Biblio

Servers in a network are typically assigned a static identity. Static assignment of identities is a cornerstone for adversaries in finding targets. Moving Target Defense (MTD) mutates the environment to increase unpredictability for an attacker. On another side, Software Defined Networks (SDN) facilitate a global view of a network through a central control point. The potential of SDN can not only make network management flexible and convenient, but it can also assist MTD to enhance attack surface obfuscation. In this paper, we propose an effective framework for the prevention, detection, and mitigation of flooding-based Denial of Service (DoS) attacks. Our framework includes a light-weight SDN assisted MTD strategy for network reconnaissance protection and an efficient approach for tackling DoS attacks using Software Defined-Internet Exchange Point (SD-IXP). To assess the effectiveness of the MTD strategy and DoS mitigation scheme, we set two different experiments. Our results confirm the effectiveness of our framework. With the MTD strategy in place, at maximum, barely 16% reconnaissance attempts were successful while the DoS attacks were accurately detected with false alarm rate as low as 7.1%.

HDFS has been widely used for storing massive scale data which is vulnerable to site disaster. The file system backup is an important strategy for data retention. In this paper, we present an efficient, easy- to-use Backup and Disaster Recovery System for HDFS. The system includes a client based on HDFS with additional feature of remote backup, and a remote server with a HDFS cluster to keep the backup data. It supports full backup and regularly incremental backup to the server with very low cost and high throughout. In our experiment, the average speed of backup and recovery is up to 95 MB/s, approaching the theoretical maximum speed of gigabit Ethernet.

Emerging communication technologies in distributed network systems require transfer of biometric digital images with high security. Network security is identified by the changes in system behavior which is either Dynamic or Deterministic. Performance computation is complex in dynamic system where cryptographic techniques are not highly suitable. Chaotic theory solves complex problems of nonlinear deterministic system. Several chaotic methods are combined to get hyper chaotic system for more security. Chaotic theory along with DNA sequence enhances security of biometric image encryption. Implementation proves the encrypted image is highly chaotic and resistant to various attacks.

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 ({\textbackslash}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- {\textbackslash}textbar{\textbackslash}textbar

This paper proposes a novel scheme for RFID anti-counterfeiting by applying bisectional multivariate quadratic equations (BMQE) system into an RF tag data encryption. In the key generation process, arbitrarily choose two matrix sets (denoted as A and B) and a base Rab such that [AB] = λRABT, and generate 2n BMQ polynomials (denoted as p) over finite field Fq. Therefore, (Fq, p) is taken as a public key and (A, B, λ) as a private key. In the encryption process, the EPC code is hashed into a message digest dm. Then dm is padded to d'm which is a non-zero 2n×2n matrix over Fq. With (A, B, λ) and d'm, Sm is formed as an n-vector over F2. Unlike the existing anti-counterfeit scheme, the one we proposed is based on quantum cryptography, thus it is robust enough to resist the existing attacks and has high security.

In this paper, we quantify the effectiveness of third-party tracker blockers on a large scale. First, we analyze the architecture of various state-of-the-art blocking solutions and discuss the advantages and disadvantages of each method. Second, we perform a two-part measurement study on the effectiveness of popular tracker-blocking tools. Our analysis quantifies the protection offered against trackers present on more than 100,000 popular websites and 10,000 popular Android applications. We provide novel insights into the ongoing arms race between trackers and developers of blocking tools as well as which tools achieve the best results under what circumstances. Among others, we discover that rule-based browser extensions outperform learning-based ones, trackers with smaller footprints are more successful at avoiding being blocked, and CDNs pose a major threat towards the future of tracker-blocking tools. Overall, the contributions of this paper advance the field of web privacy by providing not only the largest study to date on the effectiveness of tracker-blocking tools, but also by highlighting the most pressing challenges and privacy issues of third-party tracking.
 

