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Identification of P2P (peer to peer) applications inside network traffic plays an important role for route provisioning, traffic policing, flow prioritization, network service pricing, network capacity planning and network resource management. Inspecting and identifying the P2P applications is one of the most important tasks to have a network that runs efficiently. In this paper, we focus on identification of different P2P applications. To this end, we explore four commonly used supervised machine learning algorithms as C4.5, Ripper, SVM(Support Vector Machines), Naïve Bayesian and well known unsupervised machine learning algorithm K-Means on four different datasets. We evaluate their performances to identify the P2P applications that each traffic flow belongs to. Evaluations show that, Ripper algorithm gives better results than the others.

The Android platform commands a dramatic majority of the mobile market, and this popularity makes it an appealing target for malicious actors. Android malware is especially dangerous because of the versatility in distribution and acquisition of software on the platform. In this paper, we continue to investigate evolutionary Android malware detection systems, implementing new features in an artificial arms race, and comparing different systems' performances on three new datasets. Our evaluations show that the artificial arms race based system achieves the overall best performance on these very challenging datasets. CCS CONCEPTS • Computing methodologies → Genetic algorithms; • Security and privacy → Malware and its mitigation;

This research focuses on the identi¯cation of relevant experience required for solving IT (Information Technology) problems in small-to medium-sized enterprises. To achieve this, we integrated information retrieval techniques with clustering and optimization techniques to design and develop a custom-built Experience Management system for IT management support. We have built and evaluated our system on three di®erent publicly available data sets: Princeton, Parallels and GoDaddy. Results support that it is possible to provide the right mix of automation and manual activity for IT experience management while achieving a high accuracy.

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