Biblio

Anonymous communications are important for many of the applications of mobile ad hoc networks (MANETs) deployed in adversary environments. A major requirement on the network is the ability to provide unidentifiability and unlinkability for mobile nodes and their traffic. Although a number of anonymous secure routing protocols have been proposed, the requirement is not fully satisfied. The existing protocols are vulnerable to the attacks of fake routing packets or denial-of-service broadcasting, even the node identities are protected by pseudonyms. In this paper, we propose a new routing protocol, i.e., authenticated anonymous secure routing (AASR), to satisfy the requirement and defend against the attacks. More specifically, the route request packets are authenticated by a group signature, to defend against potential active attacks without unveiling the node identities. The key-encrypted onion routing with a route secret verification message is designed to prevent intermediate nodes from inferring a real destination. Simulation results have demonstrated the effectiveness of the proposed AASR protocol with improved performance as compared with the existing protocols.

Attribute-Based Access Control (ABAC) has received significant attention in recent years, although the concept has been around for over two decades now. Many ABAC models, with different variations, have been proposed and formalized. Besides basic ABAC models, there are models designed with additional capabilities such as group attributes, group and attribute hierarchies and so on. Hierarchical relationship among groups and attributes enhances access control flexibility and facilitates attribute management and administration. However, implementation and demonstration of ABAC models in real-world applications is still lacking. In this paper, we present a restricted HGABAC (rHGABAC) model with user and object groups and group hierarchy. We then introduce attribute hierarchies in this model. We also present an authorization architecture for implementing rHGABAC utilizing the NIST Policy Machine (PM). PM allows to define attribute-based access control policies, however, the attributes in PM are different in nature than attributes in typical ABAC models as name-value pairs. We identify a policy configuration mechanism for our proposed model employing PM capabilities, and demonstrate use cases and their configuration and implementation in PM using our authorization architecture.

In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.

Abnormal crowd behavior detection is an important research issue in video processing and computer vision. In this paper we introduce a novel method to detect abnormal crowd behaviors in video surveillance based on interest points. A complex network-based algorithm is used to detect interest points and extract the global texture features in scenarios. The performance of the proposed method is evaluated on publicly available datasets. We present a detailed analysis of the characteristics of the crowd behavior in different density crowd scenes. The analysis of crowd behavior features and simulation results are also demonstrated to illustrate the effectiveness of our proposed method.

CFRS (Collaborative Filtering Recommendation System) is one of the most widely used individualized recommendation systems. However, CFRS is susceptible to shilling attacks based on profile injection. The current research on shilling attack mainly focuses on the recognition of false user profiles, but these methods depend on the specific attack models and the computational cost is huge. From the view of item, some abnormal item detection methods are proposed which are independent of attack models and overcome the defects of user profiles model, but its detection rate, false alarm rate and time overhead need to be further improved. In order to solve these problems, it proposes an abnormal item detection method based on time window merging. This method first uses the small window to partition rating time series, and determine whether the window is suspicious in terms of the number of abnormal ratings within it. Then, the suspicious small windows are merged to form suspicious intervals. We use the rating distribution characteristics RAR (Ratio of Abnormal Rating), ATIAR (Average Time Interval of Abnormal Rating), DAR(Deviation of Abnormal Rating) and DTIAR (Deviation of Time Interval of Abnormal Rating) in the suspicious intervals to determine whether the item is subject to attacks. Experiment results on the MovieLens 100K data set show that the method has a high detection rate and a low false alarm rate.

The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.

In this work, an approach for the automatic analysis of people trajectories is presented, using a multi-camera and card reader system. Data is first extracted from surveillance cameras and card readers to create trajectories which are sequences of paths and activities. A distance model is proposed to compare sequences and calculate similarities. The popular unsupervised model One-Class Support Vector Machine (One-Class SVM) is used to train a detector. The proposed method classifies trajectories as normal or abnormal and can be used in two modes: off-line and real-time. Experiments are based on data simulation corresponding to an attack scenario proposed by a security expert. Results show that the proposed method successfully detects the abnormal sequences in the scenario with very low false alarm rate.