Primary user emulation (PUE) attack is one of the main threats affecting cognitive radio (CR) networks. The PUE can forge the same signal as the real primary user (PU) in order to use the licensed channel and cause deny of service (DoS). Therefore, it is important to locate the position of the PUE in order to stop and avoid any further attack. Several techniques have been proposed for localization, including the received signal strength indication RSSI, Triangulation, and Physical Network Layer Coding. However, the area surrounding the real PU is always affected by uncertainty. This uncertainty can be described as a lost (cost) function and conditional probability to be taken into consideration while proclaiming if a PU/PUE is the real PU or not. In this paper, we proposed a combination of a Bayesian model and trilateration technique. In the first part a trilateration technique is used to have a good approximation of the PUE position making use of the RSSI between the anchor nodes and the PU/PUE. In the second part, a Bayesian decision theory is used to claim the legitimacy of the PU based on the lost function and the conditional probability to help to determine the existence of the PUE attacker in the uncertainty area.

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.

Data randomization or scrambling has been effectively used in various applications to improve the data security. In this paper, we use the idea of data randomization to proactively randomize the spectrum (re)allocation to improve connections' security. As it is well-known that random (re)allocation fragments the spectrum and thus increases blocking in elastic optical networks, we analyze the tradeoff between system performance and security. To this end, in addition to spectrum randomization, we utilize an on-demand defragmentation scheme every time a request is blocked due to the spectrum fragmentation. We model the occupancy pattern of an elastic optical link (EOL) using a multi-class continuous-time Markov chain (CTMC) under the random-fit spectrum allocation method. Numerical results show that although both the blocking and security can be improved for a particular so-called randomization process (RP) arrival rate, while with the increase in RP arrival rate the connections' security improves at the cost of the increase in overall blocking.

With the use of external cloud services such as Google Docs or Evernote in an enterprise setting, the loss of control over sensitive data becomes a major concern for organisations. It is typical for regular users to violate data disclosure policies accidentally, e.g. when sharing text between documents in browser tabs. Our goal is to help such users comply with data disclosure policies: we want to alert them about potentially unauthorised data disclosure from trusted to untrusted cloud services. This is particularly challenging when users can modify data in arbitrary ways, they employ multiple cloud services, and cloud services cannot be changed. To track the propagation of text data robustly across cloud services, we introduce imprecise data flow tracking, which identifies data flows implicitly by detecting and quantifying the similarity between text fragments. To reason about violations of data disclosure policies, we describe a new text disclosure model that, based on similarity, associates text fragments in web browsers with security tags and identifies unauthorised data flows to untrusted services. We demonstrate the applicability of imprecise data tracking through BrowserFlow, a browser-based middleware that alerts users when they expose potentially sensitive text to an untrusted cloud service. Our experiments show that BrowserFlow can robustly track data flows and manage security tags for documents with no noticeable performance impact.

Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.

A fresh look at the way secure communications is currently being done has been undertaken as a consequence of the large hacking's that have taken place recently. A plausible option maybe a return to the future via Morse code using how a quantum bit (Qubit) reacts when entangled to suggest a cypher. This quantum cyphers uses multiple properties of unique entities that have many random radicals which makes hacking more difficult that traditional 'Rivest-Shamir-Adleman' (RSA), 'Digital Signature Algorithm' (DSA) or 'Elliptic Curve Digital Signature Algorithm' (ECDSA). Additional security is likely by Backchannelling (slipstreaming) Quantum Morse code (Q-Morse) keys composed of living and non-living entities. This means Blockchain ledger history (forwards-backwards) is audited during an active session. Verification keys are Backchannelling (slipstreaming) during the session (e.g. train driver must incrementally activate a switch otherwise the train stops) using predicted-expected sender-receiver properties as well as their past history of disconformities to random radicals encountered. In summary, Quantum Morse code (Q-Morse) plausibly is the enabler to additional security by Backchannelling (slipstreaming) during a communications session.

