Biblio

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.

Many innovations in the field of cryptography have been made in recent decades, ensuring the confidentiality of the message's content. However, sometimes it's not enough to secure the message, and communicating parties need to hide the fact of the presence of any communication. This problem is solved by covert channels. A huge number of ideas and implementations of different types of covert channels was proposed ever since the covert channels were mentioned for the first time. The spread of the Internet and networking technologies was the reason for the use of network protocols for the invention of new covert communication methods and has led to the emergence of a new class of threats related to the data leakage via network covert channels. In recent years, web applications, such as web browsers, email clients and web messengers have become indispensable elements in business and everyday life. That's why ubiquitous HTTP messages are so useful as a covert information containers. The use of HTTP for the implementation of covert channels may increase the capacity of covert channels due to HTTP's flexibility and wide distribution as well. We propose a detailed analysis of all known HTTP covert channels and techniques of their detection and capacity limitation.

Many IoT devices are part of fixed critical infrastructure, where the mere act of moving an IoT device may constitute an attack. Moving pressure, chemical and radiation sensors in a factory can have devastating consequences. Relocating roadside speed sensors, or smart meters without knowledge of command and control center can similarly wreck havoc. Consequently, authenticating geolocation of IoT devices is an important problem. Unfortunately, an IoT device itself may be compromised by an adversary. Hence, location information from the IoT device cannot be trusted. Thus, we have to rely on infrastructure to obtain a proximal location. Infrastructure routers may similarly be compromised. Therefore, there must be a way to authenticate trusted routers remotely. Unfortunately, IP packets may be blocked, hijacked or forged by an adversary. Therefore IP packets are not trustworthy either. Thus, we resort to covert channels for authenticating Internet packet routers as an intermediate step towards proximal geolocation of IoT devices. Several techniques have been proposed in the literature to obtain the geolocation of an edge device, but it has been shown that a knowledgeable adversary can circumvent these techniques. In this paper, we survey the state-of-the-art geolocation techniques and corresponding adversarial countermeasures to evade geolocation to justify the use of covert channels on networks. We propose a technique for determining proximal geolocation using covert channel. Challenges and directions for future work are also explored.

Encryption is often not sufficient to secure communication, since it does not hide that communication takes place or who is communicating with whom. Covert channels hide the very existence of communication enabling individuals to communicate secretly. Previous work proposed a covert channel hidden inside multi-player first person shooter online game traffic (FPSCC). FPSCC has a low bit rate, but it is practically impossible to eliminate other than by blocking the overt game trac. This paper shows that with knowledge of the channel’s encoding and using machine learning techniques, FPSCC can be detected with an accuracy of 95% or higher.

Open Science Big Data is emerging as an important area of research and software development. Although there are several high quality frameworks for Big Data, additional capabilities are needed for Open Science Big Data. These include data provenance, citable reusable data, data sources providing links to research literature, relationships to other data and theories, transparent analysis/reproducibility, data privacy, new optimizations/advanced algorithms, data curation, data storage and transfer. An important part of science is explanation of results, ideally leading to theory formation. In this paper, we examine means for supporting the use of theory in big data analytics as well as using big data to assist in theory formation. One approach is to fit data in a way that is compatible with some theory, existing or new. Functional Data Analysis allows precise fitting of data as well as penalties for lack of smoothness or even departure from theoretical expectations. This paper discusses principal differential analysis and related techniques for fitting data where, for example, a time-based process is governed by an ordinary differential equation. Automation in theory formation is also considered. Case studies in the fields of computational economics and finance are considered.

The Sensor Web is evolving into a complex information space, where large volumes of sensor observation data are often consumed by complex applications. Provenance has become an important issue in the Sensor Web, since it allows applications to answer “what”, “when”, “where”, “who”, “why”, and “how” queries related to observations and consumption processes, which helps determine the usability and reliability of data products. This paper investigates characteristics and requirements of provenance in the Sensor Web and proposes an interoperable approach to building a provenance model for the Sensor Web. Our provenance model extends the W3C PROV Data Model with Sensor Web domain vocabularies. It is developed using Semantic Web technologies and thus allows provenance information of sensor observations to be exposed in the Web of Data using the Linked Data approach. A use case illustrates the applicability of the approach.

