Biblio

Homomorphic signatures can provide a credential of a result which is indeed computed with a given function on a data set by an untrusted third party like a cloud server, when the input data are stored with the signatures beforehand. Boneh and Freeman in EUROCRYPT2011 proposed a homomorphic signature scheme for polynomial functions of any degree, however the scheme is not based on the normal short integer solution (SIS) problems as its security assumption. In this paper, we show a homomorphic signature scheme for quadratic polynomial functions those security assumption is based on the normal SIS problems. Our scheme constructs the signatures of multiplication as tensor products of the original signature vectors of input data so that homomorphism holds. Moreover, security of our scheme is reduced to the hardness of the SIS problems respect to the moduli such that one modulus is the power of the other modulus. We show the reduction by constructing solvers of the SIS problems respect to either of the moduli from any forger of our scheme.

Mimicking the collaborative behavior of biological swarms, such as bird flocks and ant colonies, Swarm Intelligence algorithms provide efficient solutions for various optimization problems. On the other hand, a computational model of the human brain, spiking neural networks, has been showing great promise in recognition, inference, and learning, due to recent emergence of neuromorphic hardware for high-efficient and low-power computing. Through bridging these two distinct research fields, we propose a novel computing paradigm that implements the swarm intelligence with a population of coupled spiking neural oscillators in basic leaky integrate-and-fire (LIF) model. Our model behaves as a meta-heuristic searching conducted by multiple collaborative agents. In this design, the oscillating neurons serve as agents in the swarm, search for solutions in frequency coding and communicate with each other through spikes. The firing rate of each agent is adaptive to other agents with better solutions and the optimal solution is rendered as the swarm synchronization is reached. We apply the proposed method to the parameter optimization in several test objective functions and demonstrate its effectiveness and efficiency. Our new computing paradigm expands the computational power of coupled spiking neurons in the field of solving optimization problem and brings opportunities for the connection between individual intelligence and swarm intelligence.

The Internet routing ecosystem is facing substantial scalability challenges on the data plane. Various “clean slate” architectures for representing forwarding tables (FIBs), such as IPv6, introduce additional constraints on efficient implementations from both lookup time and memory footprint perspectives due to significant classification width. In this work, we propose an abstraction layer able to represent IPv6 FIBs on existing IP and even MPLS infrastructure. Feasibility of the proposed representations is confirmed by an extensive simulation study on real IPv6 forwarding tables, including low-level experimental performance evaluation.

This paper briefly presents a position that hardware-based roots of trust, integrated in silicon with System-on-Chip (SoC) solutions, represent the most current stage in a progression of technologies aimed at realizing the most foundational computer security concepts. A brief look at this historical progression from a personal perspective is followed by an overview of more recent developments, with particular focus on a root of trust for cryptographic key provisioning and SoC feature management aimed at achieving supply chain assurances and serves as a basis for trust that is linked to properties enforced in hardware. The author assumes no prior knowledge of these concepts and developments by the reader.

This paper describes Smartpig, an algorithm for the iterative mosaicking of images of a planar surface using a unique parameterization which decomposes inter-image projective warps into camera intrinsics, fronto-parallel projections, and inter-image similarities. The constraints resulting from the inter-image alignments within an image set are stored in an undirected graph structure allowing efficient optimization of image projections on the plane. Camera pose is also directly recoverable from the graph, making Smartpig a feasible solution to the problem of simultaneous location and mapping (SLAM). Smartpig is demonstrated on a set of 144 high resolution aerial images and evaluated with a number of metrics against ground control.

The cuttlefish optimization algorithm is a new combinatorial optimization algorithm in the family of metaheuristics, applied in the continuous domain, and which provides mechanisms for local and global research. This paper presents a new adaptation of this algorithm in the discrete case, solving the famous travelling salesman problem, which is one of the discrete combinatorial optimization problems. This new adaptation proposes a reformulation of the equations to generate solutions depending a different algorithm cases. The experimental results of the proposed algorithm on instances of TSPLib library are compared with the other methods, show the efficiency and quality of this adaptation.

In the Internet-of-Things (IoT), users might share part of their data with different IoT prosumers, which offer applications or services. Within this open environment, the existence of an adversary introduces security risks. These can be related, for instance, to the theft of user data, and they vary depending on the security controls that each IoT prosumer has put in place. To minimize such risks, users might seek an “optimal” set of prosumers. However, assuming the adversary has the same information as the users about the existing security measures, he can then devise which prosumers will be preferable (e.g., with the highest security levels) and attack them more intensively. This paper proposes a decision-support approach that minimizes security risks in the above scenario. We propose a non-cooperative, two-player game entitled Prosumers Selection Game (PSG). The Nash Equilibria of PSG determine subsets of prosumers that optimize users' payoffs. We refer to any game solution as the Nash Prosumers Selection (NPS), which is a vector of probabilities over subsets of prosumers. We show that when using NPS, a user faces the least expected damages. Additionally, we show that according to NPS every prosumer, even the least secure one, is selected with some non-zero probability. We have also performed simulations to compare NPS against two different heuristic selection algorithms. The former is proven to be approximately 38% more effective in terms of security-risk mitigation.