Cloud computing is revolutionizing many IT ecosystems through offering scalable computing resources that are easy to configure, use and inter-connect. However, this model has always been viewed with some suspicion as it raises a wide range of security and privacy issues that need to be negotiated. This research focuses on the construction of a trust layer in cloud computing to build a trust relationship between cloud service providers and cloud users. In particular, we address the rise of container-based virtualisation has a weak isolation compared to traditional VMs because of the shared use of the OS kernel and system components. Therefore, we will build a trust layer to solve the issues of weaker isolation whilst maintaining the performance and scalability of the approach. This paper has two objectives. Firstly, we propose a security system to protect containers from other guests through the addition of a Role-based Access Control (RBAC) model and the provision of strict data protection and security. Secondly, we provide a stress test using isolation benchmarking tools to evaluate the isolation in containers in term of performance.

Bitcoin, a decentralized cryptographic currency that has experienced proliferating popularity over the past few years, is the common denominator in a wide variety of cybercrime. We perform a measurement analysis of CryptoLocker, a family of ransomware that encrypts a victim's files until a ransom is paid, within the Bitcoin ecosystem from September 5, 2013 through January 31, 2014. Using information collected from online fora, such as reddit and BitcoinTalk, as an initial starting point, we generate a cluster of 968 Bitcoin addresses belonging to CryptoLocker. We provide a lower bound for CryptoLocker's economy in Bitcoin and identify 795 ransom payments totalling 1,128.40 BTC (\$310,472.38), but show that the proceeds could have been worth upwards of \$1.1 million at peak valuation. By analyzing ransom payment timestamps both longitudinally across CryptoLocker's operating period and transversely across times of day, we detect changes in distributions and form conjectures on CryptoLocker that corroborate information from previous efforts. Additionally, we construct a network topology to detail CryptoLocker's financial infrastructure and obtain auxiliary information on the CryptoLocker operation. Most notably, we find evidence that suggests connections to popular Bitcoin services, such as Bitcoin Fog and BTC-e, and subtle links to other cybercrimes surrounding Bitcoin, such as the Sheep Marketplace scam of 2013. We use our study to underscore the value of measurement analyses and threat intelligence in understanding the erratic cybercrime landscape.

The secure two-party computation (S2PC) protocols SHADE and GSHADE have been introduced by Bringer et al. in the last two years. The protocol GSHADE permits to compute different distances (Hamming, Euclidean, Mahalanobis) quite efficiently and is one of the most efficient compared to other S2PC methods. Thus this protocol can be used to efficiently compute one-to-many identification for several biometrics data (iris, face, fingerprint). In this paper, we introduce two extensions of GSHADE. The first one enables us to evaluate new multiplicative functions. This way, we show how to apply GSHADE to a classical machine learning algorithm. The second one is a new proposal to secure GSHADE against malicious adversaries following the recent dual execution and cut-and-choose strategies. The additional cost is very small. By preserving the GSHADE's structure, our extensions are very efficient compared to other S2PC methods.

Web Service Architecture gives a compatible and scalable structure for web service interactions with performance, responsiveness, reliability and security to make a quality of software design. Systematic quantitative approaches have been discussed for designing and developing software systems that meet performance objectives. Many companies have successfully applied these techniques in different applications to achieve better performance in terms of financial, customer satisfaction, and other benefits. This paper describes the architecture, design, implementation, integration testing, performance and maintenance of new applications. The most successful best practices used in world class organizations are discussed. This will help the application, component, and software system designers to develop web applications and fine tune the existing methods in line with the best practices. In business process automation, many standard practices and technologies have been used to model and execute business processes. The emerging technology is web applications technology which provides a great flexibility for development of interoperable environment services. In this paper we propose a Case study of Automatic Gas Booking system, a business process development strategy and best practices used in development of software components used in web applications. The classification of QWS dataset with 2507 records, service invocations, integration and security for web applications have been discussed.

