Recommendation techniques based on off-line data processing: a multifaceted survey

Recommendations based on off-line data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, and translate the research results into real-world applications, etc. This paper surveys the recent progress in the research of recommendations based on off-line data processing, with emphasis on new techniques (such as context-based recommendation, temporal recommendation), and new features (such as serendipitous recommendation). Finally, we outline some existing challenges for future research.

Scalable RFID security framework and protocol supporting Internet of Things

Radio-frequency identification (RFID) is seen as one of the requirements for the implementation of the Internet-of-Things (IoT). However, an RFID system has to be equipped with a holistic security framework for a secure and scalable operation. Although much work has been done to provide privacy and anonymity, little focus has been given to performance, scalability and customizability issues to support robust implementation of IoT. Also, existing protocols suffer from a number of deficiencies such as insecure or inefficient identification techniques, throughput delay and inadaptability. In this paper, we propose a novel identification technique based on a hybrid approach (group-based approach and collaborative approach) and security check handoff (SCH) for RFID systems with mobility. The proposed protocol provides customizability and adaptability as well as ensuring the secure and scalable deployment of an RFID system to support a robust distributed structure such as the IoT. The protocol has an extra fold of protection against malware using an incorporated malware detection technique. We evaluated the protocol using a randomness battery test and the results show that the protocol offers better security, scalability and customizability than the existing protocols. © 2014 Elsevier B.V. All rights reserved.

A block-aware hybrid data dissemination with hotspot elimination in wireless sensor network

As a significant milestone in the data dissemination of wireless sensor networks (WSNs), the comb-needle (CN) model was developed to dynamically balance the sensor data pushing and pulling during hybrid data dissemination. Unfortunately, the hybrid push-pull data dissemination strategy may overload some sensor nodes and form the hotspots that consume energy significantly. This usually leads to the collapse of the network at a very early stage. In the past decade, although many energy-aware dynamic data dissemination methods have been proposed to alleviate the hotspots issue, the block characteristic of sensor nodes has been overlooked and how to offload traffic from hot blocks with low energy through long-distance hybrid dissemination remains an open problem. In this paper, we developed a block-aware data dissemination model to balance the inter-block energy and eliminate the spreading of intra-block hotspots. Through the clustering mechanism based on geography and energy, "similar" large-scale sensor nodes can be efficiently grouped into specific blocks to form the global block information (GBI). Based on GBI, the long-distance block-cross hybrid algorithms are further developed by effectively aggregating inter-block and intra-block data disseminations. Extensive experimental results demonstrate the capability and the efficiency of the proposed approach. © 2014 Elsevier Ltd.

On the multicast lifetime of WANETs with multibeam antennas : Formulation, algorithms, and analysis

We explore the multicast lifetime capacity of energy-limited wireless ad hoc networks using directional multibeam antennas by formulating and solving the corresponding optimization problem. In such networks, each node is equipped with a practical smart antenna array that can be configured to support multiple beams with adjustable orientation and beamwidth. The special case of this optimization problem in networks with single beams have been extensively studied and shown to be NP-hard. In this paper, we provide a globally optimal solution to this problem by developing a general MILP formulation that can apply to various configurable antenna models, many of which are not supported by the existing formulations. In order to study the multicast lifetime capacity of large-scale networks, we also propose an efficient heuristic algorithm with guaranteed theoretical performance. In particular, we provide a sufficient condition to determine if its performance reaches optimum based on the analysis of its approximation ratio. These results are validated by experiments as well. The multicast lifetime capacity is then quantitatively studied by evaluating the proposed exact and heuristic algorithms using simulations. The experimental results also show that using two-beam antennas can exploit most lifetime capacity of the networks for multicast communications. © 2013 IEEE.

An analytical model for optimal spectrum leasing under constraints of quality of service in CRNs

Cognitive radio improves spectrum efficiency and mitigates spectrum scarcity by allowing cognitive users to opportunistically access idle chunks of the spectrum owned by licensed users. In long-term spectrum leasing markets, secondary network operators make a decision about how much spectrum is optimal to fulfill their users' data transmission requirements. We study this optimization problem in multiple channel scenarios. Under the constrains of expected user admission rate and quality of service, we model the secondary network into a dynamic data transportation system. In this system, the spectrum accesses of both primary users and secondary users are in accordance with stochastic processes, respectively. The main metrics of quality of service we are concerned with include user admission rate, average transmission delay and stability of the delay. To quantify the relationship between spectrum provisioning and quality of service, we propose an approximate analytical model. We use the model to estimate the lower and upper bounds of the optimal amount of the spectrum. The distance between the bounds is relatively narrow. In addition, we design a simple algorithm to compute the optimum by using the bounds. We conduct numerical simulations on a slotted multiple channel dynamic spectrum access network model. Simulation results demonstrate the preciseness of the proposed model. Our work sheds light on the design of game and auction based dynamic spectrum sharing mechanisms in cognitive radio networks.

Control flow-based malware variant detection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms. © 2013 IEEE.