Abnormality detection is useful in reducing the amount of data to be processed manually by directing attention to the specific portion of data. However, selections of suitable features are important for the success of an abnormality detection system. Designing and selecting appropriate features are time-consuming, requires expensive domain knowledge and human labor. Further, it is very challenging to represent high-level concepts of abnormality in terms of raw input. Most of the existing abnormality detection system use handcrafted feature detector and are based on shallow architecture. In this work, we explore Deep Belief Network for abnormality detection and simultaneously, compared the performance of classic neural network in terms of features learned and accuracy of detecting the abnormality. Further, we explore the set of features learn by each layer of the deep architecture. We also provide a simple and fast mechanism to visualize the feature at the higher layer. Further, the effect of different activation function on abnormality detection is also compared. We observed that deep learning based approach can be used for detecting an abnormality. It has better performance compare to classical neural network in separating distinct as well as almost similar data.

Platform as a Service (PaaS) provides middleware resources to cloud customers. As demand for PaaS services increases, so do concerns about the security of PaaS. This paper discusses principal PaaS security and integrity requirements, and vulnerabilities and the corresponding countermeasures. We consider three core cloud elements: multi-tenancy, isolation, and virtualization and how they relate to PaaS services and security trends and concerns such as user and resource isolation, side-channel vulnerabilities in multi-tenant environments, and protection of sensitive data

This paper deals with the comparison of the most popular methods of a logical analysis of natural language Montague intensional logic and Transparent intensional logic. At first, these logical apparatuses are compared in terms of their founding theoretical principles. Later, the selected sentence is examined through the logical analysis. The aim of the paper is to identify a more expressive logical method, which will be a suitable basis for the future design of an algorithm for the automated translation of the natural language into a formal representation of its meaning through a semantic machine.

We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as a heuristic for detecting abrupt changes in the system outputs. We provide sufficient detectability conditions for the proposed heuristic. As an application, we consider detecting malicious cyber data attacks on power systems and test our proposed heuristic on the IEEE 39-bus testbed.
 

In today's world, it's almost impossible to find a sphere of human life in which information technologies would not be used. On the one hand, it simplifies human life - virtually everyone carries a mini-computer in his pocket and it allows to perform many operations, that took a lot of time, in minutes. In addition, IT has simplified and promptly developed areas such as medicine, banking, document circulation, military, and many other infrastructures of the state. Nevertheless, even today, privacy remains a major problem in many information transactions. One of the most important directions for ensuring the information confidentiality in open communication networks has been and remains its protection by cryptographic methods. Although it is known that traditional cryptography methods give reasons to doubt in their reliability, quantum cryptography has proven itself as a more reliable information security technology. As far is it quite new direction there is no sufficiently complete classification of attacks on quantum cryptography methods, in view of this new extended classification of attacks on quantum protocols and quantum cryptosystems is proposed in this work. Classification takes into account the newest attacks (which use devices loopholes) on quantum key distribution equipment. These attacks have been named \textbackslashtextless; \textbackslashtextless; quantum hacking\textbackslashtextgreater\textbackslashtextgreater. Such classification may be useful for choosing commercially available quantum key distribution system. Also abstract model of eavesdropper in quantum systems was created and it allows to determine a set of various nature measures that need to be further implemented to provide reliable security with the help of specific quantum systems.

We propose an interactive approach where analysts reason about the security of a system using an abstraction of its runtime structure, as opposed to looking at the code. They interactively refine a hierarchical object graph, set security properties on abstract objects or edges, query the graph, and investigate the results by studying highlighted objects or edges or tracing to the code. Behind the scenes, an inference analysis and an extraction analysis maintain the soundness of the graph with respect to the code.

The growing complexity and diversification of cyber-attacks are largely reflected in the increasing sophistication of security appliances, which are often too cumbersome to be run in virtual services and IoT devices. Hence, the design of cyber-security frameworks is today looking at more cooperative models, which collect security-related data from a large set of heterogeneous sources for centralized analysis and correlation.In this paper, we outline a flexible abstraction layer for access to security context. It is conceived to program and gather data from lightweight inspection and enforcement hooks deployed in cloud applications and IoT devices. We also provide a preliminary description of its implementation, by reviewing the main software components and their role.

