Biblio

In a collaboration with the Georgia Peanut Commission’s Education Center and museum in Georgia, USA, we developed an augmented reality app to guide visitors through the museum and offer immersive educational information about the artifacts, exhibits, and artwork displayed therein. Notably, our augmented reality system applies the First Order Motion Model for Image Animation to several portraits of individuals influential to the Georgia peanut industry to provide immersive animated narration and monologue regarding their contributions to the peanut industry. [4]

This short paper argues that current conceptions in trust formation scholarship miss the context of zero trust, a practice growing in importance in cyber security. The contribution of this paper presents a novel approach to help conceptualize and operationalize zero trust and a call for a research agenda. Further work will expand this model and explore the implications of zero trust in future digital systems.

Instead of developing single-server software system for the powerful computers, the software is turning from large single-server to multi-server system such as distributed system. This change introduces a new challenge for the software quality measurement, since the current software analysis methods for single-server software could not observe and assess the correlation among the components on different nodes. In this paper, a new dynamic cohesion approach is proposed for distributed system. We extend Calling Network model for distributed system by differentiating methods of components deployed on different nodes. Two new cohesion metrics are proposed to describe the correlation at component level, by extending the cohesion metric of single-server software system. The experiments, conducted on a distributed systems-Netflix RSS Reader, present how to trace the various system functions accomplished on three nodes, how to abstract dynamic behaviors using our model among different nodes and how to evaluate the software cohesion on distributed system.

Industrial Control Systems (ICS) are complex systems made up of many components with different tasks. For a safe and secure operation, each device needs to carry out its tasks correctly. To monitor a system and ensure the correct behavior of systems, anomaly detection is used.Models of expected behavior often rely only on cyber or physical features for anomaly detection. We propose an anomaly detection system that combines both types of features to create a dynamic fingerprint of an ICS. We present how a cyber-physical anomaly detection using sound on the physical layer can be designed, and which challenges need to be overcome for a successful implementation. We perform an initial evaluation for identifying actions of a 3D printer.

Since the number of cyber attacks by insider threats and the damage caused by them has been increasing over the last years, organizations are in need for specific security solutions to counter these threats. To limit the damage caused by insider threats, the timely detection of erratic system behavior and malicious activities is of primary importance. We observed a major paradigm shift towards anomaly-focused detection mechanisms, which try to establish a baseline of system behavior – based on system logging data – and report any deviations from this baseline. While these approaches are promising, they usually have to cope with scalability issues. As the amount of log data generated during IT operations is exponentially growing, high-performance security solutions are required that can handle this huge amount of data in real time. In this paper, we demonstrate how high-performance bioinformatics tools can be leveraged to tackle this issue, and we demonstrate their application to log data for outlier detection, to timely detect anomalous system behavior that points to insider attacks.

The increase of size, capabilities, and speed of FPGAs enables the shared usage of reconfigurable resources by multiple applications and even operating systems. While research on FPGA virtualization in HPC-datacenters and cloud is already well advanced, it is a rather new concept for embedded systems. The necessity for FPGA virtualization of embedded systems results from the trend to integrate multiple environments into the same hardware platform. As multiple guest operating systems with different requirements, e.g., regarding real-time, security, safety, or reliability share the same resources, the focus of research lies on isolation under the constraint of having minimal impact on the overall system. Drivers for this development are, e.g., computation intensive AI-based applications in the automotive or medical field, embedded 5G edge computing systems, or the consolidation of electronic control units (ECUs) on a centralized MPSoC with the goal to increase reliability by reducing complexity. This survey outlines key concepts of hypervisor-based virtualization of embedded reconfigurable systems. Hypervisor approaches are compared and classified into FPGA-based hypervisors, MPSoC-based hypervisors and hypervisors for distributed embedded reconfigurable systems. Strong points and limitations are pointed out and future trends for virtualization of embedded reconfigurable systems are identified.

In order to improve the efficiency of the existing homomorphic encryption method, based on the DGHV scheme, an improved fully homomorphic scheme over the integer is proposed. Under the premise of ensuring data owner and user data security, the scheme supports the addition and multiplication operations of ciphertext, and ensures faster execution efficiency and meets the security requirements of cloud computing. Security analysis shows that our scheme is safe. Performance assessment demonstrates that our scheme can more efficiently implement data than DGHV scheme.

A “tripartite and bilateral” dynamic game model was constructed to study the impact of space deterrence on the challenger's military strategy in a military conflict. Based on the signal game theory, the payment matrices and optimal strategies of the sheltering side and challenging side were analyzed. In a theoretical framework, the indicators of the effectiveness of the challenger's response to space deterrence and the influencing factors of the sheltering's space deterrence were examined. The feasibility and effective means for the challenger to respond to the space deterrent in a “tripartite and bilateral” military conflict were concluded.

