No abstract

In the previous years, Skype has gained more and more popularity, since it is seen as the best VoIP software with good quality of sound, ease of use and one that works everywhere and with every OS. Because of its great diffusion, both the operators and the users are, for different reasons, interested in detecting Skype traffic.In this paper we propose a real-time algorithm (named Skype-Hunter) to detect and classify Skype traffic. In more detail, this novel method, by means of both signature-based and statistical procedures, is able to correctly reveal and classify the signaling traffic as well as the data traffic (calls and file transfers). To assess the effectiveness of the algorithm, experimental tests have been performed with several traffic data sets, collected in different network scenarios. Our system outperforms the 'classical' statistical traffic classifiers as well as the state-of-the-art ad hoc Skype classifier.

The increasing number of network attacks causes growing problems for network operators and users. Thus, detecting anomalous traffic is of primary interest in IP networks management. In this paper we address the problem considering a method based on PCA for detecting network anomalies. In more detail, we present a new technique that extends the state of the art in PCA based anomaly detection. Indeed, by means of the Kullback-Leibler divergence we are able to obtain great improvements with respect to the performance of the "classical" approach. Moreover we also introduce a method for identifying the flows responsible for an anomaly detected at the aggregated level. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed method

IEEE 802.15.4-2015 is the third revision of IEEE 802.15.4 Standard for Low-Rate Wireless Networks. The standard presents Time Slotted Channel Hopping (TSCH) Medium Access Control (MAC) protocol, which provides high reliability and low power consumption to various industrial applications. Despite the effectiveness and the importance of the TSCH protocol, the standard leaves out of its scope in defining how the schedule is built and maintained. In this work, we focus on scheduling in IEEE 802.15.4-2015 TSCH networks from the energy efficiency perspective in a centralized manner where the gateway makes frequency allocations and time slot assignments. At first, we derive an energy consumption model of a TSCH node to determine the network lifetime. Afterwards, we formulate the scheduling problem as an energy efficiency maximization problem, which is a nonlinear integer programming. Motivated by the high computational complexity of the problem, we propose a low-complexity Energy Efficient Scheduler (EES) and Vogel's Approximation Method Heuristic Scheduling Algorithm (VAM-HSA). We make a comparison with the Round Robin Scheduler (RRS) and analyse the schedulers in terms of success probability and energy efficiency. Performance evaluation indicates that EES and VAM-HSA perform better in terms of energy efficiency, while at the same time yielding a good throughput

The increasing number of network attacks causes growing problems for network operators and users. Thus, detecting anomalous traffic is of primary interest in IP networks management. In this paper, we address the problem considering a method based on PCA for detecting network anomalies. In more detail, this paper presents a new technique that extends the state of the art in PCA-based anomaly detection. Indeed, by means of multi-scale analysis and Kullback-Leibler divergence, we are able to obtain great improvements with respect to the performance of the 'classical' approach. Moreover, we also introduce a method for identifying the flows responsible for an anomaly detected at the aggregated level. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed metho