Compute-intensive simulations typically charge substantial workloads on an online simulation platform backed by limited computing clusters and storage resources. Some (or most) of the simulations initiated by users may accompany input parameters/files that have been already provided by other (or same) users in the past. Unfortunately, these duplicate simulations may aggravate the performance of the platform by drastic consumption of the limited resources shared by a number of users on the platform. To minimize or avoid conducting repeated simulations, we present a novel system, called SUPERMAN (SimUlation ProvEnance Recycling MANager) that can record simulation provenances and recycle the results of past simulations. This system presents a great opportunity to not only reutilize existing results but also perform various analytics helpful for those who are not familiar with the platform. The system also offers interoperability across other systems by collecting the provenances in a standardized format. In our simulated experiments we found that over half of past computing jobs could be answered without actual executions by our system.

Data provenance provides a way for scientists to observe how experimental data originates, conveys process history, and explains influential factors such as experimental rationale and associated environmental factors from system metrics measured at runtime. The US Department of Energy Office of Science Integrated end-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) project has developed a provenance harvester that is capable of collecting observations from file based evidence typically produced by distributed applications. To achieve this, file based evidence is extracted and transformed into an intermediate data format inspired in part by W3C CSV on the Web recommendations, called the Harvester Provenance Application Interface (HAPI) syntax. This syntax provides a general means to pre-stage provenance into messages that are both human readable and capable of being written to a provenance store, Provenance Environment (ProvEn). HAPI is being applied to harvest provenance from climate ensemble runs for Accelerated Climate Modeling for Energy (ACME) project funded under the U.S. Department of Energy's Office of Biological and Environmental Research (BER) Earth System Modeling (ESM) program. ACME informally provides provenance in a native form through configuration files, directory structures, and log files that contain success/failure indicators, code traces, and performance measurements. Because of its generic format, HAPI is also being applied to harvest tabular job management provenance from Belle II DIRAC scheduler relational database tables as well as other scientific applications that log provenance related information.

Provenance describes detailed information about the history of a piece of data, containing the relationships among elements such as users, processes, jobs, and workflows that contribute to the existence of data. Provenance is key to supporting many data management functionalities that are increasingly important in operations such as identifying data sources, parameters, or assumptions behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. Despite its importance, however, provenance support is largely underdeveloped in highly parallel architectures and systems. One major challenge is the demanding requirements of providing provenance service in situ. The need to remain lightweight and to be always on often conflicts with the need to be transparent and offer an accurate catalog of details regarding the applications and systems. To tackle this challenge, we introduce a lightweight provenance service, called LPS, for high-performance computing (HPC) systems. LPS leverages a kernel instrument mechanism to achieve transparency and introduces representative execution and flexible granularity to capture comprehensive provenance with controllable overhead. Extensive evaluations and use cases have confirmed its efficiency and usability. We believe that LPS can be integrated into current and future HPC systems to support a variety of data management needs.

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (textless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- textbartextbar

The Internet of Things (IoT) is envisioned to include billions of pervasive and mission-critical sensors and actuators connected to the (public) Internet. This network of smart devices is expected to generate and have access to vast amounts of information, creating unique opportunities for novel applications but, at the same time raising significant privacy and security concerns that impede its further adoption and development. In this paper, we explore the potential of a blockchain-assisted information distribution system for the IoT. We identify key security requirements of such a system and we discuss how they can be satisfied using blockchains and smart contracts. Furthermore, we present a preliminary design of the system and we identify enabling technologies.

Multi-agent simulations are useful for exploring collective patterns of individual behavior in social, biological, economic, network, and physical systems. However, there is no provenance support for multi-agent models (MAMs) in a distributed setting. To this end, we introduce ProvMASS, a novel approach to capture provenance of MAMs in a distributed memory by combining inter-process identification, lightweight coordination of in-memory provenance storage, and adaptive provenance capture. ProvMASS is built on top of the Multi-Agent Spatial Simulation (MASS) library, a framework that combines multi-agent systems with large-scale fine-grained agent-based models, or MAMs. Unlike other environments supporting MAMs, MASS parallelizes simulations with distributed memory, where agents and spatial data are shared application resources. We evaluate our approach with provenance queries to support three use cases and performance measures. Initial results indicate that our approach can support various provenance queries for MAMs at reasonable performance overhead.

