Biblio

Strings are used to model genomic, natural language, and web activity data, and are thus often shared broadly. However, string data sharing has raised privacy concerns stemming from the fact that knowledge of length-k substrings of a string and their frequencies (multiplicities) may be sufficient to uniquely reconstruct the string; and from that the inference of such substrings may leak confidential information. We thus introduce the problem of protecting length-k substrings of a single string S by applying Differential Privacy (DP) while maximizing data utility for frequency-based mining tasks. Our theoretical and empirical evidence suggests that classic DP mechanisms are not suitable to address the problem. In response, we employ the order-k de Bruijn graph G of S and propose a sampling-based mechanism for enforcing DP on G. We consider the task of enforcing DP on G using our mechanism while preserving the normalized edge multiplicities in G. We define an optimization problem on integer edge weights that is central to this task and develop an algorithm based on dynamic programming to solve it exactly. We also consider two variants of this problem with real edge weights. By relaxing the constraint of integer edge weights, we are able to develop linear-time exact algorithms for these variants, which we use as stepping stones towards effective heuristics. An extensive experimental evaluation using real-world large-scale strings (in the order of billions of letters) shows that our heuristics are efficient and produce near-optimal solutions which preserve data utility for frequency-based mining tasks.

The recent technological advances and changes in the daily human activities increased the production and sharing of data. In the ecosystem of interconnected systems, data can be circulated among systems for various reasons. This could lead to exchange of private or sensitive information between entities. Data Sanitisation involves processes and practices that remove sensitive and private information from documents before sharing them with entities that should not have access to this information. This paper presents the design and development of a data sanitisation and redaction solution for a Cyber Threat Intelligence sharing platform. The Data Sanitisation and Redaction Plugin has been designed with the purpose of operating as a plugin for the ECHO Project’s Early Warning System platform and enhancing its operative capabilities during information sharing. This plugin aims to provide automated security and privacy-based controls to the concept of CTI sharing over a ticketing system. The plugin has been successfully tested and the results are presented in this paper.

Network traffic detection is closely related to network security, and it is also a hot research topic now. With the development of encryption technology, traffic detection has become more and more difficult, and many crimes have occurred on the dark web, so how to detect dark web traffic is the subject of this study. In this paper, we proposed a dark web traffic(Tor traffic) detection scheme based on deep learning and conducted experiments on public data sets. By analyzing the results of the experiment, our detection precision rate reached 95.47%.

Software Defined Networking (SDN) has changed the way of designing and managing networks through programmability. However, programmability also introduces security threats. In this work we address the issue of malicious hosts running malicious applications that bypass the standard SDN based detection mechanisms. The SDN security system we are proposing periodically monitors the system calls utilization of the different SDN applications installed, learns from past system behavior using machine learning classifiers, and thus accurately detects the existence of an unusual activity or a malicious application.

In view of the common edge computing-based cloud-side collaborative environment summary existing search key and authentication key sharing caused by data information leakage, this paper proposes a cryptographic search based on public key searchable encryption in an edge computing environment method, this article uses the public key to search for the characteristics of the encryption algorithm, and allows users to manage the corresponding private key. In the process of retrieval and execution, the security of the system can be effectively ensured through the secret trapdoor. Through the comparison of theoretical algorithms, the searchable encryption scheme in the edge computing environment proposed in this paper can effectively reduce the computing overhead on the user side, and complete the over-complex computing process on the edge server or the central server, which can improve the overall efficiency of encrypted search.

Dynamic searchable symmetric encryption (DSSE) that enables a client to perform searches and updates on encrypted data has been intensively studied in cloud computing. Recently, forward privacy and backward privacy has engaged significant attention to protect DSSE from the leakage of updates. However, the research in this field almost focused on keyword-level updates. That is, the client needs to know the keywords of the documents in advance. In this paper, we proposed a document-level update scheme, DBP, which supports immediate deletion while guaranteeing forward privacy and backward privacy. Compared with existing forward and backward private DSSE schemes, our DBP scheme has the following merits: 1) Practicality. It achieves deletion based on document identifiers rather than document/keyword pairs; 2) Efficiency. It utilizes only lightweight primitives to realize backward privacy while supporting immediate deletion. Experimental evaluation on two real datasets demonstrates the practical efficiency of our scheme.