This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance. Our results show that accurate outlier identification is achievable using very few linear summaries of the original data matrix - as few as the squared rank of the low-rank component plus the number of outliers, times constant and logarithmic factors. We demonstrate the performance of our approach experimentally in two stylized applications, one motivated by robust collaborative filtering tasks, and the other by saliency map estimation tasks arising in computer vision and automated surveillance.

Communication architecture is a crucial component in smart grid. Most of the previous researches have been focused on the traditional Internet and proposed numerous evolutionary designs. However, the traditional network architecture has been reported with multiple inherent shortcomings, which bring unprecedented challenges for the Smart Grid. Moreover, the smart network architecture for the future Smart Grid is still unexplored. In this context, this paper proposes a clean-slate communication approach to boost the development of smart grid in the respective of Smart Identifier Network (SINET), named SI4SG. It also designs the service resolution mechanism and the ns-3 based simulating tool for the proposed communication architecture.

The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs ("insufficient evidence versus evidence collection risk" and "filtering false evidence sequentially and distributedly"), and propose a simple yet effective method, look ahead, to address the challenges. Furthermore, we propose two extensions to look ahead, dogmatic filtering, and adaptive look ahead, to address the challenge of "malicious nodes sharing false evidence." Real mobile network traces are used to verify the effectiveness of the proposed methods.
 

Since the massive deployment of Cyber-Physical Systems (CPSs) calls for long-range and reliable communication services with manageable cost, it has been believed to be an inevitable trend to relay a significant portion of CPS traffic through existing networking infrastructures such as the Internet. Adversaries who have access to networking infrastructures can therefore eavesdrop network traffic and then perform traffic analysis attacks in order to identify CPS sessions and subsequently launch various attacks. As we can hardly prevent all adversaries from accessing network infrastructures, thwarting traffic analysis attacks becomes indispensable. Traffic morphing serves as an effective means towards this direction. In this paper, a novel traffic morphing algorithm, CPSMorph, is proposed to protect CPS sessions. CPSMorph maintains a number of network sessions whose distributions of inter-packet delays are statistically indistinguishable from those of typical network sessions. A CPS message will be sent through one of these sessions with assured satisfaction of its time constraint. CPSMorph strives to minimize the overhead by dynamically adjusting the morphing process. It is characterized by low complexity as well as high adaptivity to changing dynamics of CPS sessions. Experimental results have shown that CPSMorph can effectively performing traffic morphing for real-time CPS messages with moderate overhead.
 

Cloud computing brings in a lot of advantages for enterprise IT infrastructure; virtualization technology, which is the backbone of cloud, provides easy consolidation of resources, reduction of cost, space and management efforts. However, security of critical and private data is a major concern which still keeps back a lot of customers from switching over from their traditional in-house IT infrastructure to a cloud service. Existence of techniques to physically locate a virtual machine in the cloud, proliferation of software vulnerability exploits and cross-channel attacks in-between virtual machines, all of these together increases the risk of business data leaks and privacy losses. This work proposes a framework to mitigate such risks and engineer customer trust towards enterprise cloud computing. Everyday new vulnerabilities are being discovered even in well-engineered software products and the hacking techniques are getting sophisticated over time. In this scenario, absolute guarantee of security in enterprise wide information processing system seems a remote possibility; software systems in the cloud are vulnerable to security attacks. Practical solution for the security problems lies in well-engineered attack mitigation plan. At the positive side, cloud computing has a collective infrastructure which can be effectively used to mitigate the attacks if an appropriate defense framework is in place. We propose such an attack mitigation framework for the cloud. Software vulnerabilities in the cloud have different severities and different impacts on the security parameters (confidentiality, integrity, and availability). By using Markov model, we continuously monitor and quantify the risk of compromise in different security parameters (e.g.: change in the potential to compromise the data confidentiality). Whenever, there is a significant change in risk, our framework would facilitate the tenants to calculate the Mean Time to Security Failure (MTTSF) cloud and allow them to adopt a dynamic mitigation plan. This framework is an add-on security layer in the cloud resource manager and it could improve the customer trust on enterprise cloud solutions.

With the growing demand for increased spectral efficiencies, there has been renewed interest in enabling full-duplex communications. However, existing approaches to enable full-duplex require a clean-slate approach to address the key challenge in full-duplex, namely self-interference suppression. This serves as a big deterrent to enabling full-duplex in existing cellular networks. Towards our vision of enabling full-duplex in legacy cellular, specifically LTE networks, with no modifications to existing hardware at BS and client as well as technology specific industry standards, we present the design of our experimental system FD-LTE, that incorporates a combination of passive SI cancellation schemes, with legacy LTE half-duplex BS and client devices. We build a prototype of FD-LTE, integrate it with LTE's evolved packet core and conduct over-the-air experiments to explore the feasibility and potential for full-duplex with legacy LTE networks. We report promising experimental results from FD-LTE, which currently applies to scenarios with limited ranges that is typical of small cells.
 

