Abstract

Random Number Generators (RNGs) are the solution for cryptographic applications to enhance hardware security. These RNGs ought to have three specific properties unpredictability, aperiodic, and good statistical criteria. This brief presents a True Random Number Generator (TRNG) based on Ring oscillators’ jitter with MLFSR. The MLFSR is augmented with a set of prime primitive polynomials, Boolean, and non-linear functions to attain a non-linear, unpredictable, and extended sequence period. Paper mainly focused on achieving high randomness by integrated the TRNG with a new promising crypto engine Keccak as a post-processing block; leads to extensively more security in data transfer, encryption keys, data authenticity of ICs, and IoT based applications. The RNG design is coded in Verilog HDL and implemented on the FPGA Zed board. Hashing is performed with a throughput of 2.4Gbps at 100 MHz either with RNG data or SHA data. Evaluated the randomness of the generated non-deterministic bitstreams (10 Mb) using the NIST 800–22 & Diehard test suite and successfully passed.

Abstract

In an Intelligent Precision Agriculture (IPA), several Internet of Things (IoT) smart devices and drones can be deployed to monitor an agricultural environment. The drones can be further utilized to collect the data from smart devices and send to the Ground Station Server (GSS). However, insecure communication among the smart devices, drones and the GSS make the IoT agriculture environment vulnerable to various potential attacks. For this goal, a new authentication and key management scheme for IoT-enabled IPA, called AKMS-AgriIoT, has been put forward with the private blockchain-based solution. The blocks formed with the encrypted transactions and their respective signatures by the GSS are mined by the cloud servers to verify and add the blocks in the private blockchain center. A detailed security analysis and comparative study reveal that the proposed AKMS-AgriIoT supports better security, and provides more functionality features, less communication costs and comparable computation costs as compared to other relevant schemes. In addition, the blockchain-based implementation on the proposed AKMS-AgriIoT has been also carried out.

Abstract

—Effective design of autopilots for fixed-wing unmanned aerial vehicles (UAVs) is still a great challenge, due to unmodeled effects and uncertainties that these vehicles exhibit during flight. Unmodeled effects and uncertainties comprise longitudinal/lateral cross-couplings, as well as poor knowledge of equilibrium points (trimming points) of the UAV dynamics. The main contribution of this article is a new adaptive autopilot design, based on uncertain Euler–Lagrange dynamics of the UAV and where the control can explicitly take into account under-actuation in the dynamics, reduced structural knowledge of cross-couplings and trimming points. This system uncertainty is handled via appropriately designed adaptive laws: stability of the controlled UAV is analyzed. Hardware-in-the-loop tests, comparisons with an Ardupilot autopilot and with a robustified autopilot validate the effectiveness of the control design, even in the presence of strong saturation of the UAV actuators.

Abstract

—Connected vehicle means providing different services, such as advanced driver-assistance systems (ADAS) from vehicles connected to the network. Vehicular ad-hoc networks (VANETs) can support vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to realize connected vehicle. In VANETs, secure communication must be ensured, as otherwise it can lead to traffic accidents and human injuries. Recently, many studies on V2I authentication have been conducted to guarantee the security of V2I communications. However, recent V2I authentication protocols do not consider the handover situation, and it causes unnecessary computations. As vehicles have limited computing resources, unnecessary computation can lead to overload to the vehicles. In recent years, blockchain-based VANET is an active field of research because it can provide decentralization, data integrity and transparency. Using the strength of the blockchain technology, we design a blockchain-based handover authentication protocol for VANETs. In the proposed protocol, vehicles only perform lightweight computations in handover situations for efficiency of the network. We also conduct the formal analysis such as Burrows–Abadi–Needham (BAN) logic, Real-Or-Random (ROR) oracle model, and Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation to the proposed protocol. We simulate the proposed protocol using network simulator 3 (NS-3) to verify that the proposed protocol is practical. Finally, we compare the computational cost and security features of the proposed protocol with existing protocols to show that the proposed protocol is more secure and efficient.

