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

The U(1) extensions of the Standard Model contain a heavy neutral gauge boson Z 0 . If leptophobic, the boson can evade the stringent bounds from the dilepton resonance searches. In these extensions, righthanded neutrinos NR are also well-motivated by the gauge-anomaly cancellation and light-neutrino mass generation via seesaw mechanisms. The coexistence of a leptophobic Z 0 and the right-handed neutrinos opens up a new possibility of searching for these particles simultaneously through the production of Z 0 at the LHC and its decay to a NR pair. We study this process in an inverse seesaw set-up where the heavy neutrinos are pseudo-Dirac in nature. Hence, in our case, the NR pair produce only opposite-sign lepton pairs, as opposed to the Majorana-type neutrinos, which can produce both the same and the oppositesign lepton pairs. In particular, the signature we study involves a same-flavour-opposite-sign lepton pair plus hadronically-decaying boosted W bosons. Our analysis shows that the High Luminosity LHC can discover a TeV-scale leptophobic Z 0 decaying via NR pair for a wide range of available parameters in our model. Considerable parameter regions beyond the reach of the future dijet resonance searches can be probed through our channel.

Abstract

TeV-scale vector-like quarks (VLQs) are an essential ingredient of many new physics models. Because of their vectorlike nature, they do not contribute to the gauge anomalies and are less restricted than their chiral counterparts by the current experimental data. Ones that couple with the thirdgeneration quarks (top and bottom partners, we shall collectively refer to them as top partners) appear in composite Higgs models with a partially-composite top quark [1– 6], extra-dimensional models [7–11], Little-Higgs models [12–15], etc. However, in the last few years, an extensive direct search program at the LHC has gradually tightened the mass bounds on these quarks. For top partners like the T quark (with electromagnetic charge 2/3) or the B quark (with charge −1/3), the current exclusion limit stands as high as ∼ 1.5 TeV [16–20]. The direct VLQ searches generally assume that they decay exclusively to Standard Model (SM) particles, i.e., to a third-generation quark and a heavy vector-boson or a Higgs. However, this assumption need not hold in general, especially if one looks beyond the minimal models where the top partners can have new decay modes. For example, a top partner can decay to another heavy quark or a new boson [11, 21–43]. A possibility that has attracted some interest in the current literature is that a vector-like top partner decays to a new spinless state singlet under the SM gauge group [i.e., (1, 1, 0) under SU(3)c×SU(2)L ×U(1)Y ] and a third-generation quark. The singlet state could be

Abstract

Object detection plays an essential role in providing localization, path planning, and decision making capabilities in autonomous navigation systems. However, existing object detection models are trained and tested on a fixed number of known classes. This setting makes the object detection model difficult to generalize well in real-world road scenarios while encountering an unknown object. We address this problem by introducing our framework that handles the issue of unknown object detection and updates the model when unknown object labels are available. Next, our solution includes three major components that address the inherent problems present in the road scene datasets. The novel components are a) Feature-Mix that improves the unknown object detection by widening the gap between known and unknown classes in latent feature space, b) Focal regression loss handling the problem of improving small object detection and intra-class scale variation, and c) Curriculum learning further enhances the detection of small objects. We use Indian Driving Dataset (IDD) and Berkeley Deep Drive (BDD) dataset for evaluation. Our solution provides state-of-the-art performance on open-world evaluation metrics. We hope this work will create new directions for open-world object detection for road scenes, making it more reliable and robust autonomous systems.

Abstract

Keyword Spotting (KWS) detects a set of pre-defined spoken keywords. Building a KWS system for an arbitrary set requires massive training datasets. We propose to use the text transcripts from an Automatic Speech Recognition (ASR) system alongside triplets for KWS training. The intermediate representation from the ASR system trained on a speech corpus is used as acoustic word embeddings for keywords. Triplet loss is added to the Connectionist Temporal Classification (CTC) loss in the ASR while training. This method achieves an Average Precision (AP) of 0.843 over 344 words unseen by the model trained on the TIMIT dataset. In contrast, the Multi-View recurrent method that learns jointly on the text and acoustic embeddings achieves only 0.218 for out-of-vocabulary words. This method is also applied to low-resource languages such as Tamil by converting Tamil characters to English using transliteration. This is a very challenging novel task for which we provide a dataset of transcripts for the keywords. Despite our model not generalizing well, we achieve a benchmark AP of 0.321 on over 38 words unseen by the model on the MSWC Tamil keyword set. The model also produces an accuracy of 96.2% for classification tasks on the Google Speech Commands dataset. Index Terms: speech recognition, keyword spotting, lowresource languages

Abstract

Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.

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.