Biblio

Here, we propose a simple design approach based on metal-masked titanium dioxide nanopillars, which can realize strong Mie resonance in metasurfaces and enables light confinement within itself over the range of visible wavelengths. By selecting the appropriate period and diameter of individual titanium dioxide nanopillars, the coincidence of resonance peak positions derived from excited electric and magnetic dipoles can be achived. And the optical properties in this design have been investigated with the Finite-Difference Time-Domain(FDTD) solutions.

Modern cryptographic ransomware pose a severe threat to the security of individuals and organizations. Targeted ransomware attacks exhibit refinement in attack vectors owing to the manual reconnaissance performed by the perpetrators for infiltration. The result is an impenetrable lock on multiple hosts within the organization which allows the cybercriminals to demand hefty ransoms. Reliance on prevention strategies is not sufficient and a firm comprehension of implementation details is necessary to develop effective solutions that can thwart ransomware after preventative strategies have failed. Ransomware depend heavily on the abstraction offered by Windows APIs. This paper provides a detailed review of the common API calls in ransomware. We propose four classes of API calls that can be used for profiling and generating effective API call relationships useful in automated detection. Finally, we present counts and visualizations pertaining to API call extraction from real-world ransomware that demonstrate that even advanced variants from different families carry similarities in implementation.

Steganography is the concept to conceal information and the data by embedding it as secret data into various digital medium in order to achieve higher security. To achieve this, many steganographic algorithms are already proposed. The ability of human eyes as well as invisibility remain the most important and prominent factor for the security and protection. The most commonly used security measure of data hiding within imagesYet it is ineffective against Steganalysis and lacks proper verifications. Thus the proposed system of Image Steganography using PVD (Pixel Value Differentiating) proves to be a better choice. It compresses and embeds data in images at the pixel value difference calculated between two consecutive pixels. To increase the security, another technique called Edge Detection is used along with PVD to embed data at the edges. Edge Detection techniques like Canny algorithm are used to find the edges in an image horizontally as well as vertically. The edge pixels in an image can be used to handle more bits of messages, because more pixel value shifts can be handled by the image edge area.

Owing to the advancements in communication media and devices all over the globe, there has arisen a dire need for to limit the alarming number of attacks targeting these and to enhance their security. Multiple techniques have been incorporated in different researches and various protocols and schemes have been put forward to cater security issues of session initiation protocol (SIP). In 2008, Qiu et al. presented a proposal for SIP authentication which while effective than many existing schemes, was still found vulnerable to many security attacks. To overcome those issues, Zhang et al. proposed an authentication protocol. This paper presents the analysis of Zhang et al. authentication scheme and concludes that their proposed scheme is susceptible to user traceablity. It also presents an improved SIP authentication scheme that eliminates the possibility of traceability of user's activities. The proposed scheme is also verified by contemporary verification tool, ProVerif and it is found to be more secure, efficient and practical than many similar SIP authetication scheme.

For the task of natural language processing (NLP), Word embedding technology has a certain impact on the accuracy of deep neural network algorithms. Considering that the current word embedding method cannot realize the coexistence of words and phrases in the same vector space. Therefore, we propose an enhanced word embedding (EWE) method. Before completing the word embedding, this method introduces a unique sentence reorganization technology to rewrite all the sentences in the original training corpus. Then, all the original corpus and the reorganized corpus are merged together as the training corpus of the distributed word embedding model, so as to realize the coexistence problem of words and phrases in the same vector space. We carried out experiment to demonstrate the effectiveness of the EWE algorithm on three classic benchmark datasets. The results show that the EWE method can significantly improve the classification performance of the CNN model.

Physical interaction of robots with their environment is a challenging problem because of the exchanged forces. Hybrid position/force control schemes often exhibit problems during the contact phase, whereas impedance control appears to be more simple and reliable, especially when impedance is shaped to be energetically passive. Even if recent technologies enable shaping the impedance of a robot, how best to plan impedance parameters for task execution remains an open question. In this paper we present an optimization-based approach to plan not only the robot motion but also its desired end-effector mechanical impedance. We show how our methodology is able to take into account the transition from free motion to a contact condition, typical of physical interaction tasks. Results are presented for planar and three-dimensional open-chain manipulator arms. The compositionality of mechanical impedance is exploited to deal with kinematic redundancy and multi-arm manipulation.

