Verification Scheme Research Articles

Selective Authentication Based Geographic Opportunistic Routing in Wireless Sensor Networks for Internet of Things Against DoS Attacks

Wireless Sensor Networks (WSNs) have been widely used as the communication system in the Internet of Things (IoT). In addition to the services provided by WSNs, many IoT-based applications require reliable data delivery over unstable wireless links. To guarantee reliable data delivery, existing works exploit geographic opportunistic routing with multiple candidate forwarders in WSNs. However, these approaches suffer from serious Denial of Service (DoS) attacks, where a large number of invalid data are deliberately delivered to receivers to disrupt the normal operations of WSNs. In this paper, we propose a selective authentication-based geographic opportunistic routing (SelGOR) to defend against the DoS attacks, meeting the requirements of authenticity and reliability in WSNs. By analyzing statistic state information (SSI) of wireless links, SelGOR leverages an SSI-based trust model to improve the efficiency of data delivery. Unlike previous opportunistic routing protocols, SelGOR ensures data integrity by developing an entropy-based selective authentication algorithm, and is able to isolate DoS attackers and reduce the computational cost. Specifically, we design a distributed cooperative verification scheme to accelerate the isolation of attackers. This scheme also makes SelGOR avoid duplicate data transmission and redundant signature verification resulting from opportunistic routing. The extensive simulations show that SelGOR provides reliable and authentic data delivery, while it only consumes 50% of the computational cost compared to other related solutions.

Cryptanalysis and Improvement of an Anonymous Batch Verification Scheme for Mobile Healthcare Crowd Sensing

As an emerging application of smart healthcare, mobile healthcare crowd sensing (MHCS) has become a research hotspot. However, how to ensure the confidentiality and integrity of data and protect the privacy of user is still a challenge for MHCS. To handle these issues, an effective and secure privacy protection scheme is indispensable. Recently, a large-scale concurrent data anonymous batch verification scheme for mobile healthcare crowd sensing was proposed by Liu et al. Unfortunately, we demonstrate that their scheme is insecure. This paper presents an improved anonymous scheme based on certificateless aggregate signature (CL-AS) for MHCS. First, considering the efficiency and the characteristics of the MHCS, the technique of aggregate signature is adopted, which can achieve batch verification and greatly save the bandwidth and computation resources. Second, anonymous communication is carried out in this scheme to realize privacy preservation. Third, based on certificateless cryptography, the proposed scheme can simplify the complicated certificate management and eliminate the key escrow problem. In addition, our scheme is provably secure against the existential forgery on adaptively chosen message attack in the Random Oracle Model assuming the computational Diffie-Hellman problem is intractable. Furthermore, security and efficiency analysis shows that our scheme is secure and efficient.

Improving Reliability: User Authentication on Smartphones Using Keystroke Biometrics

Keystroke biometrics is a well-investigated dynamic behavioral methodology that utilizes the unique behavioral patterns of users to verify their identity when tapping keys. However, the performance of keystroke biometrics is unreliable due to its high error rate and low robustness. In this paper, we propose differential evolution and adversarial noise-based user authentication (DEANUA), which is a verification scheme for enhancing reliability by reducing the error rate and improving robustness. We investigate the current mainstream features and build a more comprehensive feature set that composed of 146 features. Then, we use a differential evolution method to select an optimized feature set. With the support vector regression method on this feature set, we achieve an equal error rate (EER) of 0.12660% and also a 31.25% energy consumption reduction rate. In this paper, the model is trained with the training samples collected from one situation, but the model is used in various situations. Thus, the robustness of the model is inadequate. We constructed the adversarial noise samples to simulate users' behavioral characteristics in different situational contexts. We use the adversarial noise samples to test the models in a strict experimental environment, which raises the EER by 83.59%, to 10.9299%. Then, we enhance the model with adversarial noise samples to obtain an EER of 8.70932%, which is a reduction of 20.32%.

Research on High-Impedance Fault Diagnosis and Location Method for Mesh Topology Constant Current Remote Power Supply System in Cabled Underwater Information Networks

Cabled underwater information networks (CUINs) have evolved over the last decade to provide abundant power and broad bandwidth communication to enable marine science. To ensure reliable operation of the CUINs, the technology for high-impedance fault diagnosis and isolation with high reliability and accuracy is essential. In this paper, we review diagnosis and location methods as applied to a constant voltage ring and the tree topology network. A high-impedance fault diagnosis method based on the variation of the sampling voltage in the primary nodes (PNs) for a constant current remote power supply system is proposed. The methods for analyzing the fault voltage with using power monitoring and control system (PMACS) and communications monitoring, control system (CMACS), and hybrid detection with alternating current and direct current are used for research the high-impedance fault location based on the designed fault isolation circuit. In particular, a verification scheme for high-impedance fault location is designed for the CUINs based on the classical mesh topology. Furthermore, high-impedance faults of nodes and submarine cable sections in the trunk cable are simulated, and the variations of leakage voltage are analyzed. By researching the change of leakage voltage before and after the fault occurs, the feasibility and practicability of the diagnosis and location scheme are verified.

