The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: &#x201c;privacy&#x201d; and &#x201c;incentive&#x201d; which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

Xiaoguang Niu

Jiawei Wang

Qiongzan Ye

Yihao Zhang

A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

DNS tunneling is a method used by malicious users who intend to bypass the firewall to send or receive commands and data. This has a significant impact on revealing or releasing classified information. Several researchers have examined the use of machine learning in terms of detecting DNS tunneling. However, these studies have treated the problem of DNS tunneling as a binary classification where the class label is either legitimate or tunnel. In fact, there are different types of DNS tunneling such as FTP-DNS tunneling, HTTP-DNS tunneling, HTTPS-DNS tunneling, and POP3-DNS tunneling. Therefore, there is a vital demand to not only detect the DNS tunneling but rather classify such tunnel. This study aims to propose a multilabel support vector machine in order to detect and classify the DNS tunneling. The proposed method has been evaluated using a benchmark dataset that contains numerous DNS queries and is compared with a multilabel Bayesian classifier based on the number of corrected classified DNS tunneling instances. Experimental results demonstrate the efficacy of the proposed SVM classification method by obtaining an <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.42947pt" id="M1" height="12.7178pt" version="1.1" viewBox="-0.0498162 -9.28833 8.47692 12.7178" width="8.47692pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-103" d="M619 670C619 686 593 712 555 712S459 686 410 634S335 504 320 430H250L219 400L222 388H312L258 73C223 -133 201 -166 187 -180C175 -191 158 -199 140 -199C123 -199 88 -188 74 -172C68 -166 63 -164 54 -171C38 -185 23 -201 23 -215C23 -236 60 -261 93 -261C122 -261 161 -247 207 -200C268 -138 300 -71 337 94C365 220 376 277 394 387L501 399L521 430H401C432 623 464 665 501 665C524 665 544 651 567 627C577 617 583 618 592 625C601 631 619 651 619 670Z"/></g></svg>-measure of 0.80.

Ahmed Almusawi

Haleh Amintoosi

DNS Tunneling Detection Method Based on Multilabel Support Vector Machine

We propose a two-step integral imaging coding based three-dimensional (3D) information encryption approach in this paper. In this approach, a synthetic aperture integral imaging system is applied to acquire a set of parallax images including spatial and angular information of 3D scene. In the encryption process, two-step coding is performed. In the first step, the acquired parallax images are encrypted firstly by double random-phase coding in the Fresnel domain. In the second step, these encrypted parallax images are encoded into a cipher image by mapping algorithm which is used to generate elemental image array of integral imaging. In the decryption process, an inverse operation is performed. The experimental results demonstrate the feasibility, security, and robustness of the proposed approach.

Min Zhao

Yan Xing

Xiao-Wei Li

Qiong-Hua Wang

Two-Step Integral Imaging Coding Based Three-Dimensional Information Encryption Approach

Network faults like link or switch failures can cause heavy congestion and packet loss. Traffic engineering systems need a lot of time to detect and react to such faults, which results in significant recovery times. Recent work either preinstalls a lot of backup paths in the switches to ensure fast rerouting or proactively prereserves bandwidth to achieve fault resiliency. Our idea agilely reacts to failures in the data plane while eliminating the preinstallation of backup paths. We propose Kuijia, a robust traffic engineering system for data center WANs, which relies on a novel failover mechanism in the data plane called rate rescaling. The victim flows on failed tunnels are rescaled to the remaining tunnels and enter lower priority queues to avoid performance impairment of aboriginal flows. Real system experiments show that Kuijia is effective in handling network faults and significantly outperforms the conventional rescaling method.

Che Zhang

Hong Xu

Libin Liu

Zhixiong Niu

Peng Wang

Kuijia: Traffic Rescaling in Software-Defined Data Center WANs

Software-Defined Networking (SDN) has quickly emerged as a promising technology for future networks and gained much attention. However, the centralized nature of SDN makes the system vulnerable to denial-of-services (DoS) attacks, especially for the currently widely deployed multicontroller system. Due to DoS attacks, SDN multicontroller model may additionally face the risk of the cascading failures of controllers. In this paper, we propose SDNManager, a lightweight and fast denial-of-service detection and mitigation system for SDN. It has five components: monitor, forecast engine, checker, updater, and storage service. It typically follows a control loop of reading flow statistics, forecasting flow bandwidth changes based on the statistics, and accordingly updating the network. It is worth noting that the forecast engine employs a novel dynamic time-series (DTS) model which greatly improves bandwidth prediction accuracy. What is more, to further optimize the defense effect, we also propose a controller dynamic scheduling strategy to ensure the global network state optimization and improve the defense efficiency. We evaluate SDNManager through a prototype implementation tested in a real SDN network environment. The results show that SDNManager is effective with adding only a minor overhead into the entire SDN/OpenFlow infrastructure.