Science is conducted collaboratively, often requiring knowledge sharing about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. In this paper, we present the sciunit, a reusable research object in which aggregated content is recomputable. We describe a Git-like client that efficiently creates, stores, and repeats sciunits. We show through analysis that sciunits repeat computational experiments with minimal storage and processing overhead. Finally, we provide an overview of sharing and reproducible cyberinfrastructure based on sciunits gaining adoption in the domain of geosciences.

Provenance counterfeit and packet loss assaults are measured as threats in the large scale wireless sensor networks which are engaged for diverse application domains. The assortments of information source generate necessitate promising the reliability of information such as only truthful information is measured in the decision procedure. Details about the sensor nodes play an major role in finding trust value of sensor nodes. In this paper, a novel lightweight secure provenance method is initiated for improving the security of provenance data transmission. The anticipated system comprises provenance authentication and renovation at the base station by means of Merkle-Hellman knapsack algorithm based protected provenance encoding in the Bloom filter framework. Side Channel Monitoring (SCM) is exploited for noticing the presence of selfish nodes and packet drop behaviors. This lightweight secure provenance method decreases the energy and bandwidth utilization with well-organized storage and secure data transmission. The investigational outcomes establishes the efficacy and competence of the secure provenance secure system by professionally noticing provenance counterfeit and packet drop assaults which can be seen from the assessment in terms of provenance confirmation failure rate, collection error, packet drop rate, space complexity, energy consumption, true positive rate, false positive rate and packet drop attack detection.

Nowadays wireless networks are fast, becoming more secure than their wired counterparts. Recent technological advances in wireless networking, IC fabrication and sensor technology have lead to the emergence of millimetre scale devices that collectively form a Wireless Sensor Network (WSN) and are radically changing the way in which we sense, process and transport signals of interest. They are increasingly become viable solutions to many challenging problems and will successively be deployed in many areas in the future such as in environmental monitoring, business, and military applications. However, deploying new technology, without security in mind has often proved to be unreasonably dangerous. This also applies to WSNs, especially those used in applications that monitor sensitive information (e.g., health care applications). There have been significant contributions to overcome many weaknesses in sensor networks like coverage problems, lack in power and making best use of limited network bandwidth, however; work in sensor network security is still in its infancy stage. Security in WSNs presents several well-known challenges stemming from all kinds of resource constraints of individual sensors. The problem of securing these networks emerges more and more as a hot topic. Symmetric key cryptography is commonly seen as infeasible and public key cryptography has its own key distribution problem. In contrast to this prejudice, this paper presents a new symmetric encryption standard algorithm which is an extension of the previous work of the authors i.e. UES version-II and III. Roy et al recently developed few efficient encryption methods such as UES version-I, Modified UES-I, UES version-II, UES version-III. The algorithm is named as Ultra Encryption Standard version — IV algorithm. It is a Symmetric key Cryptosystem which includes multiple encryption, bit-wise reshuffling method and bit-wise columnar transposition method. In the present - ork the authors have performed the encryption process at the bit-level to achieve greater strength of encryption. The proposed method i.e. UES-IV can be used to encrypt short message, password or any confidential key.

Internet of things (IoT) is internetworking of various physical devices to provide a range of services and applications. IoT is a rapidly growing field, on an account of this; the security measurements for IoT should be at first concern. In the modern day world, the most emerging cyber-attack threat for IoT is ransomware attack. Ransomware is a kind of malware with the aim of rendering a victim's computer unusable or inaccessible, and then asking the user to pay a ransom to revert the destruction. In this paper we are evaluating ransomware attacks statistics for the past 2 years and the present year to estimate growth rate of the most emerging ransomware families from the last 3 years to evaluate most threatening ransomware attacks for IoT. Growth rate results shows that the number of attacks for Cryptowall and locky ransomware are notably increasing therefore, these ransomware families are potential threat to IoT. Moreover, we present a Cryptowall ransomware attack detection model based on the communication and behavioral study of Cryptowall for IoT environment. The proposed model observes incoming TCP/IP traffic through web proxy server then extracts TCP/IP header and uses command and control (C&C) server black listing to detect ransomware attacks.