Searchable symmetric encryption enables users with the secret key to conduct keyword search on encrypted data without decryption. Recently, dynamic searchable symmetric encryption (DSSE) which provides secure functionalities for adding or deleting documents has been studied extensively. Many DSSE schemes construct indexes in order to efficiently conduct keyword search. On the other hand, the indexes constructed in DSSE are complicated and independent to indexes supported by database management systems (DBMSs). Plug-in developments over DBMSs are often restricted, and therefore it is not easy to develop softwares which can deploy DSSE schemes to DBMSs. In this paper, we propose a DBMS-friendly searchable symmetric encryption scheme which can generate indexes suitable for DBMSs. Our index can narrow down encrypted data which should be conducted keyword search, and be combined with well-used indexes supported by many DBMSs. Our index consists of a small portion of an output value of a cryptographic deterministic function (e.g. pseudo-random function or hash function). We also show an experiment result of our scheme deployed to DBMSs.

Physical layer authentication (PLA) has recently been discussed in the context of URLLC due to its low complexity and low overhead. Nevertheless, these schemes also introduce additional sources of error through missed detections and false alarms. The trade-offs of these characteristics are strongly dependent on the deployment scenario as well as the processing architecture. Thus, considering a feature-based PLA scheme utilizing channel-state information at multiple distributed radio-heads, we study these trade-offs analytically. We model and analyze different scenarios of centralized and decentralized decision-making and decoding, as well as the impacts of a single-antenna attacker launching a Sybil attack. Based on stochastic network calculus, we provide worst-case performance bounds on the system-level delay for the considered distributed scenarios under a Sybil attack. Results show that the arrival-rate capacity for a given latency deadline is increased for the distributed scenarios. For a clustered sensor deployment, we find that the distributed approach provides 23% higher capacity when compared to the centralized scenario.

In present scenario features like low-cost, power-efficientand easy-to-implement Wireless Sensor Networks (WSN’s) has become one of growing prospects.though, its security issues have become a popular topic of research nowadays. Specific attacks often experience the security issues as they easily combined with other attacks to destroy the network. In this paper, we discuss about detecting the particular attacks like Sybil, Black-holeand Denial of Service (DoS) attacks on WSNs. These networks are more vulnerable to them. We attempt to investigate the security measures and the applicability of the AODV protocol to detect and manage specific types of network attacks in VANET.The RSA algorithm is proposed here, as it is capable of detecting sensor nodes ormessages transmitted from sensor nodes to the base station and prevents network from being attacked by the source node. It also improves the security mechanism of the AODV protocol. This simulation set up is performed using MATLAB simulation tool

Social conflicts appearing in the media are increasing public awareness about security issues, resulting in a higher demand of more exhaustive environment monitoring methods. Automatic video surveillance systems are a powerful assistance to public and private security agents. Since the arrival of deep learning, object detection and classification systems have experienced a large improvement in both accuracy and versatility. However, deep learning-based object detection and classification systems often require expensive GPU-based hardware to work properly. This paper presents a novel deep learning-based foreground anomalous object detection system for video streams supplied by panoramic cameras, specially designed to build power efficient video surveillance systems. The system optimises the process of searching for anomalous objects through a new potential detection generator managed by three different multivariant homoscedastic distributions. Experimental results obtained after its deployment in a Jetson TX2 board attest the good performance of the system, postulating it as a solvent approach to power saving video surveillance systems.

Point-of-care diagnostics are a key technology for various safety-critical applications from providing diagnostics in developing countries lacking adequate medical infrastructure to fight infectious diseases to screening procedures for border protection. Digital microfluidics biochips are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. In such a technology, processing errors are inherent. Cyber-physical digital biochips offer higher reliability through the inclusion of automated error recovery mechanisms that can reconfigure operations performed on the electrode array. Recent research has begun to explore security vulnerabilities of digital microfluidic systems. This paper expands previous work that exploits vulnerabilities due to implicit trust in the error recovery mechanism. In this work, a discriminative data mining approach is introduced to identify frequent bioassay operations that can be cyber-physically attested for runtime security protection.

The router's buffer size imposes significant impli-cations on the performance of the network. Network operators nowadays configure the router's buffer size manually and stati-cally. They typically configure large buffers that fill up and never go empty, increasing the Round-trip Time (RTT) of packets significantly and decreasing the application performance. Few works in the literature dynamically adjust the buffer size, but are implemented only in simulators, and therefore cannot be tested and deployed in production networks with real traffic. Previous work suggested setting the buffer size to the Bandwidth-delay Product (BDP) divided by the square root of the number of long flows. Such formula is adequate when the RTT and the number of long flows are known in advance. This paper proposes a system that leverages programmable switches as passive instruments to measure the RTT and count the number of flows traversing a legacy router. Based on the measurements, the programmable switch dynamically adjusts the buffer size of the legacy router in order to mitigate the unnecessary large queuing delays. Results show that when the buffer is adjusted dynamically, the RTT, the loss rate, and the fairness among long flows are enhanced. Additionally, the Flow Completion Time (FCT) of short flows sharing the queue is greatly improved. The system can be adopted in campus, enterprise, and service provider networks, without the need to replace legacy routers.

Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only.In this paper, a domain-agnostic framework that creates natural language interfaces for computer-controlled systems has been developed by creating a customizable mapping between the language constructs and the system primitives. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation and machine learning models for language processing tasks.

Arbitrary style transfer refers to generate a new image based on any set of existing images. Meanwhile, the generated image retains the content structure of one and the style pattern of another. In terms of content retention and style transfer, the recent arbitrary style transfer algorithms normally perform well in one, but it is difficult to find a trade-off between the two. In this paper, we propose the Deep Content Guidance Network (DCGN) which is stacked by content guidance (CG) layers. And each CG layer involves one position self-attention (pSA) module, one channel self-attention (cSA) module and one content guidance attention (cGA) module. Specially, the pSA module extracts more effective content information on the spatial layout of content images and the cSA module makes the style representation of style images in the channel dimension richer. And in the non-local view, the cGA module utilizes content information to guide the distribution of style features, which obtains a more detailed style expression. Moreover, we introduce a new permutation loss to generalize feature expression, so as to obtain abundant feature expressions while maintaining content structure. Qualitative and quantitative experiments verify that our approach can transform into better stylized images than the state-of-the-art methods.

This article describes the definition, theoretical derivation, design ideas, and specific implementation of the nearest query algorithm for the acceleration of probabilistic optimization at first, and secondly gives an optimization conclusion that is generally applicable to high-dimensional Minkowski spaces with even-numbered feature parameters. Thirdly the operating efficiency and space sensitivity of this algorithm and the commonly used algorithms are compared from both theoretical and experimental aspects. Finally, the optimization direction is analyzed based on the results.

Data-Centric Networking (DCN) is a research project based on Named Data Networking (NDN), which focuses on the high-performance name-based forwarder, distributed pub/sub data distribution platform, distributed network storage, in-network processing platform, and blockchain-based data trading platform. In this paper, we present a design of real-time public Internet of Things (IoT) data distribution platform which is based on a Data-Centric Networking (DCN) distributed pub/sub data distribution platform.

In order to improve the status monitoring and management ability of wireless sensor networks, a dynamic data compression method based on grid deduplication is proposed. Grid-based sensor node spatial positioning and big data fusion method are adopted to realize dynamic feature mining of wireless sensor network data, extract feature sequence points of wireless sensor network data, reconstruct wireless sensor network data feature space by adopting spatial grid node recombination, build a statistical detection model of dynamic feature mining of wireless sensor network data by combining grid area grouping compression method, and realize embedded fuzzy control and joint feature distributed adaptive learning. The association matching degree of wireless sensor network data is analyzed. Combining fuzzy subspace compression and big data fusion clustering, the quantitative regression analysis model of wireless sensor network data is established. The time series reorganization of wireless sensor network database is realized by index table name, index column and other information. Compressed sensing method is used in linear fusion subspace to realize data compression and adaptive detection of wireless sensor network. Constraint feature points of wireless sensor network data compression are constructed, and dynamic compression and clustering processing of wireless sensor network data are realized at constraint points. Simulation results show that the feature clustering of data compression in wireless sensor networks is better and the storage space of data is reduced.

Ensuring the network security, resists the malicious traffic attacks as much as possible, and ensuring the network security, the Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) are combined. Then, a Software Defined Networking (SDN) anomaly detection architecture is built and continuously optimized to ensure network security as much as possible and enhance the reliability of the detection architecture. The results show that the proposed network architecture can greatly improve the accuracy of detection, and its performance will be different due to the different number of CNN layers. When the two-layer CNN structure is selected, its performance is the best among all algorithms. Especially, the accuracy of GRU- CNN-2 is 98.7%, which verifies that the proposed method is effective. Therefore, under deep learning, the utilization of GRU- CNN to explore and optimize the SDN anomaly detection is of great significance to ensure information transmission security in the future.

Recently, vehicular communications have been the focus of industry, research and development fields. There are many benefits of vehicular communications. It improves traffic management and put derivers in better control of their vehicles. Privacy and security protection are collective accountability in which all parties need to actively engage and collaborate to afford safe and secure communication environments. The primary objective of this paper is to exploit the RSS characteristic of physical layer, in order to generate a secret key that can securely be exchanged between legitimated communication vehicles. In this paper, secret key extraction from wireless channel will be the main focus of the countermeasures against VANET security attacks. The technique produces a high rate of bits stream while drop less amount of information. Information reconciliation is then used to remove dissimilarity of two initially extracted keys, to increase the uncertainty associated to the extracted bits. Five values are defined as quantization thresholds for the captured probes. These values are derived statistically, adaptively and randomly according to the readings obtained from the received signal strength.

