Biblio

Conditional Generative Adversarial Nets [1](cGAN) was recently proposed as a novel conditional learning method by feeding some extra information into the network. In this paper we propose an improved conditional GANs which use divided z-vector (DzGAN). The computation amount will be reduced because DzGAN can implement conditional learning using not images but one-hot vector by dividing the range of z-vector (e.g. -1\textasciitilde1 to -1\textasciitilde0 and 0\textasciitilde1). In the DzGAN, the discriminator is fed by the images with label using one-hot vector and the generator is fed by divided z-vector (e.g. there are 10 classes In MNIST dataset, the divided z-vector will be z1\textasciitildez10 accordingly) with corresponding label fed into the discriminator, thus we can implement conditional learning. In this paper we use conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) [7] instead of cGAN because cDCGAN can generate clear image better than cGAN. Heuristic experiments of conditional learning which compare the computation amount demonstrate that DzGAN is superior than cDCGAN.

Trust in autonomous teammates has been shown to be a key factor in human-autonomy team (HAT) performance, and anthropomorphism is a closely related construct that is underexplored in HAT literature. This study investigates whether perceived anthropomorphism can be measured from team communication behaviors in a simulated remotely piloted aircraft system task environment, in which two humans in unique roles were asked to team with a synthetic (i.e., autonomous) pilot agent. We compared verbal and self-reported measures of anthropomorphism with team error handling performance and trust in the synthetic pilot. Results for this study show that trends in verbal anthropomorphism follow the same patterns expected from self-reported measures of anthropomorphism, with respect to fluctuations in trust resulting from autonomy failures.

In 2007, Shacham published a seminal paper on Return-Oriented Programming (ROP), the first systematic formulation of code reuse. The paper has been highly influential, profoundly shaping the way we still think about code reuse today: an attacker analyzes the "geometry" of victim binary code to locate gadgets and chains these to craft an exploit. This model has spurred much research, with a rapid progression of increasingly sophisticated code reuse attacks and defenses over time. After ten years, the common perception is that state-of-the-art code reuse defenses are effective in significantly raising the bar and making attacks exceedingly hard. In this paper, we challenge this perception and show that an attacker going beyond "geometry" (static analysis) and considering the "dynamics" (dynamic analysis) of a victim program can easily find function call gadgets even in the presence of state-of-the-art code-reuse defenses. To support our claims, we present Newton, a run-time gadget-discovery framework based on constraint-driven dynamic taint analysis. Newton can model a broad range of defenses by mapping their properties into simple, stackable, reusable constraints, and automatically generate gadgets that comply with these constraints. Using Newton, we systematically map and compare state-of-the-art defenses, demonstrating that even simple interactions with popular server programs are adequate for finding gadgets for all state-of-the-art code-reuse defenses. We conclude with an nginx case study, which shows that a Newton-enabled attacker can craft attacks which comply with the restrictions of advanced defenses, such as CPI and context-sensitive CFI.

Scan design is a universal design for test (DFT) technology to increase the observability and controllability of the circuits under test by using scan chains. However, it also leads to a potential security problem that attackers can use scan design as a backdoor to extract confidential information. Researchers have tried to address this problem by using secure scan structures that usually have some keys to confirm the identities of users. However, the traditional methods to store intermediate data or keys in memory are also under high risk of being attacked. In this paper, we propose a dynamic-key secure DFT structure that can defend scan-based and memory attacks without decreasing the system performance and the testability. The main idea is to build a scan design key generator that can generate the keys dynamically instead of storing and using keys in the circuit statically. Only specific patterns derived from the original test patterns are valid to construct the keys and hence the attackers cannot shift in any other patterns to extract correct internal response from the scan chains or retrieve the keys from memory. Analysis results show that the proposed method can achieve a very high security level and the security level will not decrease no matter how many guess rounds the attackers have tried due to the dynamic nature of our method.

In interconnected power systems, dynamic model reduction can be applied to generators outside the area of interest (i.e., study area) to reduce the computational cost associated with transient stability studies. This paper presents a method of deriving the reduced dynamic model of the external area based on dynamic response measurements. The method consists of three steps, namely dynamic-feature extraction, attribution, and reconstruction (DEAR). In this method, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal “basis” of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original system. The network model is unchanged in the DEAR method. Tests on several IEEE standard systems show that the proposed method yields better reduction ratio and response errors than the traditional coherency based reduction methods.
 

