Biblio

To achieve a fine-grained access control function and guarantee the data confidentiality in the cloud storage environment, ciphertext policy attribute-based encryption (CP-ABE) has been widely implemented. However, due to the high computation and communication overhead, the nature of CP-ABE mechanism makes it difficult to be adopted in resource constrained terminals. Furthermore, the way of realizing varying levels of undo operations remains a problem. To this end, the access matrix that satisfies linear secret sharing scheme (LSSS) was optimized with Cauchy matrix, and then a user-level revocation scheme based on Chinese Remainder Theorem was proposed. Additionally, the attribute level revocation scheme which is based on the method of key encrypt key (KEK) and can help to reduce the storage overhead has also been improved.

Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.

Recommendation systems, especially collaborative filtering recommenders, are vulnerable to shilling attacks as some profit-driven users may inject fake profiles into the system to alter recommendation outputs. Current shilling attack detection methods are mostly based on feature extraction techniques. The hand-designed features can confine the model to specific domains or datasets while deep learning techniques enable us to derive deeper level features, enhance detection performance, and generalize the solution on various datasets and domains. This paper illustrates the application of two deep learning methods to detect shilling attacks. We conducted experiments on the MovieLens 100K and Netflix Dataset with different levels of attacks and types. Experimental results show that deep learning models can achieve an accuracy of up to 99%.

The present study proposes a method for detecting objects with a high recall rate for human-supported video annotation. In recent years, automatic annotation techniques such as object detection and tracking have become more powerful; however, detection and tracking of occluded objects, small objects, and blurred objects are still difficult. In order to annotate such objects, manual annotation is inevitably required. For this reason, we envision a human-supported video annotation framework in which over-detected objects (i.e., false positives) are allowed to minimize oversight (i.e., false negatives) in automatic annotation and then the over-detected objects are removed manually. This study attempts to achieve human-in-the-loop object detection with an emphasis on suppressing the oversight for the former stage of processing in the aforementioned annotation framework: bi-directional deep SORT is proposed to reliably capture missed objects and annotation-free segment identification (AFSID) is proposed to identify video frames in which manual annotation is not required. These methods are reinforced each other, yielding an increase in the detection rate while reducing the burden of human intervention. Experimental comparisons using a pedestrian video dataset demonstrated that bi-directional deep SORT with AFSID was successful in capturing object candidates with a higher recall rate over the existing deep SORT while reducing the cost of manpower compared to manual annotation at regular intervals.

The advance of smart eHealthcare and cloud computing techniques has propelled an increasing number of healthcare centers to outsource their healthcare data to the cloud. Meanwhile, in order to preserve the privacy of the sensitive information, healthcare centers tend to encrypt the data before outsourcing them to the cloud. Although the data encryption technique can preserve the privacy of the data, it inevitably hinders the query functionalities over the outsourced data. Among all practical query functionalities, the similarity range query is one of the most popular ones. However, to our best knowledge, many existing studies on the similarity range query over outsourced data still suffer from the efficiency issue in the query process. Therefore, in this paper, aiming at improving the query efficiency, we propose an efficient privacy-preserving similarity range query scheme based on the precomputed distance technique. In specific, we first introduce a pre-computed distance based similarity range query (PreDSQ) algorithm, which can improve the query efficiency by precomputing some distances. Then, we propose our privacy-preserving similarity query scheme by applying an asymmetric scalar-product-preserving encryption technique to preserve the privacy of the PreDSQ algorithm. Both security analysis and performance evaluation are conducted, and the results show that our proposed scheme is efficient and can well preserve the privacy of data records and query requests.

