Biblio

In this paper, we describe an efficient methodology to guide investigators during network forensic analysis. To this end, we introduce the concept of core attack graph, a compact representation of the main routes an attacker can take towards specific network targets. Such compactness allows forensic investigators to focus their efforts on critical nodes that are more likely to be part of attack paths, thus reducing the overall number of nodes (devices, network privileges) that need to be examined. Nevertheless, core graphs also allow investigators to hierarchically explore the graph in order to retrieve different levels of summarised information. We have evaluated our approach over different network topologies varying parameters such as network size, density, and forensic evaluation threshold. Our results demonstrate that we can achieve the same level of accuracy provided by standard logical attack graphs while significantly reducing the exploration rate of the network.

We propose a method for comparative analysis of evaluation of the cryptographic strength of the asymmetric encryption algorithms RSA and the existing GOST R 34.10-2001. Describes the fundamental design ratios, this method is based on computing capacity used for decoding and the forecast for the development of computer technology.

More and more medical data are shared, which leads to disclosure of personal privacy information. Therefore, the construction of medical data privacy preserving publishing model is of great value: not only to make a non-correspondence between the released information and personal identity, but also to maintain the data utility after anonymity. However, there is an inherent contradiction between the anonymity and the data utility. In this paper, a Principal Component Analysis-Grey Relational Analysis (PCA-GRA) K anonymous algorithm is proposed to improve the data utility effectively under the premise of anonymity, in which the association between quasi-identifiers and the sensitive information is reckoned as a criterion to control the generalization hierarchy. Compared with the previous anonymity algorithms, results show that the proposed PCA-GRA K anonymous algorithm has achieved significant improvement in data utility from three aspects, namely information loss, feature maintenance and classification evaluation performance.

High-accuracy localization is a prerequisite for many wireless applications. To obtain accurate location information, it is often required to share users' positional knowledge and this brings the risk of leaking location information to adversaries during the localization process. This paper develops a theory and algorithms for protecting location secrecy. In particular, we first introduce a location secrecy metric (LSM) for a general measurement model of an eavesdropper. Compared to previous work, the measurement model accounts for parameters such as channel conditions and time offsets in addition to the positions of users. We determine the expression of the LSM for typical scenarios and show how the LSM depends on the capability of an eavesdropper and the quality of the eavesdropper's measurement. Based on the insights gained from the analysis, we consider a case study in wireless localization network and develop an algorithm that diminish the eavesdropper's capabilities by exploiting the reciprocity of channels. Numerical results show that the proposed algorithm can effectively increase the LSM and protect location secrecy.

Spatial information network is an important part of the integrated space-terrestrial information network, its bearer services are becoming increasingly complex, and real-time requirements are also rising. Due to the structural vulnerability of the spatial information network and the dynamics of the network, this poses a serious challenge to how to ensure reliable and stable data transmission. The structural vulnerability of the spatial information network and the dynamics of the network brings a serious challenge of ensuring reliable and stable data transmission. Software Defined Networking (SDN), as a new network architecture, not only can quickly adapt to new business, but also make network reconfiguration more intelligent. In this paper, SDN is used to design the spatial information network architecture. An optimization algorithm for network self-healing based on SDN is proposed to solve the failure of switching node. With the guarantee of Quality of Service (QoS) requirement, the link is updated with the least link to realize the fast network reconfiguration and recovery. The simulation results show that the algorithm proposed in this paper can effectively reduce the delay caused by fault recovery.

In the multi-robot applications, the maintained and desired network may be destroyed by failed robots. The existing self-healing algorithms only handle with the case of single robot failure, however, multiple robot failures may cause several challenges, such as disconnected network and conflicts among repair paths. This paper presents a distributed self-healing algorithm based on 2-hop neighbor infomation to resolve the problems caused by multiple robot failures. Simulations and experiment show that the proposed algorithm manages to restore connectivity of the mobile robot network and improves the synchronization of the network globally, which validate the effectiveness of the proposed algorithm in resolving multiple robot failures.