Face Occlusion Recognition With Deep Learning in Security Framework for the IoT

Currently, the security of the Internet of Things (IoT) has aroused great concern. Face detection under arbitrary occlusion has become a key problem affecting social security. This paper designs a novel face occlusion recognition framework in the security scene of IOT, which is used to detect some crime behaviors. Our designed framework utilizes the gradient and shape cues in a deep learning model, and it has been demonstrated to be robust for its superiority to detect faces with severe occlusion. Our contributions contain three main aspects: Firstly, we present a new algorithm based on energy function for face detection; Secondly, we use the CNN models to create deep features of occluded face; Finally, to check whether the detected face is occluded, novel sparse classification model with deep learning scheme is constructed. Statistical results demonstrate that, compared with the state of the arts, our algorithm is superior in both accuracy and robustness. Our designed head detection algorithm can achieve 98.89% accuracy rate even though there are various types of severe occlusions in faces, and our designed occlusion verification scheme can achieve 97.25% accuracy rate, at a speed of 10 frames per second.

Identity verification-based cryptography for detecting intrusion in wireless sensor networks

Identity Verification Based Cryptosystem (IDVBC) mechanism is implemented here for providing a novel and secure data transmission among nodes. Here, token-based node identity verification along with certificates and data encoding mechanism is carried out to reduce the complexity in wireless networks. By applying local identity verification scheme, the communication overheads get reduced and hence the certificate management process is simplified. Each sensor owns a local private key and should get matched with the public key generated by the base station. Encoding process uses hash signature schemes for enhancing the security measures in the network. Source node sends the encrypted or encoded data with the generated local private key and labelled token ID. Once the token ID verification is done and the key matches with the global private key generated by the BS, the decrypted or decoded data is received by the receiver. The proposed IDVBC scheme has the ability to detect malicious activity of the node, to provide high-level security and able to tolerate several security attacks. Proposed scheme is improved with 39% efficiency better than the conventional method in terms of secured data transmission.

Verifiable Subgraph Matching With Cryptographic Accumulators in Cloud Computing

Due to the rapid development of social networks, bio-informatics, internet-of-things etc. , subgraph matching query can be found in various applications. Meanwhile, the increasing popularity of storing graph data in the cloud drives demands for graph query processing on a remote cloud server. However, the query results in this scenario may not be guaranteed to be correct, especially when the cloud service provider (CSP) is malicious or compromised by some adversaries, for example, a CSP might omit some edges of the graph so that its search cost would be substantially reduced. Besides, various software bugs and unintended errors are also inevitable. All current generic verifiable computation (VC) schemes applied in this scenario are not only too impractical to be implemented but also need a lot of space to locally store their auxiliary data. To that end, we have put forth both public and designated verification schemes which focus on subgraph matching problems for outsourced graph data. They utilize a modified cryptographic primitive called accumulator to realize fast verification and low local storage overhead. In addition to the two main constructions, we have proposed an optimization to make the scheme more applicable, namely, supporting dynamic updates of the graph. At last, rigorous security proofs and efficiency analysis are given, which justify that our proposed schemes are secure and efficient, satisfying the requirements of general verifiable computation protocols.

Position-based cryptography by means of quantum and classical schemes, using multiple-valued logic computing

Quantum and classical schemes of position-based cryprography are to transfer confidential message to an abonent with localised space position. Known verification proceedures are based on time-delay measurements of response signals and can`t provide unconditional security, as the eavesdropper can always cheat verifiers with the help of false signals. In order to raise the relative level of security, it is proposed to combine the quantum verification scheme by D.Unruh with multiple-valued logic methods.