Serving heterogeneous traffic demand requires efficient resource utilization to deliver the promises of 5G wireless network towards enhanced mobile broadband, massive machine type communication and ultra-reliable low-latency communication. In this paper, an integrated user application-specific demand characteristics as well as network characteristics evaluation based online slice allocation model for 5G wireless network is proposed. Such characteristics include, available bandwidth, power, quality of service demand, service priority, security sensitivity, network load, predictive load etc. A degree of intra-slice resource sharing elasticity has been considered based on their availability. The availability has been assessed based on the current availability as well as forecasted availability. On the basis of application characteristics, an admission control strategy has been proposed. An interactive AMF (Access and Mobility Function)- RAN (Radio Access Network) information exchange has been assumed. A cost function has been derived to quantify resource allocation decision metric that is valid for both static and dynamic nature of user and network characteristics. A dynamic intra-slice decision boundary estimation model has been proposed. A set of analytical comparative results have been attained in comparison to the results available in the literature. The results suggest the proposed resource allocation framework performance is superior to the existing results in the context of network utility, mean delay and network grade of service, while providing similar throughput. The superiority reported is due to soft nature of the decision metric while reconfiguring slice resource block-size and boundaries.

Scan-based test is commonly used to increase testability and fault coverage, however, it is also known to be a liability for chip security. Research has shown that intellectual property (IP) or secret keys can be leaked through scan-based attacks. In this paper, we propose a dynamically-obfuscated scan design for protecting IPs against scan-based attacks. By perturbing all test patterns/responses and protecting the obfuscation key, the proposed architecture is proven to be robust against existing non-invasive scan attacks, and can protect all scan data from attackers in foundry, assembly, and system developers (i.e., OEMs) without compromising the testability. Furthermore, the proposed architecture can be easily plugged into EDA generated scan chains without having a noticeable impact on conventional integrated circuit (IC) design, manufacturing, and test flow. Finally, detailed security and experimental analyses have been performed on several benchmarks. The results demonstrate that the proposed method can protect chips from existing brute force, differential, and other scan-based attacks that target the obfuscation key. The proposed design is of low overhead on area, power consumption, and pattern generation time, and there is no impact on test time.

An interesting idea of integrated non-Foster self-oscillating radiating system has been introduced recently. The device consists of two identical antennas, a negative impedance converter (NIC) and a tuning circuit. Admittance of one of the antennas is negatively converted via NIC, and cancelled by the positive admittance of the second identical antenna. With the change of frequency, admittances of both antennas change in the exactly same manner. It makes a self-oscillating and perfectly matched pair of antennas, regardless of the operating frequency. The adjustment of the frequency of a self-oscillating signal is achieved by the additional tunable resonant circuit. This paper analyses dynamics of oscillations of such self-oscillating radiating system and compares it with a classical negative resistance oscillator. Moreover, a simple numerical tool for prediction of the frequency and amplitude of oscillations is proposed.

The DES is a symmetric cryptosystem which encrypts data in blocks of 64 bits using 48 bit keys in 16 rounds. It comprises a key schedule, encryption and decryption components. The key schedule, in particular, uses three static component units, the PC-1, PC-2 and rotation tables. However, can these three static components of the key schedule be altered? The DES development team never explained most of these component units. Understanding the DES key schedule is, thus, hard. In addition, reproducing the DES model with unknown component units is challenging, making it hard to adapt and bring implementation of the DES model closer to novice developers' context. We propose an alternative approach for re-implementing the DES key schedule using, rather, dynamic instead of static tables. We investigate the design features of the DES key schedule and implement the same. We then propose a re-engineering view towards a more white-box design. Precisely, generation of the PC-1, rotation and PC-2 tables is revisited to random dynamic tables created at run time. In our views, randomly generated component units eliminate the feared concerns regarding perpetrators' possible knowledge of the internal structures of the static component units. Comparison of the performances of the hybrid DES key schedule to that of the original DES key schedule shows closely related outcomes, connoting the hybrid version as a good alternative to the original model. Memory usage and CPU time were measured. The hybrid insignificantly out-performs the original DES key schedule. This outcome may inspire further researches on possible alterations to other DES component units as well, bringing about completely white-box designs to the DES model.