Abstract

—Smart vehicles-enabled intelligent transportation system (ITS) supports a wide range of applications, such as, but not limited to, traffic planning and management, collision avoidance alert system, automated road speed enforcement, electronic toll collection, and real-time parking management, to name a few. However, it suffers from various types of security and privacy issues due to insecure communication among the entities over public channels. Therefore, an efficient and lightweight security mechanism is essential to protect the data that is both at rest as well as in transit. To this direction, we propose a public blockchain-envisioned secure communication framework for ITS (in short, called PBSCF-ITS). The proposed PBSCFITS guarantees access control and key management among the vehicle to vehicle, vehicle to road side unit, and road side unit to cloud server. We analyze the security of PBSCF-ITS to prove its resilience against various types of possible attacks. Furthermore, the performance of PBSCF-ITS with other related competing schemes has been compared. The obtained results illustrate that PBSCF-ITS outperforms the existing ones. Additionally, the pragmatic study of PBSCF-ITS is conducted to check its influence on various network related performance parameters, like number of mined blocks and transactions per block.

Abstract

Healthcare service providers store the patients’ electronic medical records in the cloud in order to provide high quality healthcare services. Patients’ sensitive data is typically encrypted before storing in the cloud. However, it gives rise to a new challenge, namely access control over encrypted data. Attribute-Based Encryption (ABE) is a promising cryptographic technique to achieve the fine grained access control on outsourced encrypted data. Traditional ABE schemes assume a fixed access policy that is not suitable for the present dynamic environment. Although some ABE schemes with a dynamic access control policy have been proposed in the literature, these schemes have not addressed forward security, backward security, and user revocation after a policy update. In this paper, we propose an ABE scheme that supports access policy updates, and also provides forward security, backward security and user revocation at the same time. We have formally shown that the proposed scheme is Chosen Plaintext Attack (CPA)-secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. Finally, the performance analysis exhibits that the proposed scheme is efficient in communication and computation, and is also suitable for resource constrained devices.

Abstract

The rapid development of smartphone technology and the Internet services in mobile devices facilitates easy access to online social networking (OSN) sites anytime, anywhere. At the same time, this allures the adversaries to exploit the OSNs as a soft target for easy execution of various attacks that can quickly spread to a large number of users. In distributed denial-of-service (DDoS) attacks, an adversary aims to overwhelm the normal traffic of a targeted server with a flood of fake login messages so that the associated Internet service or website turns inoperable. In this paper, we propose a secure and lightweight authentication scheme (𝑃 𝑅𝐷𝑜𝑆) that resists DDoS and other security attacks in mobile OSN environments. We provide a multi-faceted solution towards the remedy of DDoS attacks in the OSN environment. After a certain threshold, the scheme discards further user login attempts and blocks an adversary who intends to overload the network server. We use the pre-loaded shadow identity and emergency key pairs, and a key-refilling strategy that rebuilds the essential synchronization between a blocked naive user and the OSN server. This technique restores the intended unlinkability property of the protocol. Using NS3 simulation, we study the impact of DDoS attackers on network throughput and network delay. Moreover, we validate and compare the proposed scheme against state-ofthe-art solutions using the real attacks and benign datasets. We use the Canadian Institute for Cybersecurity (CIC) DoS dataset 2017, which is generated by capturing the normal and DoS attack packets separately with subsequent pre-processed for testing. We also use the machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Gaussian Naive Bayes, and Multilayer Perceptron (MLP) to demonstrate the performance of the proposed solution in a practical attack detection scenario. We observe that these algorithms provide 97.05%, 95.48%, and 96.6% DDoS attack detection accuracy, respectively.

