Abstract² Developing effective security solutions for wireless sensor networks (WSN) are not easy due to limited resources of WSNs and the hazardous nature of wireless medium. The implementation of encryption/decryption algorithms which are the most essential part of the secure communication can be very intricate in WSNs since they incorporate routines that having very complex and intense computing procedures. A secure clustering protocol that achieves the desired security goals while keeping an acceptable level of energy consumption is a challenging problem in wireless sensor network. LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol is a basic clustering-based routing protocol for WSNs. S-LEACH is the first modified version of LEACH with cryptographic protection against outsider attacks. This paper proposes MS-LEACH to enhance the security of S-LEACH by providing data confidentiality and node to cluster head (CH) authentication using pairwise keys shared between CHs and their cluster members. The security analysis of proposed MS-LEACH shows that it has efficient security properties and achieves all WSN security goals compared to the existing secured solutions of LEACH protocol. A simulation based performance evaluation of MS-LEACH demonstrates the effectiveness of proposed MS-LEACH protocol and shows that the protocol achieves the desired security goals and outperforms other protocols in terms of energy consumption, network lifetime, network throughput and normalized routing load.

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Automatic identification of the service type used by network flows (e.g., HTTP and MySQL) is an essential part of many cloud management and monitoring tasks for quality of service, security monitoring, resource allocation, etc. Several studies have adapted deep learning models for accurate service type identification of network traffic. These models vary in how the message flow data is used and what datasets are considered. There are no published guidelines on selecting the best approach for automating the service identification process. In this paper, we opt to fill such a technical gap and provide a detailed study of the trade-offs of different deep-learning based approaches for service type identification of network traffic. Towards this end, we generate flow-based datasets for a wide range of service types that are commonly deployed in the cloud. We consider two different deep learning models that have shown promising results in this context, and show their performance for both payloadand header-based datasets, considering fundamental parameters such as dynamic service port configuration, flow direction and the packet order in the flow stream.