This paper investigates the event-triggered control (ETC) problem for stochastic networked control systems (NCSs) with exogenous disturbances and Denial-of-Service (DoS) attacks. The ETC strategy is proposed to reduce the utilization of network resource while defending the DoS attacks. Based on the introduced ETC strategy, sufficient conditions, which rely on the frequency and duration properties of DoS attacks, are obtained to achieve the stochastic input-to-state stability and Zeno-freeness of the ETC stochastic NCSs. An example of air vehicle system is given to explain the effectiveness of proposed ETC strategy.

Quantum cryptography doesn't depend on computational capabilities of intruders; it uses inviolability of quantum physics postulates (postulate of measurement, no-cloning theorem, uncertainty principle). Some quantum key distribution protocols have absolute (theoretical and informational) stability, but quantum secure direct communication (deterministic) protocols have only asymptotic stability. For a whole class of methods to ensure Q-trit deterministic quantum cryptography protocols stability, reliable trit generation method is required. In this paper, authors have developed a high-speed and secure pseudorandom number (PRN) generation method. This method includes the following steps: initialization of the internal state vector and direct PRN generation. Based on this method TriGen v.2.0 pseudo-random number generator (PRNG) was developed and studied in practice. Therefore, analysing the results of study it can be concluded following: 1) Proposed Q-trit PRNG is better then standard C ++ PRNG and can be used on practice for critical applications; 2) NIST STS technique cannot be used to evaluate the quality (statistical stability) of the Q-trit PRNG and formed trit sequences; 3) TritSTS 2020 technique is suitable for evaluating Q-trit PRNG and trit sequences quality. A future research study can be related to developing a fully-functional version of TritSTS technique and software tool.

Ransomware attacks cost businesses more than \$75 billion/year, and it is predicted to cost \$6 trillion/year by 2021. These numbers demonstrate the havoc produced by ransomware on a large number of sectors and urge security researches to tackle it. Several ransomware detection approaches have been proposed in the literature that interchange between static and dynamic analysis. Recently, ransomware attacks were shown to fingerprint the execution environment before they attack the system to counter dynamic analysis. In this paper, we exploit the behavior of contemporary ransomware to prevent its attack on real systems and thus avoid the loss of any data. We explore a set of ransomware-generated artifacts that are launched to sniff the surrounding. Furthermore, we design, develop, and evaluate an approach that monitors the behavior of a program by intercepting the called Windows APIs. Consequently, we determine in real-time if the program is trying to inspect its surrounding before the attack, and abort it immediately prior to the initiation of any malicious encryption or locking. Through empirical evaluations using real and recent ransomware samples, we study how ransomware and benign programs inspect the environment. Additionally, we demonstrate how to prevent ransomware with a low false positive rate. We make the developed approach available to the research community at large through GitHub to strongly promote cyber security defense operations and for wide-scale evaluations and enhancements.

Consensus protocols are currently the bottlenecks that prevent blockchain systems from scaling. However, we argue that transaction execution is also important to the performance and security of blockchains. In other words, there are ample opportunities to speed up and further secure blockchains by reducing the cost of transaction execution. Our goal is to understand how much we can speed up blockchains by exploiting transaction concurrency available in blockchain workloads. To this end, we first analyze historical data of seven major public blockchains, namely Bitcoin, Bitcoin Cash, Litecoin, Dogecoin, Ethereum, Ethereum Classic, and Zilliqa. We consider two metrics for concurrency, namely the single-transaction conflict rate per block, and the group conflict rate per block. We find that there is more concurrency in UTXO-based blockchains than in account-based ones, although the amount of concurrency in the former is lower than expected. Another interesting finding is that some blockchains with larger blocks have more concurrency than blockchains with smaller blocks. Next, we propose an analytical model for estimating the transaction execution speed-up given an amount of concurrency. Using results from our empirical analysis, the model estimates that 6× speed-ups in Ethereum can be achieved if all available concurrency is exploited.