Many current VM monitoring approaches require guest OS modifications and are also unable to perform application level monitoring, reducing their value in a cloud setting. This paper introduces hprobes, a framework that allows one to dynamically monitor applications and operating systems inside a VM. The hprobe framework does not require any changes to the guest OS, which avoids the tight coupling of monitoring with its target. Furthermore, the monitors can be customized and enabled/disabled while the VM is running. To demonstrate the usefulness of this framework, we present three sample detectors: an emergency detector for a security vulnerability, an application watchdog, and an infinite-loop detector. We test our detectors on real applications and demonstrate that those detectors achieve an acceptable level of performance overhead with a high degree of flexibility.

A honeypot system is used to trapping hackers, track and analyze new hacking methods. However, it does not only take time for construction and deployment but also costs for maintenance because these systems are always online even when there is no attack. Since the main purpose of honeypot systems is to collect more and more attack trafc if possible, the limitation of system capacity is also a major problem. In this paper, we propose Dynamic Virtual Network Honeypot (DVNH) which leverages emerging technologies, Network Function Virtualization and Software-Defined Networking. DVNH redirects the attack to the honeypot system thereby protects the targeted system. Our experiments show that DVNH enables efficient resource usage and dynamic provision of the Honeypot system.

To improve dynamic updating of privacy protected data release caused by multidimensional sensitivity attribute privacy differences in relational data, we propose a dynamic updating method for privacy protection data release based on the multidimensional privacy differences. By adopting the multi-sensitive bucketization technology (MSB), this method performs quantitative classification of the multidimensional sensitive privacy difference and the recorded value, provides the basic updating operation unit, and thereby realizes dynamic updating of privacy protection data release based on the privacy difference among relational data. The experiment confirms that the method can secure the data updating efficiency while ensuring the quality of data release.

A dynamic overlay system is presented for supporting transport service needs of dispersed computing applications for moving data and/or code between network computation points and end-users in IoT or IoBT. The Network Backhaul Layered Architecture (Nebula) system combines network discovery and QoS monitoring, dynamic path optimization, online learning, and per-hop tunnel transport protocol optimization and synthesis over paths, to carry application traffic flows transparently over overlay tunnels. An overview is provided of Nebula's overlay system, software architecture, API, and implementation in the NRL CORE network emulator. Experimental emulation results demonstrate the performance benefits that Nebula provides under challenging networking conditions.

As the traffic congestion increases on the transport network, Payable on the road to slower speeds, longer falter times, as a consequence bigger vehicular queuing, it's necessary to introduce smart way to reduce traffic. We are already edging closer to ``smart city-smart travel''. Today, a large number of smart phone applications and connected sat-naves will help get you to your destination in the quickest and easiest manner possible due to real-time data and communication from a host of sources. In present situation, traffic lights are used in each phase. The other way is to use electronic sensors and magnetic coils that detect the congestion frequency and monitor traffic, but found to be more expensive. Hence we propose a traffic control system using image processing techniques like edge detection. The vehicles will be detected using images instead of sensors. The cameras are installed alongside of the road and it will capture image sequence for every 40 seconds. The digital image processing techniques will be applied to analyse and process the image and according to that the traffic signal lights will be controlled.

The choice of threshold is an important part of fault diagnosis. Most of the current methods use a constant threshold for detection and it is difficult to meet the robustness and sensitivity requirements of the diagnosis system. This article develops a dynamic threshold algorithm for aircraft engine fault detection and isolation systems. The algorithm firstly analyzes the bounded norm uncertainty that may appear in the process of model based on the state space equation, and gives the time domain response range calculation formula under the influence of uncertain parameters; then the Kalman filter is combined to calculate the threshold with the real-time change of state; the simulation is performed at the end. The simulation results show that dynamic threshold range changes with status in real time.

Dynamic tainting is an important part of modern software engineering research. State-of-the-art tools for debugging, bug detection and program analysis make use of this technique. Nonetheless, the research area based on dynamic tainting still has open questions, among others the automatic generation of program inputs. My proposed work concentrates on the use of dynamic tainting for test case generation. The goal is the generation of complex and valid test inputs from scratch. Therefore, I use byte level taint information enhanced with additional static and dynamic program analysis. This information is used in an evolutionary algorithm to create new offsprings and mutations. Concretely, instead of crossing and mutating the whole input randomly, taint information can be used to define which parts of the input have to be mutated. Furthermore, the taint information may also be used to define evolutionary operators. Eventually, the evolutionary algorithm is able to generate valid inputs for a program. Such inputs can be used together with the taint information for further program analysis, e.g. the generation of input grammars.

