Biblio

Conventional program analyses have made great strides by leveraging logical reasoning. However, they cannot handle uncertain knowledge, and they lack the ability to learn and adapt. This in turn hinders the accuracy, scalability, and usability of program analysis tools in practice. We seek to address these limitations by proposing a methodology and framework for incorporating probabilistic reasoning directly into existing program analyses that are based on logical reasoning. We demonstrate that the combined approach can benefit a number of important applications of program analysis and thereby facilitate more widespread adoption of this technology.

Mobility and multihoming have become the norm in Internet access, e.g. smartphones with Wi-Fi and LTE, and connected vehicles with LTE and DSRC links that change rapidly. Mobility creates challenges for active session continuity when provider-aggregatable locators are used, while multihoming brings opportunities for improving resiliency and allocative efficiency. This paper proposes a novel migration protocol, in the context of the eXpressive Internet Architecture (XIA), the XIA Migration Protocol. We compare it with Mobile IPv6, with respect to handoff latency and overhead, flow migration support, and defense against spoofing and replay of protocol messages. Handoff latencies of the XIA Migration Protocol and Mobile IPv6 Enhanced Route Optimization are comparable and neither protocol opens up avenues for spoofing or replay attacks. However, XIA requires no mobility anchor point to support client mobility while Mobile IPv6 always depends on a home agent. We show that XIA has significant advantage over IPv6 for multihomed hosts and networks in terms of resiliency, scalability, load balancing and allocative efficiency. IPv6 multihoming solutions either forgo scalability (BGP-based) or sacrifice resiliency (NAT-based), while XIA's fallback-based multihoming provides fault tolerance without a heavy-weight protocol. XIA also allows fine-grained incoming load-balancing and QoS-matching by supporting flow migration. Flow migration is not possible using Mobile IPv6 when a single IPv6 address is associated with multiple flows. From a protocol design and architectural perspective, the key enablers of these benefits are flow-level migration, XIA's DAG-based locators and self-certifying identifiers.

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.

Based on the feature analysis of image content, this paper proposes a novel steganalytic method for grayscale images in spatial domain. In this work, we firstly investigates directional lifting wavelet transform (DLWT) as a sparse representation in compressive sensing (CS) domain. Then a block CS (BCS) measurement matrix is designed by using the generalized Gaussian distribution (GGD) model, in which the measurement matrix can be used to sense the DLWT coefficients of images to reflect the feature residual introduced by steganography. Extensive experiments are showed that proposed scheme CS-based is feasible and universal for detecting stegography in spatial domain.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

In many-core systems, the processing elements are interconnected using Networks-on-Chip. An example of on-chip network is SoCIN, a low-cost interconnect architecture whose original design did not take into account security aspects. This network is vulnerable to eavesdropping and spoofing attacks, what limits its use in systems that require security. This work addresses this issue and aims to ensure the security properties of confidentiality and authenticity of SoCIN-based systems. For this, we propose the use of security mechanisms based on symmetric encryption at the network level using the AES (Advanced Encryption Standard) model. A reference multi-core platform was implemented and prototyped in programmable logic aiming at performing experiments to evaluate the implemented mechanisms. Results demonstrate the effectiveness of the proposed solution in protecting the system against the target attacks. The impact on the network performance is acceptable and the silicon overhead is equivalent to other solutions found in the literature.

Recent developments in robotics and virtual reality (VR) are making embodied agents familiar, and social behaviors of embodied conversational agents are essential to create mindful daily lives with conversational agents. Especially, natural nonverbal behaviors are required, such as gaze and gesture movement. We propose a novel method to create an agent with human-like gaze as a listener in multi-party conversation, using Hidden Markov Model (HMM) to learn the behavior from real conversation examples. The model can generate gaze reaction according to users' gaze and utterance. We implemented an agent with proposed method, and created VR environment to interact with the agent. The proposed agent reproduced several features of gaze behavior in example conversations. Impression survey result showed that there is at least a group who felt the proposed agent is similar to human and better than conventional methods.