As we all know, network coding can significantly improve the throughput and reliability of wireless networks. However, in the high-speed mobile environment, the packet loss rate of different wireless links may vary greatly due to the time-varying network state, which makes the adjustment of network coding redundancy very important. Because the network coding redundancy is too large, it will lead to excessive overhead and reduce the effective throughput. If the network coding redundancy is too small, it will lead to insufficient decoding, which will also reduce the effective throughput. In the design of multi-path transmission scheduling scheme, we introduce adaptive redundancy network coding scheme. By using multiple links to aggregate network bandwidth, we choose appropriate different coding redundancy for different links to resist the performance loss caused by link packet loss. The simulation results show that when the link packet loss rate is greatly different, the mechanism can not only ensure the transmission reliability, but also greatly reduce the total network redundancy to improve the network throughput very effectively.

Recently, Martínez-Peñas and Kschischang (IEEE Trans. Inf. Theory, 2019) showed that lifted linearized Reed-Solomon codes are suitable codes for error control in multishot network coding. We show how to construct and decode lifted interleaved linearized Reed-Solomon codes. Compared to the construction by Martínez-Peñas-Kschischang, interleaving allows to increase the decoding region significantly (especially w.r.t. the number of insertions) and decreases the overhead due to the lifting (i.e., increases the code rate), at the cost of an increased packet size. The proposed decoder is a list decoder that can also be interpreted as a probabilistic unique decoder. Although our best upper bound on the list size is exponential, we present a heuristic argument and simulation results that indicate that the list size is in fact one for most channel realizations up to the maximal decoding radius.

With almost every new vehicle being connected, the importance of vehicle data is growing rapidly. Many mobility applications rely on the fusion of data coming from heterogeneous data sources, like vehicle and "smart-city" data or process data generated by systems out of their control. This external data determines much about the behaviour of the relying applications: it impacts the reliability, security and overall quality of the application's input data and ultimately of the application itself. Hence, knowledge about the provenance of that data is a critical component in any data-driven system. The secure traceability of the data handling along the entire processing chain, which passes through various distinct systems, is critical for the detection and avoidance of misuse and manipulation. In this paper, we introduce a mechanism for establishing secure data provenance in real time, demonstrating an exemplary use-case based on a machine learning model that detects dangerous driving situations. We show with our approach based on W3C decentralized identity standards that data provenance in closed data systems can be effectively achieved using technical standards designed for an open data approach.

In this work a data provenance system for grid-oriented applications is presented. The proposed Keyless Infrastructure Security Solution (KISS) provides mechanisms to store and maintain digital data fingerprints that can later be used to validate and assert data provenance using a time-based, hash tree mechanism. The developed solution has been designed to satisfy the stringent requirements of the modern power grid including execution time and storage necessities. Its applicability has been tested using a lab-scale, proof-of-concept deployment that secures an energy management system against the attack sequence observed on the 2016 Ukrainian power grid cyberattack. The results demonstrate a strong potential for enabling data provenance in a wide array of applications, including speed-sensitive applications such as those found in control room environments.

Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns in accordance with domain knowledge and visual perception is extremely difficult. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To address these issues, we propose to use a deep convolutional embedding model for digitized painting clustering, in which the task of mapping the raw input data to an abstract, latent space is jointly optimized with the task of finding a set of cluster centroids in this latent feature space. Quantitative and qualitative experimental results show the effectiveness of the proposed method. The model is also capable of outperforming other state-of-the-art deep clustering approaches to the same problem. The proposed method can be useful for several art-related tasks, in particular visual link retrieval and historical knowledge discovery in painting datasets.

The digital transformation is injecting energy into economic growth and governance improvement for the China government. Digital governance and e-government services are playing a more and more important role in public management and social governance. Meanwhile, cyber-attacks and threats become the major challenges for e-government application systems. In this paper, we proposed a novel dynamic access entry scheme for web application, which provide a rapidly-changing defender-controlled attack surface based on Moving Target Defense (MTD) technology. The scheme can turn the static keywords of Uniform Resource Locator (URL) into the dynamic and random ones, which significantly increase the cost to adversaries attack. We present the prototype of the proposed scheme and evaluate the feasibility and effectiveness. The experimental results demonstrated the scheme is practical and effective.