We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to training families. However, novel unknown malware families frequently emerge in real-world applications, and as such, require recognizing malware instances in an open-set scenario, i.e., some unknown families are also included in the test set, which has been rarely and nonthoroughly investigated in the cyber-security domain. One practical solution for MOSR may consider jointly classifying known and detecting unknown malware families by a single classifier (e.g., neural network) from the variance of the predicted probability distribution on known families. However, conventional well-trained classifiers usually tend to obtain overly high recognition probabilities in the outputs, especially when the instance feature distributions are similar to each other, e.g., unknown versus known malware families, and thus, dramatically degrade the recognition on novel unknown malware families. To address the problem and construct an applicable MOSR system, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families and support a more robust training of the classifier. More specifically, we build upon the generative adversarial networks to explore and obtain marginal malware instances that are close to known families while falling into mimical unknown ones to guide the classifier to lower and flatten the recognition probabilities of unknown families and relatively raise that of known ones to rectify the performance of classification and detection. A cooperative training scheme involving the classification, synthesizing and rectification are further constructed to facilitate the training and jointly improve the model performance. Moreover, we also build a new large-scale malware dataset, named MAL-100, to fill the gap of lacking a large open-set malware benchmark dataset. Experimental results on two widely used malware datasets and our MAL-100 demonstrate the effectiveness of our model compared with other representative methods.

One non-negligible flaw of the convolutional neural networks (CNNs) based single image super-resolution (SISR) models is that most of them are not able to restore high-resolution (HR) images containing sufficient high-frequency information. Worse still, as the depth of CNNs increases, the training easily suffers from the vanishing gradients. These problems hinder the effectiveness of CNNs in SISR. In this paper, we propose the Dual-view Attention Networks to alleviate these problems for SISR. Specifically, we propose the local aware (LA) and global aware (GA) attentions to deal with LR features in unequal manners, which can highlight the high-frequency components and discriminate each feature from LR images in the local and global views, respectively. Furthermore, the local attentive residual-dense (LARD) block that combines the LA attention with multiple residual and dense connections is proposed to fit a deeper yet easy to train architecture. The experimental results verified the effectiveness of our model compared with other state-of-the-art methods.

Drive-thru Internet has been considered as an effective Internet access method for Internet of Vehicles (IoV). Through the opportunistic vehicle-to-roadside WiFi connection, it can provide high throughput performance with low communication cost for IoV applications, such as intelligent transportation system, automotive infotainment, etc. However, its usability is highly affected by a fundamental issue called rate adaptation (RA), which is to adjust the modulation and coding rate to adapt to the dynamic wireless channel between the vehicle and the roadside access point (AP). Conventional WiFi RA schemes are designed for indoor or quasistatic scenarios and do not account for the channel variations in drive-thru Internet. In this article, we study the limitation of applying existing RA schemes in drive-thru Internet and propose a reinforcement learning (RL)-based RA scheme to capture the potential channel variation patterns and efficiently select the rate for every vehicle's egress frame. Simulation results demonstrate that the proposed RA scheme outperforms the existing schemes in network throughput and that the efficiency of the learning model can be generalized under various conditions. The proposed RA method can provide useful inspirations for designing robust and scalable link adaptation protocols in IoV.

Existing works adopt the Edge-Oriented Resource Allocation (EORA) scheme, in which edge nodes cache services and schedule user requests to distribute workloads over cloud and edge nodes, so as to achieve high-quality services and low latency. Unfortunately, EORA does not fully take into account the fact that service providers are sometimes independent from the edge operators with their own objectives. To deal with the conflict and cooperation between service providers and edge nodes, we devise a service-oriented resource allocation (SORA) scheme, where edge nodes and service providers adjust their resource allocations to provide requested services. We first prove that such resource allocation problem is NP-hard. We then propose a three-sided cyclic game (3CG) involving users, edge nodes, and service providers who make their individual decisions by choosing respectively high-quality services, high-value users, and cost-effective edge nodes for service deployment. Based on 3CG, we prove the existence and approximation ratio of pure-strategy Nash equilibriums (NEs). We also develop both centralized and distributed approximate algorithms for resource allocation. Finally, extensive experimental results validate the effectiveness and convergence of the proposed algorithms.

The Vehicle-to-Vehicle (V2V) communication is critical in vehicular networks, besides the fact that it can enable the message sharing among neighboring vehicles, it can also help to relay neighbors' data content to remote destinations during the opportunistic V2V contacting period. The efficiency of V2V data pipe is affected drastically by the intermittent V2V connection, which requires a buffer queue to cache the data contents for random disruption and resuming of the transmission. In this paper, we propose a queueing theoretical framework to analysis the effectiveness of the opportunistic V2V communication, which captures the connection interruption due to the vehicle mobility and trades off between the V2V transmission effectiveness and average data service delay. An M/G/1/K queue model with service interruption is setup to analysis the relationship between how many data tasks can be fulfilled via V2V data pipe and the average time to accomplish the data service. We also consider two different resuming types, namely, resuming from beginning and resuming from break point. The proposed queueing framework is validated via simulation based on PTV VISSIM trace, which can provide guideline in design and development of future V2V communication protocols.

Predicting service demand of users based on the historical network traffic data in a large area has been of great importance towards better service management for geo-distributed systems, such as edge computing. Existing works relying on complete information of historical service demands cannot deal with real-world scenarios that a significant portion of historical data is not available because collecting such data comprehensively is either costly or impossible. Motivated by the challenge of data incompleteness for the demand prediction, in this paper, we propose a deep neural network model based on the encoder-decoder architecture consisting of an encoder, a converter, and a decoder. This model successfully extracts global features from incomplete historical data, predicts the future information in the feature space, and finally generates complete future demands of the whole area from future features. We devise an encoder with mixed 3D and 2D convolutional layers such that rich temporal and spatial features of service demands can still be captured even when historical data are partially missing. We embed the conditional generative adversarial networks (GANs) into our decoder that generates real demand distribution with magnified quality. Another major difference from the traditional encoder-decoder architecture is to establish a fine-grained association between the features of history and future by adding the converter in the middle of encoder and decoder. With the three components, our model can overcome the data incompleteness challenge and use the extracted global features to make an accurate prediction of service demands. We conduct experiments on a real-world dataset with demands of telecommunications services in an urban environment. The experimental results show that our model achieves much higher prediction accuracy than the state-of-the-art approaches when only incomplete historical data are available.

Managing edge resources is one of key issues in edge computing. Recent works of resource allocation in edge computing focus on service caching and request scheduling on edge nodes by distributing tasks over cloud and edge nodes, so as to achieve high-quality services and low latency. Unfortunately, most schemes do not pay enough attention on service providers which also have their own resources. To serve users, service providers cooperate with edge nodes and are sometimes independent of the edge operators with their own objectives. To deal with the cooperation and conflict among users, service providers and edge nodes, we propose a cyclic selection model to depict the resource allocation among users, edge nodes and service providers where they cooperate for completing user requests and compete for their own interest. Such resource allocation problem can be formulated as a non-linear integer programming which is very difficult to solve. Thus, we propose a three-sided cyclic game (3CG) involving users, edge nodes, and service providers who make their decisions: users select preferred services, service providers select cost-effective edge nodes, edge nodes select high-value users. 3CG is proved to have pure-strategy Nash equilibriums and an approximation ratio. We develop central and distributed approximate algorithms for resource allocation. The evaluation results of 3CG show the effectiveness and efficiency of the proposed algorithms.