Biblio

As demonstrated recently, Wireless Physical Layer Security (WPLS) has the potential to offer substantial advantages for key management for small resource-constrained and, therefore, low-cost IoT-devices, e.g., the widely applied 8-bit MCU 8051. In this paper, we present a WPLS testbed implementation for independent performance and security evaluations. The testbed is based on off-the-shelf hardware and utilizes the IEEE 802.15.4 communication standard for key extraction and secret key rate estimation in real-time. The testbed can include generically multiple transceivers to simulate legitimate parties or eavesdropper. We believe with the testbed we provide a first step to make experimental-based WPLS research results comparable. As an example, we present evaluation results of several test cases we performed, while for further information we refer to https://pls.rub.de.

In cognitive radio network, the primary user (PU) network and the secondary user (SU) network interfered with each other because of sharing the spectral resource. Also interference among multi-downlinks in SU network decreased the sum rate in SU network and the eavesdropper in PU network decreased the secrecy rate in PU network. Focusing on above problem, this paper raised two channel selection and beamforming methods based on singular value decomposition (SVD) and uplink-downlink duality respectively, and then analyzed the performance of them in physical layer security.

In this paper, we address the physical layer security problem for Wireless Sensor Networks in the presence of passive eavesdroppers, i.e., the eavesdroppers' channels are unknown to the transmitter. We use a multi-antenna relay to guarantee physical layer security. Different from the existing work, we consider that the relay works in full duplex mode and transmits artificial noise (AN) in both stages of the decode-and-forward (DF) cooperative strategy. We proposed two optimal power allocation strategies for power constrained and power unconstrained systems respectively. For power constrained system, our aim is to minimize the secrecy rate outage probability. And for power unconstrained systems, we obtain the optimal power allocation to minimize the total power under the quality of service and secrecy constraints. We also consider the secrecy outage probability for different positions of eavesdropper. Simulation results are presented to show the performance of the proposed strategies.

The cyclostationary characteristics of signals has some important applications in such as blind channel equalization, blind adaptive beamforming, and system identification. However, the cyclostationary characteristics also can be a weak link in physical layer security. With high-order cyclostationary theory, some system information can be obtained easily. In this paper, we proposed a new algorithm based on constellation phase rotation and amplitude randomization, during which the cyclostationary feature of signals can be suppressed.

In this paper, we analyze the security-reliability tradeoff (SRT) performance of the multi-relay cooperative networks over Nakagami-m fading channels. By considering the reliability of the first phase from the source to relay, a cooperative jamming (CJ) assisted secure transmission scheme is investigated to improve the security performance of the considered system. Specifically, we derive the approximate closed-form expression of the outage probability (OP) and exact closed-form expression of the intercepted probability (IP) for the CJ scheme to evaluate the SRT performance of the system. Finally, the simulation results verify the validity of our theoretical derivations and the advantage of the CJ scheme compared to the traditional scheme with no cooperative jammer.

Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into 'automated' and 'human' users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.

Anthropomorphic artificial agents, computed characters or humanoid robots, can be sued to investigate human cognition. They are intrinsically ambivalent. They appear and act as humans, hence we should tend to consider them as human, yet we know they are machine designed by humans, and should not consider them as humans. Reviewing a number of behavioral and neurophysiological studies provides insights into social mechanisms that are primarily influenced by the appearance of the agent, and in particular its resemblance to humans, and other mechanisms that are influenced by the knowledge we have about the artificial nature of the agent. A significant finding is that, as expected, humans don't naturally adopt an intentional stance when interacting with artificial agents.

In contrast with goal-oriented dialogue, social dialogue has no clear measure of task success. Consequently, evaluation of these systems is notoriously hard. In this paper, we review current evaluation methods, focusing on automatic metrics. We conclude that turn-based metrics often ignore the context and do not account for the fact that several replies are valid, while end-of-dialogue rewards are mainly hand-crafted. Both lack grounding in human perceptions.

With the repaid growth of social tagging users, it becomes very important for social tagging systems how the required resources are recommended to users rapidly and accurately. Firstly, the architecture of an agent-based intelligent social tagging system is constructed using agent technology. Secondly, the design and implementation of user interest mining, personalized recommendation and common preference group recommendation are presented. Finally, a self-adaptive recommendation strategy for social tagging and its implementation are proposed based on the analysis to the shortcoming of the personalized recommendation strategy and the common preference group recommendation strategy. The self-adaptive recommendation strategy achieves equilibrium selection between efficiency and accuracy, so that it solves the contradiction between efficiency and accuracy in the personalized recommendation model and the common preference recommendation model.

Given social media users' plethora of interactions, appropriately controlling access to such information becomes a challenging task for users. Selecting the appropriate audience, even from within their own friend network, can be fraught with difficulties. PACMAN is a potential solution for this dilemma problem. It's a personal assistant agent that recommends personalized access control decisions based on the social context of any information disclosure by incorporating communities generated from the user's network structure and utilizing information in the user's profile. PACMAN provides accurate recommendations while minimizing intrusiveness.

Whether an idea, information, disease, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in "bursts," active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns – some are bursty and others are not. We model diffusion on networks in which agents differ in their activity patterns. We show that bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any Poisson behavior.

