Biblio

Voice-based input is usually used as the primary input method for augmented reality (AR) headsets due to immersive AR experience and good recognition performance. However, recent researches have shown that an attacker can inject inaudible voice commands to the devices that lack voice verification. Even if we secure voice input with voice verification techniques, an attacker can easily steal the victim's voice using low-cast handy recorders and replay it to voice-based applications. To defend against voice-spoofing attacks, AR headsets should be able to determine whether the voice is from the person who is using the AR headsets. Existing voice-spoofing defense systems are designed for smartphone platforms. Due to the special locations of microphones and loudspeakers on AR headsets, existing solutions are hard to be implemented on AR headsets. To address this challenge, in this paper, we propose a voice-spoofing defense system for AR headsets by leveraging both the internal body propagation and the air propagation of human voices. Experimental results show that our system can successfully accept normal users with average accuracy of 97% and defend against two types of attacks with average accuracy of at least 98%.

Energy Distribution Grids are considered critical infrastructure, hence the Distribution System Operators (DSOs) have developed sophisticated engineering practices to improve their resilience. Over the last years, due to the "Smart Grid" evolution, this infrastructure has become a distributed system where prosumers (the consumers who produce and share surplus energy through the grid) can plug in distributed energy resources (DERs) and manage a bi-directional flow of data and power enabled by an advanced IT and control infrastructure. This introduces new challenges, as the prosumers possess neither the skills nor the knowledge to assess the risk or secure the environment from cyber-threats. We propose a simple and usable approach based on the Reference Model of Information Assurance & Security (RMIAS), to support the prosumers in the selection of cybesecurity measures. The purpose is to reduce the risk of being directly targeted and to establish collective responsibility among prosumers as grid gatekeepers. The framework moves from a simple risk analysis based on security goals to providing guidelines for the users for adoption of adequate security countermeasures. One of the greatest advantages of the approach is that it does not constrain the user to a specific threat model.

Voice communication is an important need in daily activities whether delivered with or without technology. Telecommunication technology has accommodated this need by providing a wide range of infrastructure, including large varieties of devices used as intermediary and end devices. One of the cellular technologies that is very widely used by the public is GSM (Global System for Mobile), while in the military, trunked radio is still popular. However, the security systems of GSM and trunked radio have limitations. Therefore, this paper proposes a platform to secure voice data over wireless mobile communication by providing end-to-end encryption. This platform is robust to noise, real-time and remains secure. The proposed encryption utilizes multicircular permutations rotated by expanded keys as dynamic keys to scramble the data. We carry out simulations and testbed implementation to prove that application of the proposed method is feasible.

Bluetooth Low Energy is a fast growing protocol which has gained wide acceptance during last years. Key features for this growth are its high data rate and its ultra low energy consumption, making it the perfect candidate for piconets. However, the lack of expandability without serious impact on its energy consumption profile, prevents its adoption on more complex systems which depend on long network lifetime. Thus, a lot of academic research has been focused on the solution of BLE expandability problem and BLE mesh has been introduced on the latest Bluetooth version. In our point of view, most of the related work cannot be efficiently implemented in networks which are mostly comprised of constrained-resource nodes. Thus, we propose a new energy efficient tree algorithm for BLE static constrained-resources networks, which achieves a longer network lifetime by both reducing as much as possible the number of needed connection events and balancing the energy dissipation in the network.

This article presents a consensus based distributed energy management optimization algorithm for an islanded microgrid. With the rapid development of renewable energy and distributed generation (DG) energy management is becoming more and more distributed. To solve this problem a multi-agent system based distributed solution is designed in this work which uses lambda-iteration method to solve optimization problem. Moreover, the algorithm is fully distributed and transmission losses are also considered in the modeling process which enhanced the practicality of proposed work. Simulations are performed for different cases on 8-bus microgrid to show the effectiveness of algorithm. Moreover, a scalability test is performed at the end to further justify the expandability performance of algorithm for more advanced networks.

Despite the wide of range of research and technologies that deal with the problem of routing in computer networks, there remains a gap between the level of network hardware administration and the level of business requirements and constraints. Not much has been accomplished in literature in order to have a direct enforcement of such requirements on the network. This paper presents a new solution in specifying and directly enforcing security policies to control the routing configuration in a software-defined network by using Row-Level Security checks which enable fine-grained security policies on individual rows in database tables. We show, as a first step, how a specific class of such policies, namely multilevel security policies, can be enforced on a database-defined network, which presents an abstraction of a network's configuration as a set of database tables. We show that such policies can be used to control the flow of data in the network either in an upward or downward manner.