The plethora of mobile apps introduce critical challenges to digital forensics practitioners, due to the diversity and the large number (millions) of mobile apps available to download from Google play, Apple store, as well as hundreds of other online app stores. Law enforcement investigators often find themselves in a situation that on the seized mobile phone devices, there are many popular and less-popular apps with interface of different languages and functionalities. Investigators would not be able to have sufficient expert-knowledge about every single app, sometimes nor even a very basic understanding about what possible evidentiary data could be discoverable from these mobile devices being investigated. Existing literature in digital forensic field showed that most such investigations still rely on the investigator's manual analysis using mobile forensic toolkits like Cellebrite and Encase. The problem with such manual approaches is that there is no guarantee on the completeness of such evidence discovery. Our goal is to develop an automated mobile app analysis tool to analyze an app and discover what types of and where forensic evidentiary data that app generate and store locally on the mobile device or remotely on external 3rd-party server(s). With the app analysis tool, we will build a database of mobile apps, and for each app, we will create a list of app-generated evidence in terms of data types, locations (and/or sequence of locations) and data format/syntax. The outcome from this research will help digital forensic practitioners to reduce the complexity of their case investigations and provide a better completeness guarantee of evidence discovery, thereby deliver timely and more complete investigative results, and eventually reduce backlogs at crime labs. In this paper, we will present the main technical approaches for us to implement a dynamic Taint analysis tool for Android apps forensics. With the tool, we have analyzed 2,100 real-world Android apps. For each app, our tool produces the list of evidentiary data (e.g., GPS locations, device ID, contacts, browsing history, and some user inputs) that the app could have collected and stored on the devices' local storage in the forms of file or SQLite database. We have evaluated our tool using both benchmark apps and real-world apps. Our results demonstrated that the initial success of our tool in accurately discovering the evidentiary data.

Symbolic execution is an important program analysis technique that provides auxiliary execution semantics to execute programs with symbolic rather than concrete values. There has been much recent interest in symbolic execution for automatic test case generation and security vulnerability detection, resulting in various tools being deployed in academia and industry. Nevertheless, (subtype or dynamic) polymorphism of object-oriented programs has been neglected: existing symbolic execution techniques can explore different targets of conditional branches but not different targets of method invocations. We address the problem of how this polymorphism can be expressed in a symbolic execution framework. We propose the notion of symbolic types, which make object types symbolic. With symbolic types,[ various targets of a method invocation can be explored systematically by mutating the type of the receiver object of the method during automatic test case generation. To the best of our knowledge, this is the first attempt to address polymorphism in symbolic execution. Mutation of method invocation targets is critical for effectively testing object-oriented programs, especially libraries. Our experimental results show that symbolic types are significantly more effective than existing symbolic execution techniques in achieving test coverage and finding bugs and security vulnerabilities in OpenJDK.

The threats of smartphone security are mostly from the privacy disclosure and malicious chargeback software which deducting expenses abnormally. They exploit the vulnerabilities of previous permission mechanism to attack to mobile phones, and what's more, it might call hardware to spy privacy invisibly in the background. As the existing Android operating system doesn't support users the monitoring and auditing of system resources, a dynamic supervisory mechanism of process behavior based on Dalvik VM is proposed to solve this problem. The existing android system framework layer and application layer are modified and extended, and special underlying services of system are used to realize a dynamic supervisory on the process behavior of Dalvik VM. Via this mechanism, each process on the system resources and the behavior of each app process can be monitored and analyzed in real-time. It reduces the security threats in system level and positions that which process is using the system resource. It achieves the detection and interception before the occurrence or the moment of behavior so that it protects the private information, important data and sensitive behavior of system security. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach.

The hyperlink structure of World Wide Web is modeled as a directed, dynamic, and huge web graph. Web graphs are analyzed for determining page rank, fighting web spam, detecting communities, and so on, by performing tasks such as clustering, classification, and reachability. These tasks involve operations such as graph navigation, checking link existence, and identifying active links, which demand scanning of entire graphs. Frequent scanning of very large graphs involves more I/O operations and memory overheads. To rectify these issues, several data structures have been proposed to represent graphs in a compact manner. Even though the problem of representing graphs has been actively studied in the literature, there has been much less focus on representation of dynamic graphs. In this paper, we propose Tree-Dictionary-Representation (TDR), a compressed graph representation that supports dynamic nature of graphs as well as the various graph operations. Our experimental study shows that this representation works efficiently with limited main memory use and provides fast traversal of edges.