Security protection is a concern for the Internet of Things (IoT) which performs data exchange autonomously over the internet for remote monitoring, automation and other applications. IoT implementations has raised concerns over its security and various research has been conducted to find an effective solution for this. Thus, this work focus on the analysis of an asymmetric encryption scheme, AA-Beta (AAβ) on a platform constrained in terms of processor capability, storage and random access Memory (RAM). For this work, the platform focused is ARM Cortex-M7 microcontroller. The encryption and decryption's performance on the embedded microcontroller is realized and time executed is measured. By enabled the I-Cache (Instruction cache) and D-Cache (Data Cache), the performances are 50% faster compared to disabled the D-Cache and I-Cache. The performance is then compared to our previous work on System on Chip (SoC). This is to analyze the gap of the SoC that has utilized the full GNU Multiple Precision Arithmetic Library (GMP) package versus ARM Cortex-M7 that using the mini-gmp package in term of the footprint and the actual performance.

Security and privacy issues of the Internet of Things (IoT in short, hereafter) attracts the hot topic of researches through these years. As the relationship between user and server become more complicated than before, the existing security solutions might not provide exhaustive securities in IoT environment and novel solutions become new research challenges, e.g., the solutions based on symmetric cryptosystems are unsuited to handle with the occasion that decryption is only allowed in specific time range. In this paper, a new scalable one-time file encryption scheme combines reliable cryptographic techniques, which is named OTFEP, is proposed to satisfy specialized security requirements. One of OTFEP's key features is that it offers a mechanism to protect files in the database from arbitrary visiting from system manager or third-party auditors. OTFEP uses two different approaches to deal with relatively small file and stream file. Moreover, OTFEP supports good node scalability and secure key distribution mechanism. Based on its practical security and performance, OTFEP can be considered in specific IoT devices where one-time file encryption is necessary.

The rapid increase of connected devices and the major advances in information and communication technologies have led to great emergence in the Internet of Things (IoT). IoT devices require software adaptation as they are in continuous transition. Multi-agent based solutions offer adaptable composition for IoT systems. Mobile agents can also be used to enable interoperability and global intelligence with smart objects in the Internet of Things. The use of agents carrying personal data and the rapid increasing number of connected IoT devices require the use of security protocols to secure the user data. Elliptic Curve Cryptography (ECC) Algorithm has emerged as an attractive and efficient public-key cryptosystem. We recommend the use of ECC in the proposed Broadcast based Secure Mobile Agent Protocol (BROSMAP) which is one of the most secure protocols that provides confidentiality, authentication, authorization, accountability, integrity and non-repudiation. We provide a methodology to improve BROSMAP to fulfill the needs of Multi-agent based IoT Systems in general. The new BROSMAP performs better than its predecessor and provides the same security requirements. We have formally verified ECC-BROSMAP using Scyther and compared it with BROSMAP in terms of execution time and computational cost. The effect of varying the key size on BROSMAP is also presented. A new ECC-based BROSMAP takes half the time of Rivest-Shamir-Adleman (RSA) 2048 BROSMAP and 4 times better than its equivalent RSA 3072 version. The computational cost was found in favor of ECC-BROSMAP which is more efficient by a factor of 561 as compared to the RSA-BROSMAP.

Security of the information is the main problem in network communications nowadays. There is no algorithm which ensures the one hundred percent reliability of the transmissions. The current society uses the Internet, to exchange information such as from private images to financial data. The cryptographic systems are the mechanisms developed to protect and hide the information from intruders. However, advancing technology is also used by intruders to breach the security of the systems. Hence, every time cryptosystems developed based on complex Mathematics. Elliptic curve cryptography(ECC) is one of the technique in such kind of cryptosystems. Security of the elliptic curves lies in hardness of solving the discrete logarithms problems. In this research, a new cryptographic system is built by using the elliptic curve cryptography based on square matrices to achieve a secure communication between two parties. First, an invertible matrix is chosen arbitrarily in the the field used in the system. Then, by using the Cayley Hamilton theorem, private key matrices are generated for both parties. Next, public key vectors of the both parties are generated by using the private keys of them and arbitrary points of the given elliptic curve. Diffie Hellman protocol is used to authenticate the key exchange. ElGamal plus Menezes Qu Vanstone encryption protocols are used to encrypt the messages. MATLAB R2015a is used to implement and test the proper functioning of the built cryptosystem.