Differential privacy is a rigorous privacy standard that has been applied to a range of data analysis tasks. To broaden the application scenarios of differential privacy when data records have dependencies, the notion of Bayesian differential privacy has been recently proposed. However, it is unknown whether Bayesian differential privacy preserves three nice properties of differential privacy: sequential composability, parallel composability, and post-processing. In this paper, we provide an affirmative answer to this question; i.e., Bayesian differential privacy still have these properties. The idea behind sequential composability is that if we have m algorithms Y1, Y2,łdots, Ym, where Y$\mathscrl$ is independently $ε\mathscrl$-Bayesian differential private for $\mathscrl$ = 1,2,łdots, m, then by feeding the result of Y1 into Y2, the result of Y2 into Y3, and so on, we will finally have an $Σ$m$\mathscrl$=;1 $ε\mathscrl$-Bayesian differential private algorithm. For parallel composability, we consider the situation where a database is partitioned into m disjoint subsets. The $\mathscrl$-th subset is input to a Bayesian differential private algorithm Y$\mathscrl$, for $\mathscrl$= 1, 2,łdots, m. Then the parallel composition of Y1, Y2,łdots, Ym will be maxm$\mathscrl$=;1=1 $ε\mathscrl$-Bayesian differential private. The postprocessing property means that a data analyst, without additional knowledge abo- t the private database, cannot compute a function of the output of a Bayesian differential private algorithm and reduce its privacy guarantee.

This paper investigates the privacy-preserving problem of the distributed consensus-based energy management considering both generation units and responsive demands in smart grid. First, we reveal the private information of consumers including the electricity consumption and the sensitivity of the electricity consumption to the electricity price can be disclosed without any privacy-preserving strategy. Then, we propose a privacy-preserving algorithm to preserve the private information of consumers through designing the secret functions, and adding zero-sum and exponentially decreasing noises. We also prove that the proposed algorithm can preserve the privacy while keeping the optimality of the final state and the convergence performance unchanged. Extensive simulations validate the theoretical results and demonstrate the effectiveness of the proposed algorithm.

This paper proposed a feedback shift register structure which can be split, it is based on a research of operating characteristics about 70 kinds of cryptographic algorithms and the research shows that the “different operations similar structure” reconfigurable design is feasible. Under the configuration information, the proposed structure can implement the multiplication in finite field GF(2n), the multiply/divide linear feedback shift register and other operations. Finally, this paper did a logic synthesis based on 55nm CMOS standard-cell library and the results show that the proposed structure gets a hardware resource saving of nearly 32%, the average power consumption saving of nearly 55% without the critical delay increasing significantly. Therefore, the “different operations similar structure” reconfigurable design is a new design method and the proposed feedback shift register structure can be an important processing unit for coarse-grained reconfigurable cryptologic array.

A5-1 algorithm is a stream cipher used to encrypt voice data in GSM, which needs to be realized with high performance due to real-time requirements. Traditional implementation on FPGA or ASIC can't obtain a trade-off among performance, cost and flexibility. To this aim, this paper introduces CGRCA to implement A5-1, and in order to optimize the performance and resource consumption, this paper proposes a resource-based path seeking (RPS) algorithm to develop an advanced implementation. Experimental results show that final optimal throughput of A5-1 implemented on CGRCA is 162.87Mbps when the frequency is 162.87MHz, and the set-up time is merely 87 cycles, which is optimal among similar works.

Side-channel collision attacks have been one of the most powerful attack techniques, combining advantages of traditional side-channel attack and mathematical cryptanalysis. In this paper, we propose a novel multiple-bits side-channel collision attack based on double distance voting detection, which can find all 120 relations among 16 key bytes with only 32 averaged power traces when applied to AES (Advanced Encryption Standard) algorithm. Practical attack experiments are performed successfully on a hardware implementation of AES on FPGA board. Results show that the necessary number of traces for our method is about 50% less than correlation-enhanced collision attack and 76% less than binary voting test with 90% success rate.