Social awareness and social ties are becoming increasingly fashionable with emerging mobile and handheld devices. Social trust degree describing the strength of the social ties has drawn lots of research interests in many fields including secure cooperative communications. Such trust degree reflects the users' willingness for cooperation, which impacts the selection of the cooperative users in the practical networks. In this paper, we propose a cooperative relay and jamming selection scheme to secure communication based on the social trust degree under a stochastic geometry framework. We aim to analyze the involved secrecy outage probability (SOP) of the system's performance. To achieve this target, we propose a double Gamma ratio (DGR) approach through Gamma approximation. Based on this, the SOP is tractably obtained in closed form. The simulation results verify our theoretical findings, and validate that the social trust degree has dramatic influences on the network's secrecy performance.

A covert communication system under block fading channels is considered, where users experience uncertainty about their channel knowledge. The transmitter seeks to hide the covert communication to a private user by exploiting a legitimate public communication link, while the warden tries to detect this covert communication by using a radiometer. We derive the exact expression for the radiometer's optimal threshold, which determines the performance limit of the warden's detector. Furthermore, for given transmission outage constraints, the achievable rates for legitimate and covert users are analyzed, while maintaining a specific level of covertness. Our numerical results illustrate how the achievable performance is affected by the channel uncertainty and required level of covertness.

Wireless communications in Cyber-Physical Systems (CPS) are vulnerable to many adversarial attacks such as eavesdropping. To secure the communications, secret session keys need to be established between wireless devices. In existing symmetric key establishment protocols, it is assumed that devices are pre-loaded with secrets. In the CPS, however, wireless devices are produced by different companies. It is not practical to assume that the devices are pre-loaded with certain secrets when they leave companies. As a consequence, existing symmetric key establishment protocols cannot be directly implemented in the CPS. Motivated by these observations, this paper presents a cross-layer key establishment model for heterogeneous wireless devices in the CPS. Specifically, by implementing our model, wireless devices extract master keys (shared with the system authority) at the physical layer using ambient wireless signals. Then, the system authority distributes secrets for devices (according to an existing symmetric key establishment protocol) by making use of the extracted master keys. Completing these operations, wireless devices can establish secret session keys at higher layers by calling the employed key establishment protocol. Additionally, we prove the security of the proposed model. We analyse the performance of the new model by implementing it and converting existing symmetric key establishment protocols into cross-layer key establishment protocols.

In several critical military missions, more than one decision level are involved. These decision levels are often independent and distributed, and sensitive pieces of information making up the military mission must be kept hidden from one level to another even if all of the decision levels cooperate to accomplish the same task. Usually, a mission is negotiated through insecure networks such as the Internet using cryptographic protocols. In such protocols, few security properties have to be ensured. However, designing a secure cryptographic protocol that ensures several properties at once is a very challenging task. In this paper, we propose a new secure protocol for multipart military missions that involve two independent and distributed decision levels having different security levels. We show that it ensures the secrecy, authentication, and non-repudiation properties. In addition, we show that it resists against man-in-the-middle attacks.

Nowadays, an increasing number of IoT vendors have complied and deployed third-party code bases across different architectures. Therefore, to avoid the firmware from being affected by the same known vulnerabilities, searching known vulnerabilities in binary firmware across different architectures is more crucial than ever. However, most of existing vulnerability search methods are limited to the same architecture, there are only a few researches on cross-architecture cases, of which the accuracy is not high. In this paper, to promote the accuracy of existing cross-architecture vulnerability search methods, we propose a new approach based on Support Vector Machine (SVM) and Attributed Control Flow Graph (ACFG) to search known vulnerability in firmware across different architectures at function level. We employ a known vulnerability function to recognize suspicious functions in other binary firmware. First, considering from the internal and external characteristics of the functions, we extract the function level features and basic-block level features of the functions to be inspected. Second, we employ SVM to recognize a little part of suspicious functions based on function level features. After the preliminary screening, we compute the graph similarity between the vulnerability function and suspicious functions based on their ACFGs. We have implemented our approach CVSSA, and employed the training samples to train the model with previous knowledge to improve the accuracy. We also search several vulnerabilities in the real-world firmware images, the experimental results show that CVSSA can be applied to the realistic scenarios.