Tao Wang

Hongchang Chen

Guozhen Cheng

Yulin Lu

SDNManager: A Safeguard Architecture for SDN DoS Attacks Based on Bandwidth Prediction

As the most competitive solution for next-generation network, SDN and its dominant implementation OpenFlow are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also introduces new kinds of limitations and security issues. Of these limitations, the most obvious and maybe the most neglected one is the flow table capacity of SDN/OpenFlow switches. In this paper, we proposed a novel inference attack targeting at SDN/OpenFlow network, which is motivated by the limited flow table capacities of SDN/OpenFlow switches and the following measurable network performance decrease resulting from frequent interactions between data and control plane when the flow table is full. To the best of our knowledge, this is the first proposed inference attack model of this kind for SDN/OpenFlow. We implemented an inference attack framework according to our model and examined its efficiency and accuracy. The evaluation results demonstrate that our framework can infer the network parameters (flow table capacity and usage) with an accuracy of 80&#x25; or higher. We also proposed two possible defense strategies for the discovered vulnerability, including routing aggregation algorithm and multilevel flow table architecture. These findings give us a deeper understanding of SDN/OpenFlow limitations and serve as guidelines to future improvements of SDN/OpenFlow.

Yadong Zhou

Kaiyue Chen

Junjie Zhang

Junyuan Leng

Yazhe Tang

Exploiting the Vulnerability of Flow Table Overflow in Software-Defined Network: Attack Model, Evaluation, and Defense

Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2064009pt" id="M1" height="8.8423pt" version="1.1" viewBox="-0.0498162 -8.6359 11.0475 8.8423" width="11.0475pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-79" d="M822 650H589L583 622C660 617 677 607 674 561C672 534 664 481 647 390L600 137H596L273 650H126L120 622C176 620 194 615 207 594C221 571 225 557 214 504L161 257C141 166 129 112 121 85C108 42 83 30 29 28L23 0H260L266 28C193 33 173 42 176 89C178 122 186 172 202 255L256 527H259L583 -8H612L690 390C708 481 720 535 728 558C744 603 756 619 816 622L822 650Z"/></g></svg>-gram-based malware detection which is similar to our method.

Jinpei Yan

Yong Qi

Qifan Rao

LSTM-Based Hierarchical Denoising Network for Android Malware Detection

Wireless covert channel is an emerging covert communication technique which conceals the very existence of secret information in wireless signal including GSM, CDMA, and LTE. The secret message bits are always modulated into artificial noise superposed with cover signal, which is then demodulated with the shared codebook at the receiver. In this paper, we first extend the traditional KS test and regularity test in covert timing channel detection into wireless covert channel, which can be used to reveal the very existence of secret data in wireless covert channel from the aspect of multiorder statistics. In order to improve the undetectability, a wireless covert channel for OFDM-based communication system based on constellation shaping modulation is proposed, which generates additional constellation points around the standard points in normal constellations. The carrier signal is then modulated with the dirty constellation and the secret message bits are represented by the selection mode of the additional constellation points; shaping modulation is employed to keep the distribution of constellation errors unchanged. Experimental results show that the proposed wireless covert channel scheme can resist various statistical detections. The communication reliability under typical interference is also proved.

Pengcheng Cao

Weiwei Liu

Guangjie Liu

Xiaopeng Ji

Jiangtao Zhai

Yuewei Dai

A Wireless Covert Channel Based on Constellation Shaping Modulation

Compared with traditional paper medical records, electronic medical records have many advantages such as easy transmission, high efficiency, better accuracy, and easier storage. The further development and penetration of electronic medical records will raise increasingly critical transmitted-data accuracy and security issues. Previous studies have proposed a verifier-based three-party authentication scheme and to provide high efficiency and security, with low computation and transmission costs. However, this protocol fails to achieve anonymity, is vulnerable to tracking attacks, and is inefficient. In this paper, we propose a new authentication scheme which provides patient anonymity and resistance to tracking attacks, while reducing computation and communication costs. The proposed system is easier to implement and is more suitable for use in remote electronic medical record exchange systems.

Shin-Yan Chiou

Ching-Hsuan Lin

An Efficient Three-Party Authentication Scheme for Data Exchange in Medical Environment

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<p><em>Security and Communication Networks</em> provides a prestigious forum for the R&amp;D community in academia and industry working at the interdisciplinary nexus of next generation communications technologies for security implementations in all network layers.</p>


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<p>Chief Editor Dr Roberto Di Pietro&nbsp;is a Full Professor of Computer Science at KAUST, Saudi Arabia. His research interests include AI-based cybersecurity, distributed systems security, cloud security, and wireless security.&nbsp;</p>


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Security and Communication Networks - Table of contents 2018

Security and Communication Networks | Hindawi

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Security and Communication Networks provides a prestigious forum for the R&D community in academia and industry working at the interdisciplinary nexus of next generation communications technologies for security implementations in all network layers.

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Security and Communication Networks