Cooperation of software and hardware with hybrid architectures, such as Xilinx Zynq SoC combining ARM CPU and FPGA fabric, is a high-performance and low-power platform for accelerating RSA Algorithm. This paper adopts the none-subtraction Montgomery algorithm and the Chinese Remainder Theorem (CRT) to implement high-speed RSA processors, and deploys a 48-node cluster infrastructure based on Zynq SoC to achieve extremely high scalability and throughput of RSA computing. In this design, we use the ARM to implement node-to-node communication with the Message Passing Interface (MPI) while use the FPGA to handle complex calculation. Finally, the experimental results show that the overall performance is linear with the number of nodes. And the cluster achieves 6× 9× speedup against a multi-core desktop (Intel i7-3770) and comparable performance to a many-core server (288-core). In addition, we gain up to 2.5× energy efficiency compared to these two traditional platforms.

Time and cost are the most critical performance metrics for computer systems including embedded system, especially for the battery-based embedded systems, such as PC, mainframe computer, and smart phone. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures this paper proposes an optimal energy efficiency and system performance model and the OTHAP (Optimizing Task Heterogeneous Assignment with Probability) algorithm to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with areal-time embedded systems guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task and The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).

Graphics processing unit (GPU) has been applied successfully in many scientific computing realms due to its superior performances on float-pointing calculation and memory bandwidth, and has great potential in power system applications. The N-1 static security analysis (SSA) appears to be a candidate application in which massive alternating current power flow (ACPF) problems need to be solved. However, when applying existing GPU-accelerated algorithms to solve N-1 SSA problem, the degree of parallelism is limited because existing researches have been devoted to accelerating the solution of a single ACPF. This paper therefore proposes a GPU-accelerated solution that creates an additional layer of parallelism among batch ACPFs and consequently achieves a much higher level of overall parallelism. First, this paper establishes two basic principles for determining well-designed GPU algorithms, through which the limitation of GPU-accelerated sequential-ACPF solution is demonstrated. Next, being the first of its kind, this paper proposes a novel GPU-accelerated batch-QR solver, which packages massive number of QR tasks to formulate a new larger-scale problem and then achieves higher level of parallelism and better coalesced memory accesses. To further improve the efficiency of solving SSA, a GPU-accelerated batch-Jacobian-Matrix generating and contingency screening is developed and carefully optimized. Lastly, the complete process of the proposed GPU-accelerated batch-ACPF solution for SSA is presented. Case studies on an 8503-bus system show dramatic computation time reduction is achieved compared with all reported existing GPU-accelerated methods. In comparison to UMFPACK-library-based single-CPU counterpart using Intel Xeon E5-2620, the proposed GPU-accelerated SSA framework using NVIDIA K20C achieves up to 57.6 times speedup. It can even achieve four times speedup when compared to one of the fastest multi-core CPU parallel computing solution using KLU library. The prop- sed batch-solving method is practically very promising and lays a critical foundation for many other power system applications that need to deal with massive subtasks, such as Monte-Carlo simulation and probabilistic power flow.

This paper presents a solution to a multiple-model based stochastic active fault diagnosis problem over the infinite-time horizon. A general additive detection cost criterion is considered to reflect the objectives. Since the system state is unknown, the design consists of a perfect state information reformulation and optimization problem solution by approximate dynamic programming. An adaptive particle filter state estimation algorithm based on the efficient sample size is proposed to maintain the estimate quality while reducing computational costs. A reduction of information statistics of the state is carried out using non-resampled particles to make the solution feasible. Simulation results illustrate the effectiveness of the proposed design.

In assessing privacy on online social networks, it is important to investigate their vulnerability to reconnaissance strategies, in which attackers lure targets into being their friends by exploiting the social graph in order to extract victims' sensitive information. As the network topology is only partially revealed after each successful friend request, attackers need to employ an adaptive strategy. Existing work only considered a simple strategy in which attackers sequentially acquire one friend at a time, which causes tremendous delay in waiting for responses before sending the next request, and which lack the ability to retry failed requests after the network has changed. In contrast, we investigate an adaptive and parallel strategy, of which attackers can simultaneously send multiple friend requests in batch and recover from failed requests by retrying after topology changes, thereby significantly reducing the time to reach the targets and greatly improving robustness. We cast this approach as an optimization problem, Max-Crawling, and show it inapproximable within (1 - 1/e + $ε$). We first design our core algorithm PM-AReST which has an approximation ratio of (1 - e-(1-1/e)) using adaptive monotonic submodular properties. We next tighten our algorithm to provide a nearoptimal solution, i.e. having a ratio of (1 - 1/e), via a two-stage stochastic programming approach. We further establish the gap bound of (1 - e-(1-1/e)2) between batch strategies versus the optimal sequential one. We experimentally validate our theoretical results, finding that our algorithm performs nearoptimally in practice and that this is robust under a variety of problem settings.