In this paper we discuss building large scale virtual reality reconstructions of historical heritage sites and populating it with crowds of virtual agents. Such agents are capable of performing complex actions, while respecting the cultural and historical accuracy of agent behaviour. In many commercial video games such agents either have very limited range of actions (resulting primitive behaviour) or are manually designed (resulting high development costs). In contrast, we follow the principles of automatic goal generation and automatic planning. Automatic goal generation in our approach is achieved through simulating agent needs and then producing a goal in response to those needs that require satisfaction. Automatic planning refers to techniques that are concerned with producing sequences of actions that can successfully change the state of an agent to the state where its goals are satisfied. Classical planning algorithms are computationally costly and it is difficult to achieve real-time performance for our problem domain with those. We explain how real-time performance can be achieved with Case-Based Planning, where agents build plan libraries and learn how to reuse and combine existing plans to archive their dynamically changing goals. We illustrate the novelty of our approach, its complexity and associated performance gains through a case-study focused on developing a virtual reality reconstruction of an ancient Mesopotamian settlement in 5000 B.C.

Nonverbal cues are considered the most important part in social communication. Many people desire people; but due to the stigma and unavailability of resources, they are unable to practice their social skills. In this work, we envision a virtual assistant that can give individuals real-time feedback on their smiles, eye-contact, body language and volume modulation that is available anytime, anywhere using a computer browser. To instantiate our idea, we have set up a Wizard-of-Oz study in the context of speed-dating with 47 individuals. We collected videos of the participants having a conversation with a virtual agent before and after of a speed-dating session. This study revealed that the participants who used our system improved their gesture in a face-to-face conversation. Our next goal is to explore different machine learning techniques on the facial and prosodic features to automatically generate feedback on the nonverbal cues. In addition, we want to explore different strategies of conveying real-time feedback that is non-threatening, repeatable, objective and more likely to transfer to a real-world conversation.

In interpersonal interactions, humans speak in part by considering their social distance and position with respect to other people, thereby developing relationships. In our research, we focus on positive politeness (PP), a strategy for positively reducing the distance people in human communication using language. In addition, we propose an agent that attempts to actively interact with humans. First, we design a dialog system based on the politeness theory. Next, we examine the effect of our proposed method on interactions. For our experiments, we implemented two agents:the method proposed for performing PP and a conventional method that performs negative politeness based on the unobjectionable behavior. We then compare and analyze impressions of experiment participants in response to the two agents. From our results, male participants accepted PP more frequently than female participants. Further, the proposed method lowered the perceived sense of interacting with a machine for male participants.

For optimal human-robot interaction, understanding the determinants and components of anthropomorphism is crucial. This research assessed the influence of an agent's social cues and controlling language use on user's perceptions of the agent's expertise, sociability, and trustworthiness. In a game context, the agent attempted to persuade users to modify their choices using high or low controlling language and using different levels of social cues (advice with text-only with no robot embodiment as the agent, a robot with elementary social cues, and a robot with advanced social cues). As expected, low controlling language lead to higher perceived anthropomorphism, while the robotic agent with the most social cues was selected as the most expert advisor and the non-social agent as the most trusted advisor.

With increasing interest in the development of intelligent agents capable of learning, proficiently automating tasks, and gaining world knowledge, the importance of integrating the ability to converse naturally with users is more crucial now than ever before. This thesis aims to understand and characterize different aspects of social language to facilitate the development of intelligent agents that are socially aware and able to engage users to a level that was not previously possible with language generation systems. Using various machine learning algorithms and data-driven approaches to model the nuances of social language in dialogue, such as factual and emotional expression, sarcasm and humor and the related subclasses of rhetorical questions and hyperbole, we can come closer to modeling the characteristics of the social language that allows us to express emotion and knowledge, and thereby exhibit these styles in the agents we develop.

Since the Information Networks are added to the current electricity networks, the security and privacy of individuals is challenged. This combination of technologies creates vulnerabilities in the context of smart grid power which disrupt the consumer energy supply. Methods based on encryption are against the countermeasures attacks that have targeted the integrity and confidentiality factors. Although the cryptography strategies are used in Smart Grid, key management which is different in size from tens to millions of keys (for meters), is considered as the critical processes. The Key mismanagement causes to reveal the secret keys for attacker, a symmetric key distribution method is recently suggested by [7] which is based on a symmetric key distribution, this strategy is very suitable for smart electric meters. The problem with this method is its vulnerability to impersonating respondents attack. The proposed approach to solve this problem is to send the both side identifiers in encrypted form based on hash functions and a random value, the proposed solution is appropriate for devices such as meters that have very little computing power.

By applying power usage statistics from smart meters, users are able to save energy in their homes or control smart appliances via home automation systems. However, owing to security and privacy concerns, it is recommended that smart meters (SM) should not have direct communication with smart appliances. In this paper, we propose a design for a smart meter gateway (SMGW) associated with a two-phase authentication mechanism and key management scheme to link a smart grid with smart appliances. With placement of the SMGW, we can reduce the design complexity of SMs as well as enhance the strength of security.