An attack graph is a method used to enumerate the possible paths that an attacker can take in the organizational network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the ability to represent network protocol vulnerabilities, and thus it cannot be used to model common network attacks, such as ARP poisoning. Second, it does not support advanced types of communication, such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this paper, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols, (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service attacks, as well as attacks on advanced types of communication. We demonstrate the proposed model in a testbed which implements a simplified network architecture comprised of both IT and industrial components

LoRaWAN (Low-power Wide-Area Networks) is the main specification for application-level IoT (Internet of Things). The current version, published in October 2017, is LoRaWAN 1.1, with its 1.0 precursor still being the main specification supported by commercial devices such as PyCom LoRa transceivers. Prior (semi)-formal investigations into the security of the LoRaWAN protocols are scarce, especially for Lo-RaWAN 1.1. Moreover, amongst these few, the current encodings [4], [9] of LoRaWAN into verification tools unfortunately rely on much-simplified versions of the LoRaWAN protocols, undermining the relevance of the results in practice. In this paper, we fill in some of these gaps. Whilst we briefly discuss the most recent cryptographic-orientated works [5] that looked at LoRaWAN 1.1, our true focus is on producing formal analyses of the security and correctness of LoRaWAN, mechanised inside automated tools. To this end, we use the state-of-the-art prover, Tamarin. Importantly, our Tamarin models are a faithful and precise rendering of the LoRaWAN specifications. For example, we model the bespoke nonce-generation mechanisms newly introduced in LoRaWAN 1.1, as well as the “classical” but shortdomain nonces in LoRaWAN 1.0 and make recommendations regarding these. Whilst we include small parts on device-commissioning and application-level traffic, we primarily scrutinise the Join Procedure of LoRaWAN, and focus on version 1.1 of the specification, but also include an analysis of Lo-RaWAN 1.0. To this end, we consider three increasingly strong threat models, resting on a Dolev-Yao attacker acting modulo different requirements made on various channels (e.g., secure/insecure) and the level of trust placed on entities (e.g., honest/corruptible network servers). Importantly, one of these threat models is exactly in line with the LoRaWAN specification, yet it unfortunately still leads to attacks. In response to the exhibited attacks, we propose a minimal patch of the LoRaWAN 1.1 Join Procedure, which is as backwards-compatible as possible with the current version. We analyse and prove this patch secure in the strongest threat model mentioned above. This work has been responsibly disclosed to the LoRa Alliance, and we are liaising with the Security Working Group of the LoRa Alliance, in order to improve the clarity of the LoRaWAN 1.1 specifications in light of our findings, but also by using formal analysis as part of a feedback-loop of future and current specification writing.

The essence of network security is the asymmetric online confrontation with the partial observable cyber threats, which requires the defense ability against unexpected security incidents. The existing network intrusion detection systems are mostly static centralized structure, and usually faced with problems such as high pressure of central processing node, low fault tolerance, low damage resistance and high construction cost. In this paper, exploiting the advantage of collaborative decision-making of decentralized multiagent coordination, we design a collaborative cyber threat perception model, DI-MDPs, which is based on the decentralized coordination, and the core idea is initiative information interaction among agents. Then, we analysis the relevance and transformation conditions between the proposed model, then contribute a reinforcement learning algorithm HTI that takes advantage of the particular structure of DI-MDPs in which agent updates policies by learning both its local cognition and the additional information obtained through interaction. Finally, we compare and verify the performance of the designed algorithm under typical scenario setting.

Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become a must-have property of all new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient.

Memory corruption vulnerabilities, mainly present in C and C++ applications, may enable attackers to maliciously take control over the program running on a target machine by forcing it to execute an unintended sequence of instructions present in memory. This is the principle of modern Code-Reuse Attacks (CRAs) and of famous attack paradigms as Return-Oriented Programming (ROP) and Jump-Oriented Programming (JOP). Control-Flow Integrity (CFI) is a promising approach to protect against such runtime attacks. Recently, many CFI-based solutions have been proposed, resorting to both hardware and software implementations. However, many of these solutions are hardly applicable to microcontroller systems, often very resource-limited. The paper presents a generic, portable, and lightweight CFI solution for bare-metal embedded systems, i.e., systems that execute firmware directly from their Flash memory, without any Operating System. The proposed defense mixes software and hardware instrumentation and is based on monitoring the Control-Flow Graph (CFG) with an FPGA connected to the CPU. The solution, applicable in principle to any architecture which disposes of an FPGA, forces all control-flow transfers to be compliant with the CFG, and preserves the execution context from possible corruption when entering unpredictable code such as Interrupt Services Routines (ISR).