Wireless sensor networks (WSNs) are prone to propagating malware because of special characteristics of sensor nodes. Considering the fact that sensor nodes periodically enter sleep mode to save energy, we develop traditional epidemic theory and construct a malware propagation model consisting of seven states. We formulate differential equations to represent the dynamics between states. We view the decision-making problem between system and malware as an optimal control problem; therefore, we formulate a malware-defense differential game in which the system can dynamically choose its strategies to minimize the overall cost whereas the malware intelligently varies its strategies over time to maximize this cost. We prove the existence of the saddle-point in the game. Further, we attain optimal dynamic strategies for the system and malware, which are bang-bang controls that can be conveniently operated and are suitable for sensor nodes. Experiments identify factors that influence the propagation of malware. We also determine that optimal dynamic strategies can reduce the overall cost to a certain extent and can suppress the malware propagation. These results support a theoretical foundation to limit malware in WSNs.

Information is increasing quickly, database owners have tendency to outsource their data to an external service provider called Cloud Computing. Using Cloud, clients can remotely store their data without burden of local data storage and maintenance. However, such service provider is untrusted, therefore there are some challenges in data security: integrity, availability and confidentiality. Since integrity and availability are prerequisite conditions of the existence of a system, we mainly focus on them rather than confidentiality. To ensure integrity and availability, researchers have proposed network coding-based POR (Proof of Retrievability) schemes that enable the servers to demonstrate whether the data is retrievable or not. However, most of network coding-based POR schemes are inefficient in data checking and also cannot prevent a common attack in POR: small corruption attack. In this paper, we propose a new network coding-based POR scheme using dispersal code in order to reduce cost in checking phase and also to prevent small corruption attack.

Networked control systems consist of distributed sensors and actuators that communicate via a wireless network. The use of an open wireless medium and unattended deployment leaves these systems vulnerable to intelligent adversaries whose goal is to disrupt the system performance. In this paper, we study the wormhole attack on a networked control system, in which an adversary establishes a link between two geographically distant regions of the network by using either high-gain antennas, as in the out-of-band wormhole, or colluding network nodes as in the in-band wormhole. Wormholes allow the adversary to violate the timing constraints of real-time control systems by first creating low-latency links, which attract network traffic, and then delaying or dropping packets. Since the wormhole attack reroutes and replays valid messages, it cannot be detected using cryptographic mechanisms alone. We study the impact of the wormhole attack on the network flows and delays and introduce a passivity-based control-theoretic framework for modeling and mitigating the wormhole attack. We develop this framework for both the in-band and out-of-band wormhole attacks as well as complex, hereto-unreported wormhole attacks consisting of arbitrary combinations of in-and out-of band wormholes. By integrating existing mitigation strategies into our framework, we analyze the throughput, delay, and stability properties of the overall system. Through simulation study, we show that, by selectively dropping control packets, the wormhole attack can cause disturbances in the physical plant of a networked control system, and demonstrate that appropriate selection of detection parameters mitigates the disturbances due to the wormhole while satisfying the delay constraints of the physical system.

Networked control systems consist of distributed sensors and actuators that communicate via a wireless network. The use of an open wireless medium and unattended deployment leaves these systems vulnerable to intelligent adversaries whose goal is to disrupt the system performance. In this paper, we study the wormhole attack on a networked control system, in which an adversary establishes a link between two geographically distant regions of the network by using either high-gain antennas, as in the out-of-band wormhole, or colluding network nodes as in the in-band wormhole. Wormholes allow the adversary to violate the timing constraints of real-time control systems by first creating low-latency links, which attract network traffic, and then delaying or dropping packets. Since the wormhole attack reroutes and replays valid messages, it cannot be detected using cryptographic mechanisms alone. We study the impact of the wormhole attack on the network flows and delays and introduce a passivity-based control-theoretic framework for modeling and mitigating the wormhole attack. We develop this framework for both the in-band and out-of-band wormhole attacks as well as complex, hereto-unreported wormhole attacks consisting of arbitrary combinations of in-and out-of band wormholes. By integrating existing mitigation strategies into our framework, we analyze the throughput, delay, and stability properties of the overall system. Through simulation study, we show that, by selectively dropping control packets, the wormhole attack can cause disturbances in the physical plant of a networked control system, and demonstrate that appropriate selection of detection parameters mitigates the disturbances due to the wormhole while satisfying the delay constraints of the physical system.

Very often in the software development life cycle, security is applied too late or important security aspects are overlooked. Although the use of security patterns is gaining popularity, the current state of security requirements patterns is such that there is not much in terms of a defining structure. To address this issue, we are working towards defining the important characteristics as well as the boundaries for security requirements patterns in order to make them more effective. By examining an existing general pattern format that describes how security patterns should be structured and comparing it to existing security requirements patterns, we are deriving characterizations and boundaries for security requirements patterns. From these attributes, we propose a defining format. We hope that these can reduce user effort in elicitation and specification of security requirements patterns.