Integration of information technologies with the current power infrastructure promises something further than a smart grid: implementation of smart cities. Power efficient cities will be a significant step toward greener cities and a cleaner environment. However, the extensive use of information technologies in smart cities comes at a cost of reduced privacy. In particular, consumers' power profiles will be accessible by third parties seeking information over consumers' personal habits. In this paper, a methodology for enhancing privacy of electricity consumption patterns is proposed and tested. The proposed method exploits digital connectivity and predictive tools offered via smart grids to morph consumption patterns by grouping consumers via an optimization scheme. To that end, load anticipation, correlation and Theil coefficients are utilized synergistically with genetic algorithms to find an optimal assembly of consumers whose aggregated pattern hides individual consumption features. Results highlight the efficiency of the proposed method in enhancing privacy in the environment of smart cities.

The collection of high frequency metering data in the emerging smart grid gives rise to the concern of consumer privacy. Anonymization of metering data is one of the proposed approaches in the literature, which enables transmission of unmasked data while preserving the privacy of the sender. Distributed anonymization methods can reduce the dependency on service providers, thus promising more privacy for the consumers. However, the distributed communication among the end-users introduces overhead and requires methods to prevent external attacks. In this paper, we propose four variants of a distributed anonymization method for smart metering data privacy, referred to as the Collaborative Anonymity Set Formation (CASF) method. The performance overhead analysis and security analysis of the variants are done using NS-3 simulator and the Scyther tool, respectively. It is shown that the proposed scheme enhances the privacy preservation functionality of an existing anonymization scheme, while being robust against external 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.

Legal metrology embraces the regulation and control of measuring instruments (MI) used in a diversity of applications including industry, transportation, commerce, medical care and environment protection [3]. Only in Europe, MI are responsible for an annual turnover of more than 500 billion Euros [1]. In developing countries, MI demand has increased substantially due to the adoption of technologies and methods well established in developed countries [3]. MI also can be seen as elementary build blocks for new technologies such as smart grids, Internet of Things and cyber physical systems [1, 2]. Thus legal metrology is crucial to assure the correctness of measurements, protecting the economic system while regulating consumer relations and enhances MI reliability [2].

Power grids are undergoing major changes due to rapid growth in renewable energy resources and improvements in battery technology. While these changes enhance sustainability and efficiency, they also create significant management challenges as the complexity of power systems increases. To tackle these challenges, decentralized Internet-of-Things (IoT) solutions are emerging, which arrange local communities into transactive microgrids. Within a transactive microgrid, "prosumers" (i.e., consumers with energy generation and storage capabilities) can trade energy with each other, thereby smoothing the load on the main grid using local supply. It is hard, however, to provide security, safety, and privacy in a decentralized and transactive energy system. On the one hand, prosumers' personal information must be protected from their trade partners and the system operator. On the other hand, the system must be protected from careless or malicious trading, which could destabilize the entire grid. This paper describes Privacy-preserving Energy Transactions (PETra), which is a secure and safe solution for transactive microgrids that enables consumers to trade energy without sacrificing their privacy. PETra builds on distributed ledgers, such as blockchains, and provides anonymity for communication, bidding, and trading.

In this work, we analyze the feasibility of a physically secure implementation of the quantum-resistant supersingular isogeny Diffie-Hellman (SIDH) protocol. Notably, we analyze the defense against timing attacks, simple power analysis, differential power analysis, and fault attacks. Luckily, the SIDH protocol closely resembles its predecessor, the elliptic curve Diffie-Hellman (ECDH) key exchange. As such, much of the extensive literature in side-channel analysis can also apply to SIDH. In particular, we focus on a hardware implementation that features a true random number generator, ALU, and controller. SIDH is composed of two rounds containing a double-point multiplication to generate a secret kernel point and an isogeny over that kernel to arrive at a new elliptic curve isomorphism. To protect against simple power analysis and timing attacks, we recommend a constant-time implementation with Fermat's little theorem inversion. Differential power analysis targets the power output of the SIDH core over many runs. As such, we recommend scaling the base points by secret scalars so that each iteration has a unique power signature. Further, based on recent oracle attacks on SIDH, we cannot recommend the use of static keys from both parties. The goal of this paper is to analyze the tradeoffs in elliptic curve theory to produce a cryptographically and physically secure implementation of SIDH.

Attacks on web services are major concerns and can expose organizations valuable information resources. Despite there are increasing awareness in secure programming, we still find vulnerabilities in web services. To protect deployed web services, it is important to have defense techniques. Signaturebased Intrusion Detection Systems (IDS) have gained popularity to protect applications against attacks. However, signature IDSs have limited number of attack signatures. In this paper, we propose a Genetic Algorithm (GA)-based attack signature generation approach and show its application for web services. GA algorithm has the capability of generating new member from a set of initial population. We leverage this by generating new attack signatures at SOAP message level to overcome the challenge of limited number of attack signatures. The key contributions include defining chromosomes and fitness functions. The initial results show that the GA-based IDS can generate new signatures and complement the limitation of existing web security testing tools. The approach can generate new attack signatures for injection, privilege escalation, denial of service and information leakage.