The Industrial Internet of Things (IIoT) paradigm represents nowadays the cornerstone of the industrial automation since it has introduced new features and services for different environments and has granted the connection of industrial machine sensors and actuators both to local processing and to the Internet. One of the most advanced network protocol stack for IoT-IIoT networks that have been developed is 6LoWPAN which supports IPv6 on top of Low-power Wireless Personal Area Networks (LoWPANs). 6LoWPAN is usually coupled with the IEEE 802.15.4 low-bitrate and low-energy MAC protocol that relies on the time-slotted channel hopping (TSCH) technique. In TSCH networks, a coordinator node synchronizes all end-devices and specifies whether (and when) they can transmit or not in order to improve their energy efficiency. In this scenario, the scheduling strategy adopted by the coordinator plays a crucial role that impacts dramatically on the network performance. In this paper, we present a novel scheduling strategy for time-slot allocation in IIoT communications which aims at the improvement of the overall network fairness. The proposed strategy mimics the well-known shell game turning the totally unfair mechanics of this game into a fair scheduling strategy. We compare our proposal with three allocation strategies, and we evaluate the fairness of each scheduler showing that our allocator outperforms the others.

The Security available in personal computers and laptops are not possible in mobile communication, since there is no controlling software such as an operating system. The European Union General Data Protection Regulation (GDPR) will require many organisations throughout the European Union to comply with new requirements that are intended to protect their user's personal data. The responsibilities of the organizations and the penalties related to the protection of personal data of the users are proved to be both organisationally and technically challenging. Under the GDPR's 'privacy by design' and 'privacy by default' requirements, organizations need to prove that they are in control of user data and have taken steps to protect it. There are a large number of organizations that makes use of mobile devices to process personal data of their customers. GDPR mandates that the organization shall be able to manage all devices that handles sensitive data so that the company can implement group updates, restrict apps and networks, and enforce security measures. In this work, we propose a Mobile Device Management solution using the built-in frameworks of Android and iOS mobile platforms which is compatible and incorporates GDPR articles relevant to a small to medium sized organization.

This paper proposes a mathematical model for assessing the trustworthiness of the system condition prognosis. The set of mutually exclusive events at the time of predictive checking are analyzed. Correct and incorrect decisions correspond to events such as true-positive, false-positive, true-negative, and false-negative. General expressions for computing the probabilities of possible decisions when predicting the system condition at discrete times are proposed. The paper introduces the effectiveness indicators of predictive maintenance in the form of average operating costs, total error probability, and a posteriori probability of failure-free operation in the upcoming interval. We illustrate the developed approach by calculating the probabilities of correct and incorrect decisions for a specific stochastic deterioration process.

Current Internet Protocol routing provides minimal privacy, which enables multiple exploits. The main issue is that the source and destination addresses of all packets appear in plain text. This enables numerous attacks, including surveillance, man-in-the-middle (MITM), and denial of service (DoS). The talk explains how these attacks work in the current network. Endpoints often believe that use of Network Address Translation (NAT), and Dynamic Host Configuration Protocol (DHCP) can minimize the loss of privacy.We will explain how the regularity of human behavior can be used to overcome these countermeasures. Once packets leave the local autonomous system (AS), they are routed through the network by the Border Gateway Protocol (BGP). The talk will discuss the unreliability of BGP and current attacks on the routing protocol. This will include an introduction to BGP injects and the PEERING testbed for BGP experimentation. One experiment we have performed uses statistical methods (CUSUM and F-test) to detect BGP injection events. We describe work we performed that applies BGP injects to Internet Protocol (IP) address randomization to replace fixed IP addresses in headers with randomized addresses. We explain the similarities and differences of this approach with virtual private networks (VPNs). Analysis of this work shows that BGP reliance on autonomous system (AS) numbers removes privacy from the concept, even though it would disable the current generation of MITM and DoS attacks. We end by presenting a compromise approach that creates software-defined data exchanges (SDX), which mix traffic randomization with VPN concepts. We contrast this approach with the Tor overlay network and provide some performance data.