Traditional DDoS attacks mainly send massive data packets through the attacking machine, consuming the network resources or server resources of the target server, making users unable to use server resources to achieve the purpose of denial of service. This type of attack is called a Flooding-based DDoS (FDDoS) attack. It has the characteristics of large traffic and suddenness. However, Low-rate DDoS (LDDoS) attack is a new type of DDoS attack. LDDoS utilize the TCP congestion control mechanism and sends periodic pulses to attack, which can seriously reduce the TCP flow throughput of the attacked link. It has the characteristics of small traffic and strong concealment. Each of these two DDoS attack methods has its own hard-to-handle characteristics, so that there is currently no particularly effective method to prevent such attacks. This paper uses time-frequency analysis to classify and filter DDoS traffic. The proposed filtering method is designed as a system in the actual environment. Experimental results show that the designed filtering algorithm can resist not only FDDoS attacks, but also LDDoS attacks.

For future Internet, information-centric networking (ICN) is considered a potential solution to many of its current problems, such as content distribution, mobility, and security. Named Data Networking (NDN) is a more popular ICN project. However, concern regarding the protection of user data persists. Information caching in NDN decouples content and content publishers, which leads to content security threats due to lack of secure controls. Therefore, this paper presents a CP-ABE (ciphertext policy attribute based encryption) access control scheme based on hash table and data segmentation (CHTDS). Based on data segmentation, CHTDS uses a method of linearly splitting fixed data blocks, which effectively improves data management. CHTDS also introduces CP-ABE mechanism and hash table data structure to ensure secure access control and privilege revocation does not need to re-encrypt the published content. The analysis results show that CHTDS can effectively realize the security and fine-grained access control in the NDN environment, and reduce communication overhead for content access.

Generalized chaos systems have more complex dynamic behavior than conventional chaos systems. If a generalized response system can be synchronized with a conventional drive system, the flexible control parameters and unpredictable synchronization state will increase significantly. The study first constructs a four-dimensional nonlinear dynamic equation with quadratic variables as a drive system. The numerical simulation and analyses of the Lyapunov exponent show that it is also a chaotic system. Based on the generalized chaos synchronization (GCS) theory, a four-dimensional diffeomorphism function is designed, and the corresponding GCS response system is generated. Simultaneously, the structural and synchronous circuits of information interaction and control are constructed with Multisim™ software, with the circuit simulation resulting in a good agreement with the numerical calculations. In order to verify the practical effect of generalized synchronization, an RGB digital image secure communication scheme is proposed. We confuse a 24-bit true color image with the designed GCS system, extend the original image to 48-bits, analyze the scheme security from keyspace, key sensitivity and non-symmetric identity authentication, classical types of attacks, and statistical average from the histogram, image correlation. The research results show that this GCS system is simple and feasible, and the encryption algorithm is closely related to the confidential information, which can resist the differential attack. The scheme is suitable to be applied in network images or other multimedia safe communications.

Signed social networks have become increasingly important in recent years because of the ability to model trust-based relationships in review sites like Slashdot, Epinions, and Wikipedia. As a result, many traditional network mining problems have been re-visited in the context of networks in which signs are associated with the links. Examples of such problems include community detection, link prediction, and low rank approximation. In this paper, we will examine the problem of ranking nodes in signed networks. In particular, we will design a ranking model, which has a clear physical interpretation in terms of the sign of the edges in the network. Specifically, we propose the Troll-Trust model that models the probability of trustworthiness of individual data sources as an interpretation for the underlying ranking values. We will show the advantages of this approach over a variety of baselines.

The confidentiality of tenant's data is confronted with high risk when facing hardware attacks and privileged malicious software. Hardware-based memory encryption is one of the promising means to provide strong guarantees of data security. Recently AMD has proposed its new memory encryption hardware called SME and SEV, which can selectively encrypt memory regions in a fine-grained manner, e.g., by setting the C-bits in the page table entries. More importantly, SEV further supports encrypted virtual machines. This, intuitively, has provided a new opportunity to protect data confidentiality in guest VMs against an untrusted hypervisor in the cloud environment. In this paper, we first provide a security analysis on the (in)security of SEV and uncover a set of security issues of using SEV as a means to defend against an untrusted hypervisor. Based on the study, we then propose a software-based extension to the SEV feature, namely Fidelius, to address those issues while retaining performance efficiency. Fidelius separates the management of critical resources from service provisioning and revokes the permissions of accessing specific resources from the un-trusted hypervisor. By adopting a sibling-based protection mechanism with non-bypassable memory isolation, Fidelius embraces both security and efficiency, as it introduces no new layer of abstraction. Meanwhile, Fidelius reuses the SEV API to provide a full VM life-cycle protection, including two sets of para-virtualized I/O interfaces to encode the I/O data, which is not considered in the SEV hardware design. A detailed and quantitative security analysis shows its effectiveness in protecting tenant's data from a variety of attack surfaces, and the performance evaluation confirms the performance efficiency of Fidelius.