With the integration of computing, communication, and physical processes, the modern power grid is becoming a large and complex cyber physical power system (CPPS). This trend is intended to modernize and improve the efficiency of the power grid, yet it makes the CPPS vulnerable to potential cascading failures caused by cyber-attacks, e.g., the attacks that are originated by the cyber network of CPPS. To prevent these risks, it is essential to analyze how cyber-attacks can be conducted against the CPPS and how they can affect the power systems. In light of that General Packet Radio Service (GPRS) has been widely used in CPPS, this paper provides a case study by examining possible cyber-attacks against the cyber-physical power systems with GPRS-based SCADA system. We analyze the vulnerabilities of GPRS-based SCADA systems and focus on DoS attacks and message spoofing attacks. Furthermore, we show the consequence of these attacks against power systems by a simulation using the IEEE 9-node system, and the results show the validity of cascading failures propagated through the systems under our proposed attacks.

With the rapid development of smart grid, smart meters are deployed at energy consumers' premises to collect real-time usage data. Although such a communication model can help the control center of the energy producer to improve the efficiency and reliability of electricity delivery, it also leads to some security issues. For example, this real-time data involves the customers' privacy. Attackers may violate the privacy for house breaking, or they may tamper with the transmitted data for their own benefits. For this purpose, many data aggregation schemes are proposed for privacy preservation. However, rare of them cares about both the data aggregation and fine-grained access control to improve the data utility. In this paper, we proposes a data aggregation scheme based on attribute decision tree. Security analysis illustrates that our scheme can achieve the data integrity, data privacy preservation and fine- grained data access control. Experiment results show that our scheme are more efficient than existing schemes.

The start-up value of an SRAM cell is unique, random, and unclonable as it is determined by the inherent process mismatch between transistors. These properties make SRAM an attractive circuit for generating encryption keys. The primary challenge for SRAM based key generation, however, is the poor stability when the circuit is subject to random noise, temperature and voltage changes, and device aging. Temporal majority voting (TMV) and bit masking were used in previous works to identify and store the location of unstable or marginally stable SRAM cells. However, TMV requires a long test time and significant hardware resources. In addition, the number of repetitive power-ups required to find the most stable cells is prohibitively high. To overcome the shortcomings of TMV, we propose a novel data remanence based technique to detect SRAM cells with the highest stability for reliable key generation. This approach requires only two remanence tests: writing `1' (or `0') to the entire array and momentarily shutting down the power until a few cells flip. We exploit the fact that the cells that are easily flipped are the most robust cells when written with the opposite data. The proposed method is more effective in finding the most stable cells in a large SRAM array than a TMV scheme with 1,000 power-up tests. Experimental studies show that the 256-bit key generated from a 512 kbit SRAM using the proposed data remanence method is 100% stable under different temperatures, power ramp up times, and device aging.

Convolutional Neural Network (CNN) based methods have shown significant performance gains in the problem of visual tracking in recent years. Due to many uncertain changes of objects online, such as abrupt motion, background clutter and large deformation, the visual tracking is still a challenging task. We propose a novel algorithm, namely Deep Location-Specific Tracking, which decomposes the tracking problem into a localization task and a classification task, and trains an individual network for each task. The localization network exploits the information in the current frame and provides a specific location to improve the probability of successful tracking, while the classification network finds the target among many examples generated around the target location in the previous frame, as well as the one estimated from the localization network in the current frame. CNN based trackers often have massive number of trainable parameters, and are prone to over-fitting to some particular object states, leading to less precision or tracking drift. We address this problem by learning a classification network based on 1 × 1 convolution and global average pooling. Extensive experimental results on popular benchmark datasets show that the proposed tracker achieves competitive results without using additional tracking videos for fine-tuning. The code is available at https://github.com/ZjjConan/DLST