Wireless sensor networks are responsible for sensing, gathering and processing the information of the objects in the network coverage area. Basic data fusion technology generally does not provide data privacy protection mechanism, and the privacy protection mechanism in health care, military reconnaissance, smart home and other areas of the application is usually indispensable. In this paper, we consider the privacy, confidentiality, and the accuracy of fusion results, and propose a data fusion algorithm for privacy preserving. This algorithm relies on the characteristics of data fusion, and uses the method of pre-distribution random number in the node to get the privacy protection requirements of the original data. Theoretical analysis shows that the malicious attacker attempts to steal the difficulty of node privacy in PPND algorithm. At the same time in the TOSSIM simulation results also show that, compared with TAG, SMART algorithm, PPND algorithm in the data traffic, the convergence accuracy of the good performance.

Creating and implementing fault-tolerant distributed algorithms is a challenging task in highly safety-critical industries. Using formal methods supports design and development of complex algorithms. However, formal methods are often perceived as an unjustifiable overhead. This paper presents the experience and insights when using TLA+ and PlusCal to model and develop fault-tolerant and safety-critical modules for TAS Control Platform, a platform for railway control applications up to safety integrity level (SIL) 4. We show how formal methods helped us improve the correctness of the algorithms, improved development efficiency and how part of the gap between model and implementation has been closed by translation to C code. Additionally, we describe how we gained trust in the formal model and tools by following a specific design process called property-driven design, which also implicitly addresses software quality metrics such as code coverage metrics.

With the development of modern logistics industry railway freight enterprises as the main traditional logistics enterprises, the service mode is facing many problems. In the era of big data, for railway freight enterprises, coordinated development and sharing of information resources have become the requirements of the times, while how to protect the privacy of citizens has become one of the focus issues of the public. To prevent the disclosure or abuse of the citizens' privacy information, the citizens' privacy needs to be preserved in the process of information opening and sharing. However, most of the existing privacy preserving models cannot to be used to resist attacks with continuously growing background knowledge. This paper presents the method of applying differential privacy to protect associated data, which can be shared in railway freight service association information. First, the original service data need to slice by optimal shard length, then differential method and apriori algorithm is used to add Laplace noise in the Candidate sets. Thus the citizen's privacy information can be protected even if the attacker gets strong background knowledge. Last, sharing associated data to railway information resource partners. The steps and usefulness of the discussed privacy preservation method is illustrated by an example.

Identity masking methods have been developed in recent years for use in multiple applications aimed at protecting privacy. There is only limited work, however, targeted at evaluating effectiveness of methods-with only a handful of studies testing identity masking effectiveness for human perceivers. Here, we employed human participants to evaluate identity masking algorithms on video data of drivers, which contains subtle movements of the face and head. We evaluated the effectiveness of the “personalized supervised bilinear regression method for Facial Action Transfer (FAT)” de-identification algorithm. We also evaluated an edge-detection filter, as an alternate “fill-in” method when face tracking failed due to abrupt or fast head motions. Our primary goal was to develop methods for humanbased evaluation of the effectiveness of identity masking. To this end, we designed and conducted two experiments to address the effectiveness of masking in preventing recognition and in preserving action perception. 1- How effective is an identity masking algorithm?We conducted a face recognition experiment and employed Signal Detection Theory (SDT) to measure human accuracy and decision bias. The accuracy results show that both masks (FAT mask and edgedetection) are effective, but that neither completely eliminated recognition. However, the decision bias data suggest that both masks altered the participants' response strategy and made them less likely to affirm identity. 2- How effectively does the algorithm preserve actions? We conducted two experiments on facial behavior annotation. Results showed that masking had a negative effect on annotation accuracy for the majority of actions, with differences across action types. Notably, the FAT mask preserved actions better than the edge-detection mask. To our knowledge, this is the first study to evaluate a deidentification method aimed at preserving facial ac- ions employing human evaluators in a laboratory setting.