A practical data-flow verification scheme for business processes

Data in business processes becomes more and more important. Current standard languages for process modeling like BPMN 2.0 which include the data flow reflect this. Ensuring the correctness of the data flow in processes is challenging. Model checking, i.e., verifying properties of process models, is a well-known technique to this end. An important part of model checking is the construction of the state space of the model. However, state-space explosion typically is in the way of an effective verification. We study how to overcome this problem in our context by means of reduction. More specifically, we propose a reduction on the level of the process model. To our knowledge, this is new for the data-flow analysis of processes. The core of our approach are so-called regions of the process model that are relevant for the verification of properties describing the data flow. Non-relevant regions are candidates for reduction of the process model, yielding a smaller state space. Our evaluation shows that our approach works well on industrial process models.

RESIDUAL HEAT POWER REMOVAL FROM SPENT NUCLEAR FUEL DURING DRY AND WET STORAGE

This project deals with the thermal analyses of the wet and dry storages of the spent nuclear fuel. The dry spent fuel storage sub-channel code COBRA-SFS has been used in order to calculate the temperature field. In this code, the new model of residual heat removal was created for the SKODA 1000/19 cask where the spent nuclear fuel TVSA-T type from NPP Temelin will be stored. The object of calculations was to obtain the inside temperatures under maximum loads. After that, the results were compared to the requirements of the local regulatory body. Because of the absence of experimental data, the validation of the created computational models could not be accomplished. However, according to the verification scheme of the COBRA-SFS authors, the verification of the new models was implemented.

An Effective Integrity Verification Scheme of Cloud Data Based on BLS Signature

Cloud storage services allow users to outsource their data remotely to save their local storage space and enable them to manage resources on demand. However, once users outsourced their data to the remote cloud platform, they lose the physical control of the data. How to ensure the integrity of outsourced data is the major concern of cloud users and also is the main challenge in the cloud service deployment. Limited by the communication and computation overheads, traditional hash-based integrity verification solutions in the stand-alone systems cannot be directly adopted in remote cloud storing environment. In this paper, we improve the previous privacy preserving model and propose an effective integrity verification scheme of cloud data based on BLS signature (EoCo), which ensures public audition and data privacy preserving. In addition, EoCo also supports batch auditing operations. We conducted theoretical analysis of our scheme, demonstrated its correctness and security properties, and evaluated the system performance as well.

Mitigating Content Caching Attack in NDN

Content caching is a core component in Named Data Networking (NDN), where content is cached in routers and served for future requests. However, the adversary can launch verification attack by placing poisoned data into the network with a legitimate name and allow the routers in the delivery path to frequently identify the content. Since NDN employs digital signature on each piece of content, verifying all content will exhaust routers’ computational resources due to the massive data in the network. In this paper, we propose a selective verification scheme upon the contents that are hit in the content store and allow the contents that are not actually served to be unverified. We also consider the redundant verification of popular content and incorporated in our design to lessen the re-accessing overhead. Analysis and performance results show that the proposed scheme can greatly mitigate the risk of content verification attacks and save the computational resources of relay nodes.

Comments on “A Large-Scale Concurrent Data Anonymous Batch Verification Scheme for Mobile Healthcare Crowd Sensing”

As an important application of the Internet of Things technologies, mobile healthcare crowd sensing (MHCS) still has challenging issues, such as privacy protection and efficiency. Quite recently in the IEEE Internet of Things Journal (DOI: 10.1109/JIOT.2018.2828463), Liu et al. proposed a large-scale concurrent data anonymous batch verification scheme for MHCS, claiming to provide batch authentication, nonrepudiation, and anonymity. However, after a close look at the scheme, we point out that the scheme suffers two types of signature forgery attacks and hence fails to achieve the claimed security properties. In addition, a reasonable and rigorous probability analysis indicates that the security reduction from the security of the scheme to the hardness of the computational Diffie–Hellman problem is invalid. We hope that similar design flaws can be avoided in future design of anonymous batch verification schemes for MHCS.

A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification

In spite of the benefits of biometric-based authentication systems, there are few concerns raised because of the sensitivity of biometric data to outliers, low performance caused due to intra-class variations and privacy invasion caused by information leakage. To address these issues, we propose a hybrid fusion framework where only the protected modalities are combined to fulfill the requirement of secrecy and performance improvement. This paper presents a method to integrate cancelable modalities utilizing mean-closure weighting (MCW) score level and Dempster-Shafer (DS) theory based decision level fusion for iris and fingerprint to mitigate the limitations in the individual score or decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the similarity scores from different matchers corresponding to each protected modality. The individual scores obtained from different matchers for each modality are combined using MCW score fusion method. The MCW technique achieves the optimal weight for each matcher involved in the score computation. Further, DS theory is applied to the induced scores to output the final decision. The rigorous experimental evaluations on three virtual databases indicate that the proposed hybrid fusion framework outperforms over the component level or individual fusion methods (score level and decision level fusion). As a result, we achieve (48%,66%), (72%,86%) and (49%,38%) of performance improvement over unimodal cancelable iris and unimodal cancelable fingerprint verification systems for Virtual_A, Virtual_B and Virtual_C databases, respectively. Also, the proposed method is robust enough to the variability of scores and outliers satisfying the requirement of secure authentication.