The data collected by IoT devices is of great value, which makes people urgently need a secure device key management strategy to protect their data. Existing works introduce the blockchain technology to transfer the responsibility of key management from the trusted center in the traditional key management strategy to the devices, thus eliminating the trust crisis caused by excessive dependence on third parties. However, the lightweight implementation of IoT devices limits the ability to resist side channel attacks, causing the private key to be exposed and subject to masquerading attacks. Accordingly, we strengthen the original blockchain based key management scheme to defend against key exposure attack. On the one hand, we introduce two hash functions to bind transactions in the blockchain to legitimate users. On the other hand, we design a secure key exchange protocol for identifying and exchanging access keys between legitimate users. Security analysis and performance show that the proposed scheme improves the robustness of the network with small storage and communication overhead increments.

As Web traffics is increasing on the Internet, caching solutions for Web systems are becoming more important since they can greatly expand system scalability. An important part of a caching solution is cache replacement policy, which is responsible for selecting victim items that should be removed in order to make space for new objects. Typical replacement policies used in practice only take advantage of temporal reference locality by removing the least recently/frequently requested items from the cache. Although those policies work well in memory or filesystem cache, they are inefficient for Web systems since they do not exploit semantic relationship between Web items. This paper presents a semantic-aware caching policy that can be used in Web systems to enhance scalability. The proposed caching mechanism defines semantic distance from a web page to a set of pivot pages and use the semantic distances as a metric for choosing victims. Also, it use a function-based metric that combines access frequency and cache item size for tie-breaking. Our simulations show that out enhancements outperform traditional methods in terms of hit rate, which can be useful for websites with many small and similar-in-size web objects.

Network steganography is a branch of steganography that hides information through packet header manipulation and uses protocols as carriers to hide secret information. Many techniques were already developed using the Transmission Control Protocol (TCP) headers. Among the schemes in hiding information in the TCP header, the Initial Sequence Number (ISN) field is the most difficult to be detected since this field can have arbitrary values within the requirements of the standard. In this paper, a more undetectable scheme is proposed by increasing the complexity of hiding data in the TCP ISN using dynamic identifiers. The experimental results have shown that using Bayes Net, the proposed scheme outperforms the existing scheme with a low detection accuracy of 0.52%.

This study aims to enhance the security of Moodle system environment during the Execution of online exams, Taking into consideration the most common problems facing online exams and working to solve them. This was handled by improving the security performance of Moodle Quiz tool, which is one of the most important tools in the learning Management system as general and in Moodle system as well. In this paper we include two enhancement aspects: The first aspect is solving the problem of losing the answers during sudden short disconnection of the network because of the server crash or any other reasons, the second aspect is Increasing the level of confidentiality of e-Quiz by preventing accessing the Quiz from more than one computer or browser at the same time. In order to verify the efficiency of the new quiz tool features, the upgraded tool have been tested using an experimental test Moodle site.

Optimal placement of new sensors is of great importance to enhancing distribution system monitoring and resiliency. Utilities are in need of a platform for an optimal sensor placement strategy other than the traditional experience-based strategy. In this paper, a sensor placement optimization tool (SPOT) is developed. It contains two selected modules based on industry priority: distribution system state estimation (DSE) and recloser placement (RP). The DSE module incorporates three-phase system functionality to reflect practical distribution systems with asymmetrical topology and unbalanced loading. In the RP module, the impact of microgrids is modeled. SPOT is timely since it can assist utilities in developing their own optimal sensor allocation strategies.

Smart Grid cyber-security sounds to be a critical issue, because of widespread development of information technology. To achieve secure and reliable operation, the complexity of human automation interaction (HAI) necessitates more sophisticated and intelligent methodologies. In this paper, an adaptive autonomy fuzzy expert system is developed using gradient descent algorithm to determine the Level of Automation (LOA), based on the changing of Performance Shaping Factors (PSF). These PSFs indicate the effects of environmental conditions on the performance of HAI. The major advantage of this method is that the fuzzy rule or membership function can be learnt without changing the form of the fuzzy rule in conventional fuzzy control. Because of data shortage, Leave-One-Out Cross-Validation (LOOCV) technique is applied for assessing how the results of proposed system generalizes to the new contingency situations. The expert system database is extracted from superior experts' judgments. In order to regard the importance of each PSF, weighted rules are also considered. In addition, some new environmental conditions are introduced that has not been seen before. Nine scenarios are discussed to reveal the performance of the proposed system. Results confirm that the presented fuzzy expert system can effectively calculates the proper LOA even in the new contingency situations.

Foreign adversaries are increasingly creating and exploiting vulnerabilities in information and communications technology and services, which store and communicate vast amounts of sensitive information, facilitate the digital economy, and support critical infrastructure and vital emergency services, in order to commit malicious cyber-enabled actions, including eco- nomic and industrial espionage against the United States and its people.

Opportunities arising from IoT-enabled applications are significant, but market growth is inhibited by concerns over security and complexity. To address these issues, we propose the ERAMIS methodology, which is based on instantiation of a reference architecture that captures common design features, embodies best practice, incorporates good security properties by design, and makes explicit provision for operational security services and processes.