In today's world, we are surrounded by variety of computer vision applications e.g. medical imaging, bio-metrics, security, surveillance and robotics. Most of these applications require real time processing of a single image or sequence of images. This real time image/video processing requires high computational power and specialized hardware architecture and can't be achieved using general purpose CPUs. In this paper, a FPGA based generic canny edge detector is introduced. Edge detection is one of the basic steps in image processing, image analysis, image pattern recognition, and computer vision. We have implemented a re-sizable canny edge detector IP on programmable logic (PL) of PYNQ-Platform. The IP is integrated with HDMI input/output blocks and can process 1080p input video stream at 60 frames per second. As mentioned the canny edge detection IP is scalable with respect to frame size i.e. depending on the input frame size, the hardware architecture can be scaled up or down by changing the template parameters. The offloading of canny edge detection from PS to PL causes the CPU usage to drop from about 100% to 0%. Moreover, hardware based edge detector runs about 14 times faster than the software based edge detector running on Cortex-A9 ARM processor.

With the advent of QR readers and mobile phones the use of graphical codes like QR codes and data matrix code has become very popular. Despite the noise like appearance, it has the advantage of high data capacity, damage resistance and fast decoding robustness. The proposed system embeds the image chosen by the user to develop visually appealing QR codes with improved decoding robustness using BCH algorithm. The QR information bits are encoded into luminance value of the input image. The developed Picode can inspire perceptivity in multimedia applications and can ensure data security for instances like online payments. The system is implemented on Matlab and ARM cortex A8.

Now a day, need for fast accessing of data is increasing with the exponential increase in the security field. QR codes have served as a useful tool for fast and convenient sharing of data. But with increased usage of QR Codes have become vulnerable to attacks such as phishing, pharming, manipulation and exploitation. These security flaws could pose a danger to an average user. In this paper we have proposed a way, called Secured QR (SQR) to fix all these issues. In this approach we secure a QR code with the help of a key in generator side and the same key is used to get the original information at scanner side. We have used AES algorithm for this purpose. SQR approach is applicable when we want to share/use sensitive information in the organization such as sharing of profile details, exchange of payment information, business cards, generation of electronic tickets etc.

The revolution of smart devices has a significant and positive impact on the lives of many people, especially in regard to elements of healthcare. In part, this revolution is attributed to technological advances that enable individuals to wear and use medical devices to monitor their health activities, but remotely. Also, these smart, wearable medical devices assist health care providers in monitoring their patients remotely, thereby enabling physicians to respond quickly in the event of emergencies. An ancillary advantage is that health care costs will be reduced, another benefit that, when paired with prompt medical treatment, indicates significant advances in the contemporary management of health care. However, the competition among manufacturers of these medical devices creates a complexity of small and smart wearable devices such as ECG and EMG. This complexity results in other issues such as patient security, privacy, confidentiality, and identity theft. In this paper, we discuss the design and implementation of a hybrid real-time cryptography algorithm to secure lightweight wearable medical devices. The proposed system is based on an emerging innovative technology between the genomic encryptions and the deterministic chaos method to provide a quick and secure cryptography algorithm for real-time health monitoring that permits for threats to patient confidentiality to be addressed. The proposed algorithm also considers the limitations of memory and size of the wearable health devices. The experimental results and the encryption analysis indicate that the proposed algorithm provides a high level of security for the remote health monitoring system.

A technique and algorithms for early detection of the started attack and subsequent blocking of malicious traffic are proposed. The primary separation of mixed traffic into trustworthy and malicious traffic was carried out using cluster analysis. Classification of newly arrived requests was done using different classifiers with the help of received training samples and developed success criteria.