An efficient $$\mathcal{iO}$$ i O -based data integrity verification scheme for cloud storage

An efficient $$\mathcal{iO}$$ i O -based data integrity verification scheme for cloud storage

Blockchain-Based Verification Scheme for Deletion Operation in Cloud

Blockchain-Based Verification Scheme for Deletion Operation in Cloud

DoS-resilient cooperative beacon verification for vehicular communication systems

Authenticated safety beacons in Vehicular Communication (VC) systems ensure awareness among neighboring vehicles. However, the verification of beacon signatures introduces significant processing overhead for resource-constrained vehicular On-Board Units (OBUs). Even worse in dense neighborhood or when a clogging Denial of Service (DoS) attack is mounted. The OBU would fail to verify for all received (authentic or fictitious) beacons. This could significantly delay the verifications of authentic beacons or even affect the awareness of neighboring vehicle status. In this paper, we propose an efficient cooperative beacon verification scheme leveraging efficient symmetric key based authentication on top of pseudonymous authentication (based on traditional public key cryptography), providing efficient discovery of authentic beacons among a pool of received authentic and fictitious beacons, and can significantly decrease waiting times of beacons in queue before their validations. We show with simulation results that our scheme can guarantee low waiting times for received beacons even in high neighbor density situations and under DoS attacks, under which a traditional scheme would not be workable.

Cancelable Fingerprint Features Using Chaff Points Encapsulation

Recently, Biometrics imaging is widely used in several security areas such as security monitoring, database access, border control and immigration, and for reliable personal verification, identification and recognition schemes. To determine or confirm the identity of an individual's based on their physiological and/or behavioral characteristics, biometric features must be used. The aim of this paper is to review cancelable biometric generation and protection schemes. An approach for generating chaff points for fingerprint template features encapsulation as fingerprint cancelability infrastructure has been presented. Results show that strong positive correlation of original minutiae scores go with high decapsulated minutiae scores. To test the given cancelable approach performance two indexes are used, FAR (false accept rate) and FRR (false reject rate).

Online handwritten signature verification using feature weighting algorithm relief

Online handwritten signatures are widely used as a reliable identity authentication technology in industries such as banking, insurance and hospitals. Most existing online handwritten signature verification schemes usually choose the same feature sets for different users while comparing different signatures, which may reduce the stability of the dynamic characteristics. To avoid this problem, we propose a novel writer-dependent online signature verification technique based on Relief. For each user, our verification scheme falls into two phases: the training phase and the test phase. In the training phase, we select a signature as the base signature from real signatures and construct two different kinds of signature pairs: the real signature pairs and the forged signature pairs. Each pair of real signatures consists of two real signatures, and each pair of forgeries is made up of a real signature and a forgery. A group of combined features are defined for each pair of signatures. Then, we select more stable combined features by means of the Relief algorithm and compute a matrix used as a matching template, which contains the value of each combined feature for each pair of signatures. In the test phase, the classification of the pair of signatures composed of the test signature and the base reference signature is determined by the K-nearest neighbor. Experiments are conducted on our own database and SVC2004, and the results indicate that the method we propose here shows lower FAR and FRR than do traditional methods on the same databases.

SC-RANSAC: Spatial consistency on RANSAC

The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which utilizes spatial relations between extracted corresponding points in two images. It can also be considered as a preprocessing step for RANSAC to improve the accuracy as well as the runtime of RANSAC in estimating the parameters of a geometric model (such as fundamental and homography matrices). Obviously, like almost all previous works for enhancing RANSAC's runtime, the proposed method does not use heavy and compilicated processes. Performance analysis is performed on a variety of standard challenging datasets for estimating the homography and fundamental matrix (as an applicable case used in the literature, especially in the state-of-the-art methods). The performance is also compared quantitatively to RANSAC, PROSAC, and SCRAMSAC robust estimators to demonstrate its superiority. Experimental results show that the proposed method removes about 50% of outliers in most cases and hence extremely reduces the required runtime of RANSAC, while improving its accuracy.