As the number of devices that gain connectivity and join the category of smart-objects increases every year reaching unprecedented numbers, new challenges are imposed on our networks. While specialized solutions for certain use cases have been proposed, more flexible and scalable new approaches to networking will be required to deal with billions or trillions of smart objects connected to the Internet. With this paper, we take a step back looking at the set of basic problems that are posed by this group of devices. In order to develop an analysis on how these issues could be approached, we define which fundamental abstractions might help solving or at least reducing their impact on the network by offering support for fundamental matters such as mobility, group based delivery and support for distributed computing resources. Based on the concept of named-objects, we propose a set of solutions that network and show how this approach can address both scalability and functional requirements. Finally, we describe a comprehensive clean-slate network architecture (MobiityFirst) which attempts to realize the proposed capabilities.

When a user accesses a resource, the accounting process at the server side does the job of keeping track of the resource usage so as to charge the user. In cloud computing, a user may use more than one service provider and need two independent service providers to work together. In this user-centric context, the user is the owner of the information and has the right to authorize to a third party application to access the protected resource on the user's behalf. Therefore, the user also needs to monitor the authorized resource usage he granted to third party applications. However, the existing accounting protocols were proposed to monitor the resource usage in terms of how the user uses the resource from the service provider. This paper proposed the user-centric accounting model called AccAuth which designs an accounting layer to an OAuth protocol. Then the prototype was implemented, and the proposed model was evaluated against the standard requirements. The result showed that AccAuth passed all the requirements.
 

A fundamental recurring task in many machine learning applications is the search for the Nearest Neighbor in high dimensional metric spaces. Towards answering queries in large scale problems, state-of-the-art methods employ Approximate Nearest Neighbors (ANN) search, a search that returns the nearest neighbor with high probability, as well as techniques that compress the dataset. Product-Quantization (PQ) based ANN search methods have demonstrated state-of-the-art performance in several problems, including classification, regression and information retrieval. The dataset is encoded into a Cartesian product of multiple low-dimensional codebooks, enabling faster search and higher compression. Being intrinsically parallel, PQ-based ANN search approaches are amendable for hardware acceleration. This paper proposes a novel Hierarchical PQ (HPQ) based ANN search method as well as an FPGA-tailored architecture for its implementation that outperforms current state of the art systems. HPQ gradually refines the search space, reducing the number of data compares and enabling a pipelined search. The mapping of the architecture on a Stratix 10 FPGA device demonstrates over ×250 speedups over current state-of-the-art systems, opening the space for addressing larger datasets and/or improving the query times of current systems.

DDoS attacks are a significant threat to internet service or infrastructure providers. This poster presents an FPGA-accelerated device and DDoS mitigation technique to overcome such attacks. Our work addresses amplification attacks whose goal is to generate enough traffic to saturate the victims links. The main idea of the device is to efficiently filter malicious traffic at high-speeds directly in the backbone infrastructure before it even reaches the victim's network. We implemented our solution for two FPGA platforms using the high-level description in P4, and we report on its performance in terms of throughput and hardware resources.

The objective of this paper is to outline the design specification, implementation and evaluation of a proposed accelerated encryption framework which deploys both homomorphic and symmetric-key encryptions to serve the privacy preserving processing; in particular, as a sub-system within the Privacy Preserving Speech Processing framework architecture as part of the PPSP-in-Cloud Platform. Following a preliminary study of GPU efficiency gains optimisations benchmarked for AES implementation we have addressed and resolved the Big Integer processing challenges in parallel implementation of bilinear pairing thus enabling the creation of partially homomorphic encryption schemes which facilitates applications such as speech processing in the encrypted domain on the cloud. This novel implementation has been validated in laboratory tests using a standard speech corpus and can be used for other application domains to support secure computation and privacy preserving big data storage/processing in the cloud.

