConclusionsThe lack of correlation of fear extinction with personality and neuropsychological indices suggests that extinction may be less determined by trait variables and cognitive state, and may depend more on the subject’s current emotional state. The negative correlation between fear renewal and extraversion suggests that this personality characteristic may protect against post-treatment relapse of symptoms of anxiety disorders.