We study the strategic considerations of miners participating in the bitcoin's protocol. We formulate and study the stochastic game that underlies these strategic considerations. The miners collectively build a tree of blocks, and they are paid when they create a node (mine a block) which will end up in the path of the tree that is adopted by all. Since the miners can hide newly mined nodes, they play a game with incomplete information. Here we consider two simplified forms of this game in which the miners have complete information. In the simplest game the miners release every mined block immediately, but are strategic on which blocks to mine. In the second more complicated game, when a block is mined it is announced immediately, but it may not be released so that other miners cannot continue mining from it. A miner not only decides which blocks to mine, but also when to release blocks to other miners. In both games, we show that when the computational power of each miner is relatively small, their best response matches the expected behavior of the bitcoin designer. However, when the computational power of a miner is large, he deviates from the expected behavior, and other Nash equilibria arise.

Whether it is for conditional statement, constant, opaque predicate or equation obfuscation, Mixed Boolean Arithmetics (MBA) technique is a powerful tool providing concrete ways to achieve obfuscation. Recent papers ([22,1]) presented ways to mix such tools with permutation polynomials modulo 2n in order to make them more resilient to SMT solvers. However, because of limitations regarding the inversion of such permutations, the set of permutation polynomials presented suffer some restrictions. Such restrictions bring several methods of arithmetic simplification, decreasing their effectiveness at hiding information. In this work, we present general methods for permutation polynomials inversion. Those methods allow us to remove some of the restrictions presented in the literature, making simplification methods less effective. We discuss complexity and limits of these methods, and conclude that not only current simplification methods may not be as effective as we thought, but they are still many uses of polynomial permutations in obfuscation that are yet to be explored.

Modern web browsers are incredibly complex, with millions of lines of code and over one thousand JavaScript functions and properties available to website authors. This work investigates how these browser features are used on the modern, open web. We find that JavaScript features differ wildly in popularity, with over 50% of provided features never used on the web's 10,000 most popular sites according to Alexa We also look at how popular ad and tracking blockers change the features used by sites, and identify a set of approximately 10% of features that are disproportionately blocked (prevented from executing by these extensions at least 90% of the time they are used). We additionally find that in the presence of these blockers, over 83% of available features are executed on less than 1% of the most popular 10,000 websites. We further measure other aspects of browser feature usage on the web, including how many features websites use, how the length of time a browser feature has been in the browser relates to its usage on the web, and how many security vulnerabilities have been associated with related browser features.

We develop a novel model-based hardware-in-the-loop (HIL) framework for optimising energy consumption of embedded software controllers. Controller and plant models are specified as networks of parameterised timed input/output automata and translated into executable code. The controller is encoded into the target embedded hardware, which is connected to a power monitor and interacts with the simulation of the plant model. The framework then generates a power consumption model that maps controller transitions to distributions over power measurements, and is used to optimise the timing parameters of the controller, without compromising a given safety requirement. The novelty of our approach is that we measure the real power consumption of the controller and use thus obtained data for energy optimisation. We employ timed Petri nets as an intermediate representation of the executable specification, which facilitates efficient code generation and fast simulations. Our framework uniquely combines the advantages of rigorous specifications with accurate power measurements and methods for online model estimation, thus enabling automated design of correct and energy-efficient controllers.