This paper proposes and analyzes a new virtual machine (VM) placement technique called Group Instance to deal with co-location attacks in public Infrastructure-as-a-Service (IaaS) clouds. Specifically, Group Instance organizes cloud users into groups with pre-determined sizes set by the cloud provider. Our empirical results obtained via experiments with real-world data sets containing million of VM requests have demonstrated the effectiveness of the new technique. In particular, the advantages of Group Instance are three-fold: 1) it is simple and highly configurable to suit the financial and security needs of cloud providers, 2) it produces better or at least similar performance compared to more complicated, state-of-the-art algorithms in terms of resource utilization and co-location security, and 3) it does not require any modifications to the underlying infrastructures of existing public cloud services.

What is the minimum number of guesses needed on average to correctly guess a realization of a random variable? The answer to this question led to the introduction of the notion of a quantity called guesswork by Massey in 1994, which can be viewed as an alternate security criterion to entropy. In this paper, we consider guesswork in the presence of quantum side information, and show that a general sequential guessing strategy is equivalent to performing a single quantum measurement and choosing a guessing strategy based on the outcome. We use this result to deduce entropic one-shot and asymptotic bounds on the guesswork in the presence of quantum side information, and to formulate a semi-definite program (SDP) to calculate the quantity. We evaluate the guesswork for a simple example involving the BB84 states, and we prove a continuity result that certifies the security of slightly imperfect key states when the guesswork is used as the security criterion.

Designing an authentication method is a crucial component to secure privacy in information systems. Virtual Reality (VR) is a new interaction platform, in which the users can interact with natural behaviours (e.g. hand, gaze, head, etc.). In this work, we propose a novel authentication method in which user can perform hand motion in an eyes-free manner. We evaluate the usability and security between eyes-engage and eyes-free input with a pilot study. The initial result revealed our purposed method can achieve a trade-off between usability and security, showing a new way to behaviour-based authentication in VR.

The security of integrated circuits (IC) plays a very significant role on military, economy, communication and other industries. Due to the globalization of the integrated circuit (IC) from design to manufacturing process, the IC chip is vulnerable to be implanted malicious circuit, which is known as hardware Trojan (HT). When the HT is activated, it will modify the functionality, reduce the reliability of IC, and even leak confidential information about the system and seriously threatens national security. The HT detection theory and method is hotspot in the security of integrated circuit. However, most methods are focusing on the simulated data. Moreover, the measurement data of the real circuit are greatly affected by the measurement noise and process disturbances and few methods are available with small size of the Trojan circuit. In this paper, the problem of detection was cast as signal representation among multiple linear regression and sparse representation-based classifier (SRC) were first applied for Trojan detection. We assume that the training samples from a single class do lie on a subspace, and the test samples can be represented by the single class. The proposed SRC HT detection method on real integrated circuit shows high accuracy and efficiency.

This paper uses side channel analysis to detect hardware Trojan based on back propagation neural network. First, a power consumption collection platform is built to collect power waveforms, and the amplifier is utilized to amplify power consumption information to improve the detection accuracy. Then the small difference between the power waveforms is recognized by the back propagation neural network to achieve the purpose of detection. This method is validated on Advanced Encryption Standard circuit. Results show this method is able to identify the circuits with a Trojan occupied 0.19% of Advanced Encryption Standard circuit. And the detection accuracy rate can reach 100%.

Hierarchical Text classification has recently become increasingly challenging with the growing number of classification labels. In this paper, we propose a hierarchical fine-tuning based approach for hierarchical text classification. We use the ordered neurons LSTM (ONLSTM) model by combining the embedding of text and parent category for hierarchical text classification with a large number of categories, which makes full use of the connection between the upper-level and lower-level labels. Extensive experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.