Abstract

recently, the Digital Twin (DT) technology has procured a lot of attention because of its applicability in the manufacturing and space industries. The DT environment involves the formation of a clone of the tangible object to perform simulations in the virtual space. The combination of conceptual development, predictive maintenance, real-time monitoring, and simulation characteristics of DT has increased the utilization of DT in different scenarios, such as medical environments, healthcare, manufacturing industries, aerospace, etc. However, these utilizations have also brought serious security pitfalls in DT deployment. Towards this, several authentication protocols with different security and privacy features for DT environments have been proposed. In this article, we first review a recently proposed two-factor authentication protocol for DT environments that utilizes the blockchain technology. However, the analyzed scheme is unable to offer the desirable security and cannot withstand various security attacks like offline password-guessing attack, smart card stolen attack, anonymity property, and known session- specific temporary information attack. We also demonstrate that an attacker can impersonate the analyzed protocol’s legal user, owner, and cloud server. To mitigate these security loopholes, we devise an effective three-factor privacy-preserving authentication scheme for DT environments. The proposed work is demonstrated to be secure by performing the informal security analysis, the formal security analysis using the widely recognized Burrows-Abadi-Needham (BAN) logic, and the Real-or-Random (ROR) model. A detailed comparative study with the existing competing schemes including the analyzed scheme demonstrates that the devised framework furnishes better security features while also having lower computation costs and comparable communication costs than the existing schemes.

Abstract

The growth of IoT devices is so rapid that several billions of such devices would be in use in a span of four-year period. Essential security mechanisms need to be put in place to curb several security attacks prevalent in IoT. Access control is an important security mechanism that ensures legitimate and controlled access to critical and limited resources in IoT. The current access control schemes for IoT could not handle burgeoning number of IoT devices, while meeting the necessary level of security. Consequently, in this paper, we propose a new scalable and secure access control scheme for IoT. With blockchain as the root-of-trust, the proposed scheme performs access control for the IoT devices without having the resource-constrained IoT devices to be part of the blockchain network and to possess substantial amount of blockchain data. Blockchain’s tamper-proof property makes it an ideal candidate to be chosen as the root-of-trust. The scheme is secure against various security attacks prevalent in IoT. A proof-of-concept implementation for the scheme is developed and deployed in Ethereum Mainnet. The transaction costs of the different operations in the scheme are fairly below USD 3. Furthermore, scalability of the proposed scheme in different scenarios is investigated.

Abstract

IoT-enabled Smart Grid (IoT-SG) is an emerging paradigm that enables the bi-direction communication of IoT devices and hardware to efficiently collect and transmit the consumer’s information over the Internet. However, the underlying open communication network and resource-constrained capabilities of devices invite manifold challenges and threats in terms of security and privacy. To alleviate such issues, we designed a private blockchain-based access control protocol for IoT-SG using Physically Unclonable Function (PUF). Our protocol allows the Service Provider (SP) and smart meters to transfer the data in an efficient and secure manner. The participating SPs form the Peer-to-Peer (P2P) network, and each peer node is responsible for securely creating the blocks from the gathered data. Thereafter, all the peer nodes employ a voting-based consensus mechanism to verify and add the recently created block …

Abstract

With the continuous improvement of the public medical system, the current medical service industry is more intelligent. Among them, the Internet of Medical Things (IoMT) plays a significant role in intelligent medicine. The emergence of remote wireless monitoring platforms in the IoMT systems has significantly reduced the risk of virus infection among medical staff. However, data is easy to be stolen by criminals in the transmission process. To mitigate these issues, we propose a three-factor enhanced protocol for monitoring patient body information. The proposed protocol can provide the confidentiality of transmitted data and the privacy of doctors and patients. The security of the protocol is demonstrated in the Random Oracle Model (ROM) for formal security. In addition, by comparing the proposed protocol with other related works, our design has efficient performance both in execution time and communication costs.