We propose to use a genetic algorithm to evolve novel reconfigurable hardware to implement elliptic curve cryptographic combinational logic circuits. Elliptic curve cryptography offers high security-level with a short key length making it one of the most popular public-key cryptosystems. Furthermore, there are no known sub-exponential algorithms for solving the elliptic curve discrete logarithm problem. These advantages render elliptic curve cryptography attractive for incorporating in many future cryptographic applications and protocols. However, elliptic curve cryptography has proven to be vulnerable to non-invasive side-channel analysis attacks such as timing, power, visible light, electromagnetic, and acoustic analysis attacks. In this paper, we use a genetic algorithm to address this vulnerability by evolving combinational logic circuits that correctly implement elliptic curve cryptographic hardware that is also resistant to simple timing and power analysis attacks. Using a fitness function composed of multiple objectives - maximizing correctness, minimizing propagation delays and minimizing circuit size, we can generate correct combinational logic circuits resistant to non-invasive, side channel attacks. To the best of our knowledge, this is the first work to evolve a cryptography circuit using a genetic algorithm. We implement evolved circuits in hardware on a Xilinx Kintex-7 FPGA. Results reveal that the evolutionary algorithm can successfully generate correct, and side-channel resistant combinational circuits with negligible propagation delay.

In this paper, a mutual authentication protocol based on ECC is designed for RFID systems. This protocol is described in detail and the performance of this protocol is analyzed. The results show that the protocol has many advantages, such as mutual authentication, confidentiality, anonymity, availability, forward security, scalability and so on, which can resist camouflage attacks, tracking attacks, denial of service attacks, system internal attack.

Now-a-days security is a challenging task in different types of networks, such as Mobile Networks, Wireless Sensor Networks (WSN) and Radio Frequency Identifications Systems (RFIS) etc, to overcome these challenges we use sincryption. Signcryption is a new public key cryptographic primitive that performs the functions of digital signature and encryption in single logical step. The main contribution of signcrytion scheme, it is more suitable for low constrained environment. Moreover some signcryption schemes based on RSA, Elliptic Curve (EC) and Hyper Elliptic Curve (HEC). This paper contains a critical review of signcryption schemes based on hyper elliptic curve, since it reduce communication and computational costs for low constrained devices. It also explores advantages and disadvantages of different signcryption schemes based on HEC.

Promoting data sharing between organisations is challenging, without the added concerns over having actions traced. Even with encrypted search capabilities, the entities digital location and downloaded information can be traced, leaking information to the hosting organisation. This is a problem for law enforcement and government agencies, where any information leakage is not acceptable, especially for investigations. Anonymous routing is a technique to stop a host learning which agency is accessing information. Many related works for anonymous routing have been proposed, but are designed for Internet traffic, and are over complicated for internal usage. A streaming design for circuit creation is proposed using elliptic curve cryptography. Allowing for a simple anonymous routing solution, which provides fast performance with source and destination anonymity to other organisations.

Elliptic Curve Cryptography (ECC) is a promising public key cryptography, probably takes the place of RSA. Not only ECC uses less memory, key pair generation and signing are considerably faster, but also ECC's key size is less than that of RSA while it achieves the same level of security. However, the magic behind RSA and its friends can be easily explained, is also widely understood, the foundations of ECC are still a mystery to most of us. This paper's aims are to provide detailed mathematical foundations of ECC, especially, the subgroup and its generator (also called base point) formed by one elliptic curve are researched as highlights, because they are very important for practical ECC implementation. The related algorithms and their implementation details are demonstrated, which is useful for the computing devices with restricted resource, such as embedded systems, mobile devices and IoT devices.