Cyber-physical systems (CPS) are often network integrated to enable remote management, monitoring, and reporting. Such integration has made them vulnerable to cyber attacks originating from an untrusted network (e.g., the internet). Once an attacker breaches the network security, he could corrupt operations of the system in question, which may in turn lead to catastrophes. Hence there is a critical need to detect intrusions into mission-critical CPS. Signature based detection may not work well for CPS, whose complexity may preclude any succinct signatures that we will need. Specification based detection requires accurate definitions of system behaviour that similarly can be hard to obtain, due to the CPS's complexity and dynamics, as well as inaccuracies and incompleteness of design documents or operation manuals. Formal models, to be tractable, are often oversimplified, in which case they will not support effective detection. In this paper, we study a behaviour-based machine learning (ML) approach for the intrusion detection. Whereas prior unsupervised ML methods have suffered from high missed detection or false-positive rates, we use a high-fidelity CPS testbed, which replicates all main physical and control components of a modern water treatment facility, to generate systematic training data for a supervised method. The method does not only detect the occurrence of a cyber attack at the physical process layer, but it also identifies the specific type of the attack. Its detection is fast and robust to noise. Furthermore, its adaptive system model can learn quickly to match dynamics of the CPS and its operating environment. It exhibits a low false positive (FP) rate, yet high precision and recall.

Explicit non-linear transformations of existing steganalysis features are shown to boost their ability to detect steganography in combination with existing simple classifiers, such as the FLD-ensemble. The non-linear transformations are learned from a small number of cover features using Nyström approximation on pilot vectors obtained with kernelized PCA. The best performance is achieved with the exponential form of the Hellinger kernel, which improves the detection accuracy by up to 2-3% for spatial-domain contentadaptive steganography. Since the non-linear map depends only on the cover source and its learning has a low computational complexity, the proposed approach is a practical and low cost method for boosting the accuracy of existing detectors built as binary classifiers. The map can also be used to significantly reduce the feature dimensionality (by up to factor of ten) without performance loss with respect to the non-transformed features.

WSN is a collection of tiny nodes that used to absorb the natural phenomenon from the operational environment and send it to the control station to extract the useful information. In most of the Existing Systems, the assumption is that the operational environment of the sensor nodes deployed is trustworthy and secure by means of some cryptographic operations and existing trust model. But in the reality it is not the case. Most of the existing systems lacks in providing reliable security to the sensor nodes. To overcome the above problem, in this paper, Beta Reputation and Direct Trust Model (BRDT) is the combination of Direct Trust and Beta Reputation Trust for secure communication in Wireless Sensor Networks. This model is used to perform secure routing in WSN. Overall, the method provides an efficient trust in WSN compared to existing methods.

We propose a new voting scheme, BeleniosRF, that offers both receipt-freeness and end-to-end verifiability. It is receipt-free in a strong sense, meaning that even dishonest voters cannot prove how they voted. We provide a game-based definition of receipt-freeness for voting protocols with non-interactive ballot casting, which we name strong receipt-freeness (sRF). To our knowledge, sRF is the first game-based definition of receipt-freeness in the literature, and it has the merit of being particularly concise and simple. Built upon the Helios protocol, BeleniosRF inherits its simplicity and does not require any anti-coercion strategy from the voters. We implement BeleniosRF and show its feasibility on a number of platforms, including desktop computers and smartphones.

Conventional security methods like password and ID card methods are now rapidly replacing by biometrics for identification of a person. Biometrics uses physiological or behavioral characteristics of a person. Usage of biometric raises critical privacy and security concerns that, due to the noisy nature of biometrics, cannot be addressed using standard cryptographic methods. The loss of an enrollment biometric to an attacker is a security hazard because it may allow the attacker to get an unauthorized access to the system. Biometric template can be stolen and intruder can get access of biometric system using fake input. Hence, it becomes essential to design biometric system with secure template or if the biometric template in an application is compromised, the biometric signal itself is not lost forever and a new biometric template can be issued. One way is to combine the biometrics and cryptography or use transformed data instead of original biometric template. But traditional cryptography methods are not useful in biometrics because of intra-class variation. Biometric cryptosystem can apply fuzzy vault, fuzzy commitment, helper data and secure sketch, whereas, cancelable biometrics uses distorting transforms, Bio-Hashing, and Bio-Encoding techniques. In this paper, biometric cryptosystem is presented with fuzzy vault and fuzzy commitment techniques for fingerprint recognition system.