The mass integration and deployment of intelligent technologies within critical commercial, industrial and public environments have a significant impact on business operations and society as a whole. Though integration of these critical intelligent technologies pose serious embedded security challenges for technology manufacturers which are required to be systematically approached, in-line with international security regulations.This paper establish security foundation for such intelligent technologies by deriving embedded security requirements to realise the core security functions laid out by international security authorities, and proposing microarchitectural characteristics to establish cyber resilience in embedded systems. To bridge the research gap between embedded and operational security domains, a detailed review of existing embedded security methods, microarchitectures and design practises is presented. The existing embedded security methods have been found ad-hoc, passive and strongly rely on building and maintaining trust. To the best of our knowledge to date, no existing embedded security microarchitecture or defence mechanism provides continuity of data stream or security once trust has broken. This functionality is critical for embedded technologies deployed in critical infrastructure to enhance and maintain security, and to gain evidence of the security breach to effectively evaluate, improve and deploy active response and mitigation strategies. To this end, the paper proposes three microarchitectural characteristics that shall be designed and integrated into embedded architectures to establish, maintain and improve cyber resilience in embedded systems for next-generation critical infrastructure.

Malware authors are known to reuse existing code, this development process results in software evolution and a sequence of versions of a malware family containing functions that show a divergence from the initial version. This paper proposes the term evolved similarity to account for this gradual divergence of similarity across the version history of a malware family. While existing techniques are able to match functions in different versions of malware, these techniques work best when the version changes are relatively small. This paper introduces the concept of evolved similarity and presents automated Evolved Similarity Techniques (EST). EST differs from existing malware function similarity techniques by focusing on the identification of significantly modified functions in adjacent malware versions and may also be used to identify function similarity in malware samples that differ by several versions. The challenge in identifying evolved malware function pairs lies in identifying features that are relatively invariant across evolved code. The research in this paper makes use of the function call graph to establish these features and then demonstrates the use of these techniques using Zeus malware.

This paper presents an experimental analysis of current Distributed Denial of Service attacks. Our analysis is based on real data collected by a honeynet system that was installed on an ISP edge router, for a four-month period. In the examined scenario, we identify and analyze malicious activities based on packets captured and analyzed by a network protocol sniffer and signature-based attack analysis tools. Our analysis shows that IoT-based DDoS attacks are one of the latest and most proliferating attack trends in network security. Based on the analysis of the attacks, we describe some mitigation techniques that can be applied at the providers' network to mitigate the trending attack vectors.

The paper discusses the architectural, algorithmic and computing aspects of creating and operating a class of expert system for managing technological safety of an enterprise, in conditions of a large flow of diagnostic variables. The algorithm for finding a faulty technological chain uses expert information, formed as a set of evidence on the influence of diagnostic variables on the correctness of the technological process. Using the Dempster-Schafer trust function allows determining the overall probability measure on subsets of faulty process chains. To combine different evidence, the orthogonal sums of the base probabilities determined for each evidence are calculated. The procedure described above is converted into the rules of the knowledge base production. The description of the developed prototype of the expert system, its architecture, algorithmic and software is given. The functionality of the expert system and configuration tools for a specific type of production are under discussion.

Named Data Networking (NDN) intrinsically supports in-network caching and multipath forwarding. The two salient features offer the potential to simultaneously transmit content segments that comprise the requested content from original content publishers and in-network caches. However, due to the complexity of maintaining the reachability information of off-path cached content at the fine-grained packet level of granularity, the multipath forwarding and off-path cached copies are significantly underutilized in NDN so far. Network coding enabled NDN, referred to as NC-NDN, was proposed to effectively utilize multiple on-path routes to transmit content, but off-path cached copies are still unexploited. This work enhances NC-NDN with an On-demand Off-path Cache Exploration based Multipath Forwarding strategy, dubbed as O2CEMF, to take full advantage of the multipath forwarding to efficiently utilize off-path cached content. In O2CEMF, each network node reactively explores the reachability information of nearby off-path cached content when consumers begin to request a generation of content, and maintains the reachability at the coarse-grained generation level of granularity instead. Then the consumers simultaneously retrieve content from the original content publisher(s) and the explored capable off-path caches. Our experimental studies validate that this strategy improves the content delivery performance efficiently as compared to that in the present NC-NDN.