Abstract

Multiple kinds of ransomware are currently posing a growing threat to the Internet users. Important user data is encrypted by the trendy ransomware, and its recovery requires payment of a ransom in terms of some amount of money. The trend of crypto-currencies may be a contributing factor to the rise of ransomware attacks. From time to time, different mechanisms for detection and mitigation of ransomware attacks have been proposed. However, the performance of the ransomware detection process can be improved through the deployment of Machine Learning/Deep Learning based mechanisms. In this article, we propose a novel Spline Interpolation envisioned Neural Network based Ransomware Detection Scheme (in short, we call it as SINN-RD). Additionally, we introduce the mechanisms for normalizing the data and generating the new features from the log files. The conducted security analysis of the

Abstract

Designing a secure and efficient authentication protocol is crucial and challenging in the mobility network. Due to the seamless roaming of mobile users over multiple foreign agents and the broadcast nature of the communication channel, the mobile networks are often exposed to several network attacks. To achieve perfect authentication and secure communication among mobility entities like MU (Mobile User), FA (Foreign Agent) and HA (Home Agent), the researchers have proposed numerous authentication protocols in the past. However, the existing protocols for the mobility environments are insufficient to address the fundamental security concerns and an adversary can impersonate the mobile user at anytime. Thus, we propose Mobile-Chain, a secure blockchain-based authentication system for mobility environments. The proposed Mobile-Chain is designed to protect user privacy and guarantees provable

Abstract

Various studies have shown the advantages of using Machine Learning (ML) techniques for analog and digital IC design automation and optimization. Data scarcity is still an issue for electronic designs, while training highly accurate ML models. This work proposes generating and evaluating artificial data using generative adversarial networks (GANs) for circuit data to aid and improve the accuracy of ML models trained with a small training data set. The training data is obtained by various simulations in the Cadence Virtuoso, HSPICE, and Microcap design environment with TSMC 180nm and 22nm CMOS technology nodes. The artificial data is generated and tested for an appropriate set of analog and digital circuits. The experimental results show that the proposed artificial data generation significantly improves ML models and reduces the percentage error by more than 50% of the original percentage error, which were previously trained with insufficient data. Furthermore, this research aims to contribute to the extensive application of AI/ML in the field of VLSI design and technology by relieving the training data availability-related challenges.

Abstract

Extreme multilabel classification or XML, in short, has emerged as a new subtopic of interest in machine learning. Compared to traditional multilabel classification, here the number of labels is extremely large, hence the name extreme multilabel classification. Using classical one versus all classification wont scale in this case due to large number of labels, same is true for any other classifiers. Embedding of labels as well as features into smaller label space is an essential first step. Moreover, other issues include existance of head and tail labels, where tail labels are labels which exist in relatively smaller number of given samples. The existence of tail labels creates issues during embedding. This area has invited application of wide range of approaches ranging from bit compression motivated from compressed sensing, tree based embeddings, deep learning based latent space embedding including using attention weights, linear algebra based embeddings such as SVD, clustering, hashing, to name a few. The community has come up with a useful set of metrics to identify the correctly the prediction for head or tail labels. Keywords: extreme classification, head and tail labels, compressed sensing, deep learning, attention

Abstract

We consider the problem of learning tensors from partial observa- tions with structural constraints, under the low-rank assumption. To this end, we propose a general convex low-rank regularizer, pa- rameterized by linear maps, that extends the existing regularizers. Different choices of the linear maps lead to different convex low- rank regularizers. The resulting problem is called the generalized structured low-rank tensor learning problem. To solve this problem, we use duality theory to reformulate it into simpler sub-problems. The dual problem contains a rich geometric structure, which we exploit to develop first-order and second-order Riemannian opti- mization algorithms. The associated duality gap is derived, and it is shown to be zero. Moreover, we experimentally verify the cor- rectness of our algorithm on several special cases of the proposed general framework.

Abstract

We investigate whether it is possible to teleport the coherence of an unknown quantum state from Alice to Bob by communicating lesser number of classical bits in comparison to what is required in teleporting an unknown quantum state. We find that we cannot do perfect teleportation of coherence with one bit of classical communication for an arbitrary qubit. However, we find that if the qubit is chosen from equatorial and polar circles, then teleportation of coherence will be possible with transfer of one cbit of information if we have maximally entangled states as a shared resource. In the case of resource being a non maximally entangled state, we can teleport quantum coherence with a certain probability of success. In a general teleportation protocol for coherence, we derive a compact formula for the final state at Bob’s lab in terms of composition of the completely positive maps corresponding to the shared resource state and joint POVM performed by Alice on her qubit and the unknown state. With the help of this formula, we investigate the amount of coherence teleported when the resource shared state is the maximally entangled mixed state and Werner state for the unknown pure as well as mixed states state at Alice’s lab. In particular, we show that when the Werner state becomes separable then also the amount of teleported coherence is non-zero, implying the possibility of teleportation of coherence without entanglement. We have also shown that teleportation of coherence of an arbitrary state with real matrix elements is exactly possible with the help of maximally entangled state as a resource.