International Conference on Mobile Networks and Management 

Abstract: The IoT is a network of interconnected everyday objects called “things” that have been augmented with a small measure of computing capabilities. Lately, the IoT has been affected by a variety of different botnet activities. As botnets have been the cause of serious security risks and financial damage over the years, existing Network forensic techniques cannot identify and track current sophisticated methods of botnets. This is because commercial tools mainly depend on signature-based approaches that cannot discover new forms of botnet. In literature, several studies have conducted the use of Machine Learning (ML) techniques in order to train and validate a model for defining such attacks, but they still produce high false alarm rates with the challenge of investigating the tracks of botnets. This paper investigates the role of ML techniques for developing a Network forensic mechanism based on network flow identifiers that can track suspicious activities of botnets. The experimental results using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.

Abstract: Cloud computing is widely considered as an attractive service model since the users commitments for investment and operations are minimised, and costs are in direct relation to usage and demand. However, when networking aspects for distributed clouds are considered, there is little support and the effort is often underestimated. The project SAIL is addressing cloud networking as the combination of management for cloud computing and vital networking capabilities between distributed cloud resources involved to improve the management of both. This position paper presents new security challenges as considered in SAIL for ensuring legitimate usage of cloud networking resources and for preventing misuse.

Abstract: The Trickle algorithm has proven to be of great benefit to the Wireless Sensor Networking area. It has shown general applicability in this field, e.g. for code distribution to smart objects and routing information distribution between smart objects. Up to now analysis of the algorithm has focussed on simulation studies and measurement campaigns. This paper introduces an analytical models for the algorithm’s behaviour for the time to consistency. The model is compared with simulation results for a set of network topologies and enables to discover efficient settings of the algorithm for various application areas, such as logistics.

Abstract: The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.

Abstract: This paper addresses the issue of resource offloading for energy usage optimization in the cloud, using the centrality principle of social networks. Mobile users take advantage of the mobile opportunistic cloud, in order to increase their reliability in service provision by guaranteeing sufficient resources for the execution of mobile applications. This work elaborates on the improvement of the energy consumption for each mobile device, by using a social collaboration model that allows for a cooperative partial process offloading scheme. The proposed scheme uses social centrality as the underlying mobility and connectivity model for process offloading within the connected devices to maximize the energy usage efficiency, node availability and process execution reliability. Furthermore, this work considers the impact of mobility on the social-oriented offloading, by allowing partitionable resources to be executed according to the social interactions and the associated mobility of each user during the offloading process. The proposed framework is thoroughly evaluated through event driven simulations, towards defining the validity and offered efficiency of the proposed offloading policy in conjunction to the energy consumption of the wireless devices.