Robust multi-factor authentication for fragile communications

In large-scale systems, user authentication usually needs the assistance from a remote central authentication server via networks. The authentication service however could be slow or unavailable due to natural disasters or various cyber attacks on communication channels. This has raised serious concerns in systems which need robust authentication in emergency situations. The contribution of this paper is two-fold. In a slow connection situation, we present a secure generic multi-factor authentication protocol to speed up the whole authentication process. Compared with another generic protocol in the literature, the new proposal provides the same function with significant improvements in computation and communication. Another authentication mechanism, which we name stand-alone authentication, can authenticate users when the connection to the central server is down. We investigate several issues in stand-alone authentication and show how to add it on multi-factor authentication protocols in an efficient and generic way.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Modeling and analysis on the propagation dynamics of modern email malware

Due to the critical security threats imposed by email-based malware in recent years, modeling the propagation dynamics of email malware becomes a fundamental technique for predicting its potential damages and developing effective countermeasures. Compared to earlier versions of email malware, modern email malware exhibits two new features, reinfection and self-start. Reinfection refers to the malware behavior that modern email malware sends out malware copies whenever any healthy or infected recipients open the malicious attachment. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. In the literature, several models are proposed for email malware propagation, but they did not take into account the above two features and cannot accurately model the propagation dynamics of modern email malware. To address this problem, we derive a novel difference equation based analytical model by introducing a new concept of virtual infected user. The proposed model can precisely present the repetitious spreading process caused by reinfection and self-start and effectively overcome the associated computational challenges. We perform comprehensive empirical and theoretical study to validate the proposed analytical model. The results show our model greatly outperforms previous models in terms of estimation accuracy. © 2013 IEEE.

Internet traffic clustering with side information

Internet traffic classification is a critical and essential functionality for network management and security systems. Due to the limitations of traditional port-based and payload-based classification approaches, the past several years have seen extensive research on utilizing machine learning techniques to classify Internet traffic based on packet and flow level characteristics. For the purpose of learning from unlabeled traffic data, some classic clustering methods have been applied in previous studies but the reported accuracy results are unsatisfactory. In this paper, we propose a semi-supervised approach for accurate Internet traffic clustering, which is motivated by the observation of widely existing partial equivalence relationships among Internet traffic flows. In particular, we formulate the problem using a Gaussian Mixture Model (GMM) with set-based equivalence constraint and propose a constrained Expectation Maximization (EM) algorithm for clustering. Experiments with real-world packet traces show that the proposed approach can significantly improve the quality of resultant traffic clusters. © 2014 Elsevier Inc.

Containing smartphone worm propagation with an influence maximization algorithm

In recent years, wide attention has been drawn to the problem of containing worm propagation in smartphones. Unlike existing containment models for worm propagation, we study how to prevent worm propagation through the immunization of key nodes (e.g.; the top k influential nodes). Thus, we propose a novel containment model based on an influence maximization algorithm. In this model, we introduce a social relation graph to evaluate the influence of nodes and an election mechanism to find the most influential nodes. Finally, this model provides a targeted immunization strategy to disable worm propagation by immunizing the top k influential nodes. The experimental results show that the model not only finds the most influential top k nodes quickly, but also effectively restrains and controls worm propagation.

Are the popular users always important for information dissemination in online social networks?

This article verifies the importance of popular users in OSNs. The results are counter-intuitive. First, for dissemination speed, a large amount of users can swiftly distribute information to the masses, but they are not highly-connected users. Second, for dissemination scale, many powerful forwarders in OSNs cannot be identified by the degree measure. Furthermore, to control dissemination, popular users cannot capture most bridges of social communities.

Analysis of backward congestion notification with delay for enhanced Ethernet networks

At present, companies and standards organizations are enhancing Ethernet as the unified switch fabric for all of the TCP/IP traffic, the storage traffic and the high performance computing traffic in data centers. Backward congestion notification (BCN) is the basic mechanism for the end-to-end congestion management enhancement of Ethernet. To fulfill the special requirements of the unified switch fabric, i.e., losslessness and low transmission delay, BCN should hold the buffer occupancy around a target point tightly. Thus, the stability of the control loop and the buffer size are critical to BCN. Currently, the impacts of delay on the performance of BCN are unidentified. When the speed of Ethernet increases to 40 Gbps or 100 Gbps in the near future, the number of on-the-fly packets becomes the same order with the buffer size of switch. Accordingly, the impacts of delay will become significant. In this paper, we analyze BCN, paying special attention on the delay. We model the BCN system with a set of segmented delayed differential equations, and then deduce sufficient condition for the uniformly asymptotic stability of BCN. Subsequently, the bounds of buffer occupancy are estimated, which provides direct guidelines on setting buffer size. Finally, numerical analysis and experiments on the NetFPGA platform verify our theoretical analysis.

Characterization and optimization to improve uneven surface on MEMS bridge fabrication

This paper presents an optimized fabrication method for developing a freestanding bridge for RF MEMS switches. In this method, the sacrificial layer is patterned and hard baked a 220°C for 3min, after filling the gap between the slots of the coplanar waveguide. Measurement results by AFM and SEM demonstrate that this technique significantly improves the planarity of the sacrificial layer, reducing the uneven surface to less than 20nm, and the homogeneity of the Aluminum thickness across the bridge. Moreover, a mixture of O2, Ar and CF4 was used and optimized for dry releasing of the bridge. A large membrane (200×100μm2) was released without any surface bending. Therefore, this method not only simplifies the fabrication process, but also improves the surface flatness and edge smoothness of the bridge. This fabrication method is fully compatible with standard silicon IC technology.