Support vector machines (SVMs) have been widely used for classification in machine learning and data mining. However, SVM faces a huge challenge in large scale classification tasks. Recent progresses have enabled additive kernel version of SVM efficiently solves such large scale problems nearly as fast as a linear classifier. This paper proposes a new accelerated mini-batch stochastic gradient descent algorithm for SVM classification with additive kernel (AK-ASGD). On the one hand, the gradient is approximated by the sum of a scalar polynomial function for each feature dimension; on the other hand, Nesterov's acceleration strategy is used. The experimental results on benchmark large scale classification data sets show that our proposed algorithm can achieve higher testing accuracies and has faster convergence rate.

Despite significant recent advances, the effectiveness of symbolic execution is limited when used to test complex, real-world software. One of the main scalability challenges is related to constraint solving: large applications and long exploration paths lead to complex constraints, often involving big arrays indexed by symbolic expressions. In this paper, we propose a set of semantics-preserving transformations for array operations that take advantage of contextual information collected during symbolic execution. Our transformations lead to simpler encodings and hence better performance in constraint solving. The results we obtain are encouraging: we show, through an extensive experimental analysis, that our transformations help to significantly improve the performance of symbolic execution in the presence of arrays. We also show that our transformations enable the analysis of new code, which would be otherwise out of reach for symbolic execution.

The analysis of security-related event logs is an important step for the investigation of cyber-attacks. It allows tracing malicious activities and lets a security operator find out what has happened. However, since IT landscapes are growing in size and diversity, the amount of events and their highly different representations are becoming a Big Data challenge. Unfortunately, current solutions for the analysis of security-related events, so called Security Information and Event Management (SIEM) systems, are not able to keep up with the load. In this work, we propose a distributed SIEM platform that makes use of highly efficient distributed normalization and persists event data into an in-memory database. We implement the normalization on common distribution frameworks, i.e. Spark, Storm, Trident and Heron, and compare their performance with our custom-built distribution solution. Additionally, different tuning options are introduced and their speed advantage is presented. In the end, we show how the writing into an in-memory database can be tuned to achieve optimal persistence speed. Using the proposed approach, we are able to not only fully normalize, but also persist more than 20 billion events per day with relatively small client hardware. Therefore, we are confident that our approach can handle the load of events in even very large IT landscapes.

Community detection is an advanced graph operation that is used to reveal tightly-knit groups of vertices (aka. communities) in real-world networks. Given the intractability of the problem, efficient heuristics are used in practice. Yet, even the best of these state-of-the-art heuristics can become computationally demanding over large inputs and can generate workloads that exhibit inherent irregularity in data movement on manycore platforms. In this paper, we posit that effective acceleration of the graph community detection operation can be achieved by reducing the cost of data movement through a combined innovation at both software and hardware levels. More specifically, we first propose an efficient software-level parallelization of community detection that uses approximate updates to cleverly exploit a diminishing returns property of the algorithm. Secondly, as a way to augment this innovation at the software layer, we design an efficient Wireless Network on Chip (WiNoC) architecture that is suited to handle the irregular on-chip data movements exhibited by the community detection algorithm under both unicast- and broadcast-heavy cache coherence protocols. Experimental results show that our resulting WiNoC-enabled manycore platform achieves on average 52% savings in execution time, without compromising on the quality of the outputs, when compared to a traditional manycore platform designed with a wireline mesh NoC and running community detection without employing approximate updates.

In recent years, FPGAs have been deployed in data centres of major cloud service providers, such as Microsoft [1], Amazon [2], Alibaba [3], Tencent [4], Huawei [5], and Nimbix [6]. This marks the beginning of bringing FPGA computing to the masses, as being in the cloud, one can access an FPGA from anywhere. A wide range of applications are run in the cloud, including web servers and databases among many others. Memcached is a high-performance in-memory ob ject caching system, which acts as a caching layer between web servers and databases. It is used by many companies, including Flicker, Wikipedia, Wordpress, and Facebook [7, 8]. In this paper, we present a Memcached accelerator implemented on the AWS FPGA cloud (F1 instance). Compared to AWS ElastiCache, an AWS-managed CPU Memcached service, our Memcached accelerator provides up to 9 x better throughput and latency. A live demo of the Memcached accelerator running on F1 can be accessed on our website [9].