Modeling malware propagation in smartphone social networks

Smartphones have become an integral part of our everyday lives, such as online information accessing, SMS/MMS, social networking, online banking, and other applications. The pervasive usage of smartphones also results them in enticing targets of hackers and malware writers. This is a desperate threat to legitimate users and poses considerable challenges to network security community. In this paper, we model smartphone malware propagation through combining mathematical epidemics and social relationship graph of smartphones. Moreover, we design a strategy to simulate the dynamic of SMS/MMS-based worm propagation process from one node to an entire network. The strategy integrates infection factor that evaluates the propagation degree of infected nodes, and resistance factor that offers resistance evaluation towards susceptible nodes. Extensive simulations have demonstrated that the proposed malware propagation model is effective and efficient

Deep transfer learning mechanism for fine-grained cross-domain sentiment classification

The goal of cross-domain sentiment classification is to utilise useful information in the source domain to help classify sentiment polarity in the target domain, which has a large number of unlabelled data. Most of the existing methods focus on extracting the invariant features between two domains. But they cannot make better use of the unlabelled data in the target domain. To solve this problem, we present a deep transfer learning mechanism (DTLM) for fine-grained cross-domain sentiment classification. DTLM provides a transfer mechanism to better transfer sentiment across domains by incorporating BERT(Bidirextional Encoder Representations from Transformers) and KL (Kullback-Leibler) divergence. We introduce BERT as a feature encoder to map the text data of different domains into a shared feature space. Then, we design a domain adaptive model using KL divergence to eliminate the difference of feature distribution between the source domain and target domain. In addition, we introduce the entropy minimisation and consistency regularisation to process unlabelled samples in the target domain. Extensive experiments on the datasets from YelpAspect, SemEval 2014 task 4 and Twitter not only demonstrate the effectiveness of our proposed method but also provide a better way for cross-domain sentiment classification

Topology-oriented virtual network embedding approach for data centers

© 2018 IEEE. Currently, data centers have become an attractive candidate for users that require IT resources in the form of virtual networks to run their applications. Optimal mapping of the virtual network on the top of the substrate network with resource constraint is called virtual network embedding (VNE) problem. Most of the VNE algorithms are general algorithms for random topology and not suitable for data centers due to particular topological characteristics. To solve the VNE problem in data centers, this paper develops a topology-oriented algorithm based on the Discrete Particle Swarm Optimization (DPSO). We first develop a maximum spanning algorithm to compute the ranking of virtual nodes based on, not only its bandwidth and degree, but also its connectivity in the entire virtual network. Then, the virtual networks are embedded onto the substrate network according to the connectivity ranking result by a DPSO-based algorithm, in which we also propose a topological heuristic information of substrate network and combine it into the particle search process for boosting convergence speed and revenue/cost ratio of substrate network. The evaluation results show that the proposed algorithm can improve the optimization performance of VNE by comparing with a few existing algorithms

Control layer resource management in SDN-IoT networks using multi-objective constraint

Software Defined Networking (SDN) and Internet of Things (IoT) integration has thrown many critical challenges. Specifically, in heterogeneous SDN-IoT ecosystem, optimized resources utilization and effective management at the control layer is very difficult. This mainly affects the application specific Quality of Service (QoS) and energy consumption of the IoT network. Motivated from this, we propose a new Resource Management (RM) method at the control layer, in distributed SDN-IoT networks. This paper starts with reasons that why at control layer RM is more complex in the SDN-IoT ecosystem. After-that, we highlight motivated examples that necessitate to investigate new RM methods in SDN-IoT context. Further, we propose a novel method to compute controller performance. Theoretical analysis is conducted to prove that the proposed method is better than the existing methods