The proliferation of real-time cyber-physical systems (CPS) is making profound changes to our daily life. Many real-time CPSs are security and safety-critical because of their continuous interactions with the physical world. While the general perception is that the security protection mechanism deployment is often absent in real-time embedded systems, there is no existing empirical study that measures the adoption of these mechanisms in the ecosystem. To bridge this gap, we conduct a measurement study for real-time embedded firmware from both a security perspective and a real-time perspective. To begin with, we collected more than 16 terabytes of embedded firmware and sampled 1,000 of them for the study. Then, we analyzed the adoption of security protection mechanisms and their potential impacts on the timeliness of real-time embedded systems. Besides, we measured the scheduling algorithms supported by real-time embedded systems since they are also security-critical.

Since deep learning (DL) can automatically learn features from source code, it has been widely used to detect source code vulnerability. To achieve scalable vulnerability scanning, some prior studies intend to process the source code directly by treating them as text. To achieve accurate vulnerability detection, other approaches consider distilling the program semantics into graph representations and using them to detect vulnerability. In practice, text-based techniques are scalable but not accurate due to the lack of program semantics. Graph-based methods are accurate but not scalable since graph analysis is typically time-consuming. In this paper, we aim to achieve both scalability and accuracy on scanning large-scale source code vulnerabilities. Inspired by existing DL-based image classification which has the ability to analyze millions of images accurately, we prefer to use these techniques to accomplish our purpose. Specifically, we propose a novel idea that can efficiently convert the source code of a function into an image while preserving the program details. We implement Vul-CNN and evaluate it on a dataset of 13,687 vulnerable functions and 26,970 non-vulnerable functions. Experimental results report that VulCNN can achieve better accuracy than eight state-of-the-art vul-nerability detectors (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, VulDeePecker, SySeVR, VulDeeLocator, and Devign). As for scalability, VulCNN is about four times faster than VulDeePecker and SySeVR, about 15 times faster than VulDeeLocator, and about six times faster than Devign. Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN can detect large-scale vulnerability. Through the scanning reports, we finally discover 73 vulnerabilities that are not reported in NVD.

In order to improve the concealment of image steganography, a new method is proposed. The algorithm firstly adopted GM (1, 1) model to detect texture and edge points of carrier image, then embedded secret information in them. GM (1, 1) model of optimized parameters can make full use of pixels information. These pixels are the nearest to the detected point, so it improves the detection accuracy. The method is a kind of steganography based on human visual system. By testing the stegano images with different embedding capacities, the result indicates concealment and image quality of the proposed algorithm are better than BPCS (Bit-plane Complexity Segmentation) and PVD (Pixel-value Differencing), which are also based on visual characteristics.

The face is the most dominant and distinct communication tool of human beings. Automatic analysis of facial behavior allows machines to understand and interpret a human's states and needs for natural interactions. This research focuses on developing advanced computer vision techniques to process and analyze facial images for the recognition of various facial behaviors. Specifically, this research consists of two parts: automatic facial landmark detection and tracking, and facial behavior analysis and recognition using the tracked facial landmark points. In the first part, we develop several facial landmark detection and tracking algorithms on facial images with varying conditions, such as varying facial expressions, head poses and facial occlusions. First, to handle facial expression and head pose variations, we introduce a hierarchical probabilistic face shape model and a discriminative deep face shape model to capture the spatial relationships among facial landmark points under different facial expressions and face poses to improve facial landmark detection. Second, to handle facial occlusion, we improve upon the effective cascade regression framework and propose the robust cascade regression framework for facial landmark detection, which iteratively predicts the landmark visibility probabilities and landmark locations. The second part of this research applies our facial landmark detection and tracking algorithms to facial behavior analysis, including facial action recognition and face pose estimation. For facial action recognition, we introduce a novel regression framework for joint facial landmark detection and facial action recognition. For head pose estimation, we are working on a robust algorithm that can perform head pose estimation under facial occlusion.

Artificial Intelligence (AI) technology is developing rapidly, permeating every aspect of human life. Although the integration between AI and communication contributes to the flourishing development of wireless communication, it induces severer security problems. As a supplement to the upper-layer cryptography protocol, physical layer security has become an intriguing technology to ensure the security of wireless communication systems. However, most of the current physical layer security research does not consider the intelligence and mobility of collusive eavesdroppers. In this paper, we consider a MIMO system model with a friendly intelligent jammer against multiple collusive intelligent eavesdroppers, and zero-sum game is exploited to formulate the confrontation of them. The Nash equilibrium is derived by convex optimization and alternative optimization in the free-space scenario of a single user system. We propose a zero-sum game deep learning algorithm (ZGDL) for general situations to solve non-convex game problems. In terms of the effectiveness, simulations are conducted to confirm that the proposed algorithm can obtain the Nash equilibrium.

