Abstract

Due to wide-spread use of the Information and Communications Technology (ICT) and Internet of Things (IoT) enabled smart devices, called unmanned aerial vehicles (UAVs) (popularly known as drones), a lot of potential applications of Internet of Drones (IoD) are available ranging from the military to civilian applications. Access control mechanism is an important potential security service that is needed to secure communication among the drones in their respective flying zones, and also among the drones and the Ground Service Station (GSS). In 2021, Chaudhry et al. proposed a certificate based generic access control scheme for IoD environment, called GCACS-IoD. Their claims about the possible security attacks resistant of GCACS-IoD is not justified. In fact, we first prove that GCACS-IoD is unable to protect the disclosure of the private key rCR of the trusted control room (CR), which is extremely unfortunate careless design flaw and it leads to compromise the entire network. Using the disclosed private key rCR , we further show that GCACS-IoD is completely insecure against other serious attacks, such as malicious drones deployment attack, drone/GSS impersonation attacks and Ephemeral Secret Leakage (ESL) attack, which lead to compromise the session key between any two drones communicating in a particular flying zone. We thus feel that there is a strong need to remedy such serious weaknesses found in Chaudhry et al. ’s GCACS-IoD. An improved certificate-enabled generic access control scheme for IoD deployment, called as i GCACS-IoD, has been suggested, which overcomes the weaknesses found in the previous GCACS-IoD. The practical demonstration of i GCACS-IoD has been done through formal security verification and also through NS2 simulation study.

Abstract

Fog computing as an extension to the cloud computing infrastructure has been invaluable in enhancing the applicability of the Internet of Things (IoT) paradigm. IoT based Fog systems magnify the range and minimize the latency of IoT applications. However, as fog nodes are considered transient and they offer authenticated services, when an IoT end device loses connectivity with a fog node, it must authenticate freshly with a secondary fog node. In this work, we present a new security mechanism to leverage the initial authentication to perform fast lightweight secondary authentication to ensure smooth failover among fog nodes. The proposed scheme is secure in the presence of a current de-facto Canetti and Krawczyk (CK)-adversary. We demonstrate the security of the proposed scheme with a detailed security analysis using formal security under the broadly recognized Real-Or-Random (ROR) model, informal security analysis as well as through formal security verification using the broadly-used Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool. A testbed experiment for measuring computational time for different cryptographic primitives using the Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) has been done. Finally, through comparative analysis with other related schemes, we show how the presented approach is uniquely advantageous over other schemes.

Abstract

Emerging next-generation Internet yields proper administration of a wide-ranging dynamic network to assist rapid ubiquitous resource accessibility, whilst providing higher channel bandwidth. Since its inception, the traditional static network infrastructure-based solutions involve manual configuration and proprietary controls of networked devices. It then leads to improper utilization of the overall resources, and hence experiences various security threats. Although transport layer security (TLS)-based solution is presently advocated in the said framework, it is vulnerable to many security threats like man-in-the-middle, replay, spoofing, privileged-insider, impersonation, and denial-of-service attacks. Moreover, the current settings of the said tool do not facilitate any secure and reliable mechanisms for data forwarding, application flow routing, new configuration deployment, and network event management. Also, it suffers from the single point of controller failure issue. In this paper, we propose a new private blockchain-enabled fine-grained access control mechanism for the SDN environment. In this regard, attribute-based encryption (ABE) and certificate-based access control protocol are incorporated. This proposed solution can resist several well-known security threats, and alleviate different system-level inconveniences. The formal and informal security inspections and performance-wise comparative study of the proposed scheme endorse better qualifying scores as compared to the other existing competing state-of-art schemes. Besides, the experimental testbed implementation and blockchain simulation show the implementation feasibility of the proposed mechanism.

Abstract

Software-defined networking (SDN) is a programmable architecture for networking domain in which the security is provided by devising the network policies with the help of the network administrator. This is very cumbersome for the administrator to handle different attacks at various planes in SDN architecture. Blockchain can be used to prevent various attacks in SDN by providing a decentralization authentication environment. In this article, a secure Blockchain-based privacy-preserving protocol is proposed to thwart various security vulnerabilities in the SDN architecture. The proposed approach uses Modified-Delegated Proof of Stake as the consensus protocol to ensure safety and reliability in the network. Besides, a security protocol is designed using cryptographic primitives and analyzed using a detailed security analysis. Initially, the consensus protocols are implemented using solidity smart contracts and deployed to the public Blockchain using Ethereum. Consequently, the proposed approach is simulated on OMNet++ using INET framework. The experimental results show that the proposed SDN-Chain is secure, efficient, less incentive to centralize, and practically implementable in resource-limited wireless and mobile environments.

Abstract

We study several fundamental problems in the kmachine model, a message-passing model for large-scale distributed computations where k 2 machines jointly perform computations on a large input of size N, (typically, N k). The input is initially partitioned (randomly or in a balanced fashion) among the k machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication rounds of the computation. Our main result is a general technique for designing efficient deterministic distributed algorithms in the k-machine model using PRAM algorithms. Our technique works by efficiently simulating PRAM algorithms in the k-machine model in a deterministic way. This simulation allows us to arrive at new algorithms in the k-machine model for some problems for which no efficient k-machine algorithms are known before and also improve on existing results in the k-machine model for some problems. While our simulation allows us to obtain k-machine algorithms for any problem with a known PRAM algorithm, we mainly focus on graph problems. For an input graph on n vertices and m edges, we obtain O˜(m/k2) round4 algorithms for various graph problems such as r–connectivity for r = 1, 2, 3, 4, minimum spanning tree (MST), maximal independent set (MIS), (Δ+1)-coloring, maximal matching, ear decomposition, and spanners under the assumption that the edges of the input graph are partitioned (randomly, or in an arbitrary, but balanced, fashion) among the k machines. For problems such as connectivity and MST, the above bound is (essentially) the best possible (up to logarithmic factors). Our simulation technique allows us to obtain the first known efficient deterministic algorithms in the k-machine model for other problems with known deterministic PRAM algorithms.