Abstract

Movement is a universal response to music, with dance often taking place in social settings. Although previous work has suggested that socially relevant information, such as personality and gender, are encoded in dance movement, the generalizability of previous work is limited. The current study aims to decode dancers’ gender, personality traits, and music preference from music‑induced movements. We propose a method that predicts such individual difference from free dance movements, and demonstrate the robustness of the proposed method by using two data sets collected using different musical stimuli. In addition, we introduce a novel measure to explore the relative importance of different joints in predicting individual differences. Results demonstrated near perfect classification of gender, and notably high prediction of personality and music preferences. Furthermore, learned models demonstrated generalizability across datasets highlighting the importance of certain joints in intrinsic movement patterns specific to individual differences. Results further support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music.

Abstract

In this paper, we study the role of nodes and edges in a complex network in dictating the robustness of a community structure towards structural perturbations. Specifically, we attempt to identify all vital nodes, which, when removed, would lead to a large change in the underlying community structure of the network. This problem is critical because the community structure of a network allows us to explore deep underlying insights into how the function and topology of the network affect each other. Moreover, it even provides a way to condense large net- works into smaller modules where each community acts as a meta node and aids in more straightforward network analysis. If the community structure were to be compromised by either accidental or intentional per- turbations to the network, that would make such analysis difficult. Since identifying such vital nodes is computationally intractable, we propose a suite of heuristics that allow to find solutions close to the optimality. To show the effectiveness of our approach, we first test these heuristics on small networks and then move to more extensive networks to show that we achieve similar results. Further analysis reveals that the pro- posed approaches are useful to analyze the vulnerability of communities in networks irrespective of their size and scale. Additionally, we show the performance through an extrinsic evaluation framework – we employ two tasks, i.e., link prediction and information diffusion, and show that the effect of our algorithms on these tasks is higher than the other baselines.

Abstract

ackground: The adoption of nonpharmaceutical interventions and their surveillance are critical for detecting and stopping possible transmission routes of COVID-19. A study of the effects of these interventions can help shape public health decisions. The efficacy of nonpharmaceutical interventions can be affected by public behaviors in events, such as protests. We examined mask use and mask fit in the United States, from social media images, especially during the Black Lives Matter (BLM) protests, representing the first large-scale public gatherings in the pandemic. Objective: This study assessed the use and fit of face masks and social distancing in the United States and events of large physical gatherings through public social media images from 6 cities and BLM protests. Methods: We collected and analyzed 2.04 million public social media images from New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis between February 1, 2020, and May 31, 2020. We evaluated correlations between online mask usage trends and COVID-19 cases. We looked for significant changes in mask use patterns and group posting around important policy decisions. For BLM protests, we analyzed 195,452 posts from New York and Minneapolis from May 25, 2020, to July 15, 2020. We looked at differences in adopting the preventive measures in the BLM protests through the mask fit score. Results: The average percentage of group pictures dropped from 8.05% to 4.65% after the lockdown week. New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis observed increases of 5.0%, 7.4%, 7.4%, 6.5%, 5.6%, and 7.1%, respectively, in mask use between February 2020 and May 2020. Boston and Minneapolis observed significant increases of 3.0% and 7.4%, respectively, in mask use after the mask mandates. Differences of 6.2% and 8.3% were found in group pictures between BLM posts and non-BLM posts for New York City and Minneapolis, respectively. In contrast, the differences in the percentage of masked faces in group pictures between BLM and non-BLM posts were 29.0% and 20.1% for New York City and Minneapolis, respectively. Across protests, 35% of individuals wore a mask with a fit score greater than 80%. Conclusions: The study found a significant drop in group posting when the stay-at-home laws were applied and a significant increase in mask use for 2 of 3 cities where masks were mandated. Although a positive trend toward mask use and social distancing was observed, a high percentage of posts showed disregard for the guidelines. BLM-related posts captured the lack of seriousness to safety measures, with a high percentage of group pictures and low mask fit scores. Thus, the methodology provides a directional indication of how government policies can be indirectly monitored through social media