Software Defined Network (SDN) is a revolutionary networking paradigm which provides the flexibility of programming the network interface as per the need and demand of the user. Software Defined Network (SDN) is independent of vendor specific hardware or protocols and offers the easy extensions in the networking. A customized network as per on user demand facilitates communication control via a single entity i.e. SDN controller. Due to this SDN Controller has become more vulnerable to SDN security attacks and more specifically a single point of failure. It is worth noticing that vulnerabilities were identified because of customized applications which are semi-independent of underlying network infrastructure. No doubt, SDN has provided numerous benefits like breaking vendor lock-ins, reducing overhead cost, easy innovations, increasing programmability among devices, introducing new features and so on. But security of SDN cannot be neglected and it has become a major topic of debate. The communication channel used in SDN is OpenFlow which has made TLS implementation an optional approach in SDN. TLS adoption is important and still vulnerable. This paper focuses on making SDN OpenFlow communication more secure by following extended TLS support and defensive algorithm.

The new instrumentation and control (I&C) systems of the nuclear power plants (NPPs) improve the ability to operate the plant enhance the safety and performance of the NPP. However, they bring a new type of threat to the NPP's industry-cyber threat. The early fault diagnostic system (EDS) is one of the decision support systems that might be used online during the operation stage. The EDS aim is to prevent the incident/accident evolution by a timely troubleshooting process during any plant operational modes. It means that any significative deviation of plant parameters from normal values is pointed-out to plant operators well before reaching any undesired threshold potentially leading to a prohibited plant state, together with the cause that has generated the deviation. The paper lists the key benefits using the EDS to counter the cyber threat and proposes the framework for cybersecurity assessment using EDS during the operational stage.

Energy efficiency and security is a critical requirement for computing at edge nodes. Unrolled architectures for lightweight cryptographic algorithms have been shown to be energy-efficient, providing higher performance while meeting resource constraints. Hardware implementations of unrolled datapaths have also been shown to be resistant to side channel analysis (SCA) attacks due to a reduction in signal-to-noise ratio (SNR) and an increased complexity in the leakage model. This paper demonstrates optimal leakage models and an improved CFA attack which makes it feasible to extract first-order side-channel leakages from combinational logic in the initial rounds of unrolled datapaths. Several leakage models, targeting initial rounds, are explored and 1-bit hamming weight (HW) based leakage model is shown to be an optimal choice. Additionally, multi-band narrow bandpass filtering techniques in conjunction with correlation frequency analysis (CFA) is demonstrated to improve SNR by up to 4×, attributed to the removal of the misalignment effect in combinational logics and signal isolation. The improved CFA attack is performed on side channel signatures acquired for 7-round unrolled SIMON datapaths, implemented on Sakura-G (XILINX spartan 6, 45nm) based FPGA platform and a 24× reduction in minimum-traces-to-disclose (MTD) for revealing 80% of the key bits is demonstrated with respect to conventional time domain correlation power analysis (CPA). Finally, the proposed method is successfully applied to a fully-unrolled datapath for PRINCE and a parallel round-based datapath for Advanced Encryption Standard (AES) algorithm to demonstrate its general applicability.

Ze the quality of channels into either completely noisy or noieseless channels. This paper presents extrinsic information transfer (EXIT) analysis for iterative decoding of Polar codes to reveal the mechanism of channel transformation. The purpose of understanding the transformation process are to comprehend the placement process of information bit and frozen bit and to comprehend the security standard of Polar codes. Mutual information derived based on the concept of EXIT chart for check nodes and variable nodes of low density parity check (LDPC) codes and applied to Polar codes. This paper explores the quality of the polarized channels in finite blocklength. The finite block-length is of our interest since in the fifth telecommunications generation (5G) the block length is limited. This paper reveals the EXIT curve changes of Polar codes and explores the polarization characteristics, thus, high value of mutual informations for frozen bit are needed to be detectable. If it is the other way, the error correction capability of Polar codes would be drastically decreases. These results are expected to be a reference for developments of Polar codes for 5G technologies and beyond.

In order to solve the problem that there is no effective means to find the optimal number of hidden nodes of single-hidden-layer feedforward neural network, in this paper, a method will be introduced to solve it effectively by using singular value decomposition. First, the training data need to be normalized strictly by attribute-based data normalization and sample-based data normalization. Then, the normalized data is decomposed based on the singular value decomposition, and the number of hidden nodes is determined according to main eigenvalues. The experimental results of MNIST data set and APS data set show that the feedforward neural network can attain satisfactory performance in the classification task.

Causal Models are increasingly suggested as a mean to reason about the behavior of cyber-physical systems in socio-technical contexts. They allow us to analyze courses of events and reason about possible alternatives. Until now, however, such reasoning is confined to the technical domain and limited to single systems or at most groups of systems. The humans that are an integral part of any such socio-technical system are usually ignored or dealt with by “expert judgment”. We show how a technical causal model can be extended with models of human behavior to cover the complexity and interplay between humans and technical systems. This integrated socio-technical causal model can then be used to reason not only about actions and decisions taken by the machine, but also about those taken by humans interacting with the system. In this paper we demonstrate the feasibility of merging causal models about machines with causal models about humans and illustrate the usefulness of this approach with a highly automated vehicle example.