Main-memory database management systems (DBMS) can achieve excellent performance when processing massive volume of on-line transactions on modern multi-core machines. But existing durability schemes, namely, tuple-level and transaction-level logging-and-recovery mechanisms, either degrade the performance of transaction processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it is possible to achieve speedy failure recovery without introducing any costly logging overhead to the execution of concurrent transactions. We propose PACMAN, a parallel database recovery mechanism that is specifically designed for lightweight, coarse-grained transaction-level logging. PACMAN leverages a combination of static and dynamic analyses to parallelize the log recovery: at compile time, PACMAN decomposes stored procedures by carefully analyzing dependencies within and across programs; at recovery time, PACMAN exploits the availability of the runtime parameter values to attain an execution schedule with a high degree of parallelism. As such, recovery performance is remarkably increased. We evaluated PACMAN in a fully-fledged main-memory DBMS running on a 40-core machine. Compared to several state-of-the-art database recovery mechanisms, can significantly reduce recovery time without compromising the efficiency of transaction processing.

The fog platform is very suitable for time and location sensitive applications. Compared with cloud computing, fog computing faces new security and privacy challenges. This paper integrates blockchain nodes with fog nodes, and uses multi-party secure computing (MPC) in smart contracts to realize privacy-protected fog computing. MPC technology realizes encrypted input and output, so that participants can only get the output value of their own function. It is impossible to know the input and output of other people, and privacy calculation is realized. At the same time, the blockchain can perform network-wide verification and consensus on the results calculated by the MPC under the chain. Ensure the reliability of the calculation results. Due to the integration of blockchain and fog nodes, access control and encryption are guaranteed, integrity and isolation are provided, and privacy-sensitive data is controlled. As more complex topological structures emerge, the entire chain of fog nodes must be trusted. This ensures the network security of distributed data storage and network topology, users and fog service providers. Finally, trusted fog computing with privacy protection is realized.

Massive amounts of videos are generated for entertainment, security, and science, powered by a growing supply of user-produced video hosting services. Unfortunately, searching for videos is difficult due to the lack of content annotations. Recent breakthroughs in image labeling with deep neural networks (DNNs) create a unique opportunity to address this problem. While many automated end-to-end solutions have been developed, such as natural language queries, we take on a different perspective: to leverage both the development of algorithms and human capabilities. To this end, we design a query language in tandem with a user interface to help users quickly identify segments of interest from the video based on labels and corresponding bounding boxes. We combine techniques from the database and information visualization communities to help the user make sense of the object labels in spite of errors and inconsistencies.

Threat intelligence sharing is posited as an important aid to help counter cybersecurity attacks and a number of threat intelligence sharing communities exist. There is a general consensus that many challenges remain to be overcome to achieve fully effective sharing, including concerns about privacy, negative publicity, policy/legal issues and expense of sharing, amongst others. One recent trend undertaken to address this is the use of decentralized blockchain based sharing architectures. However while these platforms can help increase sharing effectiveness they do not fully address all of the above challenges. In particular, issues around trust are not satisfactorily solved by current approaches. In this paper, we describe a novel trust enhancement framework -TITAN- for decentralized sharing based on the use of P2P reputation systems to address open trust issues. Our design uses blockchain and Trusted Execution Environment technologies to ensure security, integrity and privacy in the operation of the threat intelligence sharing reputation system.

As automatic speech recognition (ASR) systems have been integrated into a diverse set of devices around us in recent years, security vulnerabilities of them have become an increasing concern for the public. Existing studies have demonstrated that deep neural networks (DNNs), acting as the computation core of ASR systems, is vulnerable to deliberately designed adversarial attacks. Based on the gradient descent algorithm, existing studies have successfully generated adversarial samples which can disturb ASR systems and produce adversary-expected transcript texts designed by adversaries. Most of these research simulated white-box attacks which require knowledge of all the components in the targeted ASR systems. In this work, we propose the first semi-black-box attack against the ASR system - Kaldi. Requiring only partial information from Kaldi and none from DNN, we can embed malicious commands into a single audio chip based on the gradient-independent genetic algorithm. The crafted audio clip could be recognized as the embedded malicious commands by Kaldi and unnoticeable to humans in the meanwhile. Experiments show that our attack can achieve high attack success rate with unnoticeable perturbations to three types of audio clips (pop music, pure music, and human command) without the need of the underlying DNN model parameters and architecture.