Abstract

Parametric path problems arise independently in diverse domains, ranging from transportation to finance, where they are studied under various assumptions. We formulate a general path problem with relaxed assumptions, and describe how this formulation is applicable in these domains. We study the complexity of the general problem, and a variant of it where preprocessing is allowed. We show that when the parametric weights are linear functions, algorithms remain tractable even under our relaxed assumptions. Furthermore, we show that if the weights are allowed to be non-linear, the problem becomes NP-hard. We also study the mutli-dimensional version of the problem where the weight functions are parameterized by multiple parameters. We show that even with two parameters, the problem is NP-hard.

Abstract

It is well known that it is impossible to clone an arbitrary quantum state. However, this inability does not lead directly to no cloning of quantum coherence. Here, in this article, we show that it is impossible to clone the coherence of an arbitrary quantum state. In particular, with an ancillary system as machine state, we show that it is impossible to clone the coherence of states whose coherence is greater than the coherence of the known states on which the transformations are defined. Also, we characterize the class of states for which coherence cloning will be possible for a given choice of machine. Furthermore, we find the maximum range of states whose coherence can be cloned perfectly. The impossibility proof also holds when we do not include machine states. Lastly, we generalize the impossibility of cloning of coherence in terms of dimension of the quantum state and coherence measure taken into consideration.

Abstract

Multilingual automatic speech recognition (ASR) system is a single entity capable of transcribing multiple languages sharing a common phone space. Performance of such a system is highly dependent on the compatibility of the languages. State of the art speech recognition systems are built using sequential architectures based on recurrent neural networks (RNN) limiting the computational parallelization in training. This poses a significant challenge in terms of time taken to bootstrap and validate the compatibility of multiple languages for building a robust multilingual system. Complex architectural choices based on self-attention networks are made to improve the parallelization thereby reducing the training time. In this work, we propose Reed, a simple system based on 1D convolutions which uses very short context to improve the training time. To improve the performance of our system, we use raw time-domain speech signals directly as input. This enables the convolutional layers to learn feature representations rather than relying on handcrafted features such as MFCC. We report improvement on training and inference times by atleast a factor of 4× and 7.4× respectively with comparable WERs against standard RNN based baseline systems on SpeechOcean's multilingual low resource dataset.

Abstract

Direct acoustic feature-based speaking rate estimation is useful in applications including pronunciation assessment, dysarthria detection and automatic speech recognition. Most of the existing works on speaking rate estimation have steps which are heuristically designed. In contrast to the existing works, in this work a data-driven approach with convolutional neural network-bidirectional long short-term memory (CNN-BLSTM) network is proposed to jointly optimize all steps in speaking rate estimation through a single framework. Also, unlike existing deep learning-based methods for speaking rate estimation, the proposed approach estimates the speaking rate for an entire speech utterance in one go instead of considering segments of a fixed duration. We consider the traditional 19 sub-band energy (SBE) contours as the low-level features as the input of the proposed CNN-BLSTM network. The state-of-the-art direct acoustic feature-based speaking rate estimation techniques are developed based on 19 SBEs as well. Experiments are performed separately using three native English speech corpora (Switchboard, TIMIT and CTIMIT) and a non-native English speech corpus (ISLE). Among these, TIMIT and Switchboard are used for training the network. However, testing is carried out on all the four corpora as well as TIMIT and Switchboard with additive noise, namely white, car, high-frequency-channel, cockpit, and babble at 20, 10 and 0 dB signal-to-noise ratios. The proposed CNN-BLSTM approach outperforms the best of the existing techniques in clean as well as noisy conditions for all four corpora.

Abstract

The teacher-student framework aims to improve the sample efficiency of RL algorithms by deploying an advising mechanism in which a teacher helps a student by guiding its exploration. Prior work in this field has considered an advising mechanism where the teacher advises the student about the optimal action to take in a given state. However, real-world teachers can leverage domain expertise to provide more informative signals. Using this insight, we propose to extend the current advising framework wherein the teacher would provide not only the optimal action but also a qualitative assessment of the state. We introduce a novel architecture, namely Advice Replay Memory (ARM), to effectively reuse the advice provided by the teacher. We demonstrate the robustness of our approach by showcasing our experiments on multiple Atari 2600 games using a fixed set of hyper-parameters. Additionally, we show that a student taking help even from a suboptimal teacher can achieve significant performance boosts and eventually outperform the teacher. Our approach outperforms the baselines even when provided with comparatively suboptimal teachers and an advising budget, which is smaller by orders of magnitude. The contributions of our paper are 4-fold (a) supplementing student’s knowledge by providing the state category (b) introduction of ARM to effectively reuse the advice throughout learning (c) ability to achieve significant performance boost even with a coarse state categorization (d) enabling the student to outperform the teacher.