Discovering the potential vulnerabilities in software plays a crucial role in ensuring the security of computer system. This paper proposes a method that can assist security auditors with the analysis of source code. When security auditors identify new vulnerabilities, our method can be adopted to make a list of recommendations that may have the same vulnerabilities for the security auditors. Our method relies on graph representation to automatically extract the mode of PDG(program dependence graph, a structure composed of control dependence and data dependence). Besides, it can be applied to the vulnerability extrapolation scenario, thus reducing the amount of audit code. We worked on an open-source vulnerability test set called Juliet. According to the evaluation results, the clustering effect produced is satisfactory, so that the feature vectors extracted by the Graph2Vec model are applied to labeling and supervised learning indicators are adopted to assess the model for its ability to extract features. On a total of 12,000 small data sets, the training score of the model can reach up to 99.2%, and the test score can reach a maximum of 85.2%. Finally, the recommendation effect of our work is verified as satisfactory.

This paper proposes a novel zero-forcing (ZF) beamforming strategy that can simultaneously cope with active and passive eavesdroppers (EDs) in visible light communication systems. A related optimization problem is formulated to maximize the signal-to-noise ratio (SNR) of the legitimate user (UE) while suppressing the SNR of active ED to zero and constraining the average SNR of passive EDs. The proposed beamforming directs the transmission along a particular eigenmode related to the null space of the active ED channel and the intensity of the passive ED point process. An inverse free preconditioned Krylov subspace projection method is used to find the eigenmode. The numerical results show that the proposed ZF beamforming scheme yields better performance relative to a traditional ZF beamforming scheme in the sense of increasing the SNR of the UE and reducing the secrecy outage probability.

Due to the extraordinary volume of business information, classy cyber-attacks pointing the networks of all enterprise have become more casual, with intruders trying to pierce vast into and grasp broader from the compromised network machines. The vital security essential is that field experts and the network administrators have a common terminology to share the attempt of intruders to invoke the system and to rapidly assist each other retort to all kind of threats. Given the enormous huge system traffic, traditional Machine Learning (ML) algorithms will provide ineffective predictions of the network anomaly. Thereby, a hybridized multi-model system can improve the accuracy of detecting the intrusion in the networks. In this manner, this article presents a novel approach Clustered Generalization oriented Ensemble Learning Model (CGELM) for predicting the network anomaly. The performance metrics of the anticipated approach are Detection Rate (DR) and False Predictive Rate (FPR) for the two heterogeneous data sets namely NSL-KDD and UGR'16. The proposed method provides 98.93% accuracy for DR and 0.14% of FPR against Decision Stump AdaBoost and Stacking Ensemble methods.

The discovery of adversarial examples poses a serious risk to the deep neural networks (DNN). By adding a subtle perturbation that is imperceptible to the human eye, a well-behaved DNN model can be easily fooled and completely change the prediction categories of the input samples. However, research on adversarial attacks in the field of modulation recognition mainly focuses on increasing the prediction error of the classifier, while ignores the importance of decreasing the perceptual invisibility of attack. Aiming at the task of DNNbased modulation recognition, this study designs the Fitting Difference as a metric to measure the perturbed waveforms and proposes a new method: the Nesterov Adam Iterative Method to generate adversarial examples. We show that the proposed algorithm not only exerts excellent white-box attacks but also can initiate attacks on a black-box model. Moreover, our method decreases the waveform perceptual invisibility of attacks to a certain degree, thereby reducing the risk of an attack being detected.

Fulcrum decoders can trade off between computational complexity and the number of received packets. This allows heterogeneous nodes to decode at different level of complexity in accordance with their computing power. Variations of Fulcrum codes, like dynamic sparsity and expansion packets (DSEP) have significantly reduced the encoders and decoders' complexity by using dynamic sparsity and expansion packets. However, limited effort had been done for recoders of Fulcrum codes and their variations, limiting their full potential when being deployed at multi-hop networks. In this paper, we investigate the drawback of the conventional Fulcrum recoding and introduce a novel recoding scheme for the family of Fulcrum codes by limiting the buffer size, and thus memory needs. Our evaluations indicate that DSEP recoding mechamism increases the recoding goodput by 50%, and reduces the decoding overhead by 60%-90% while maintaining high decoding goodput at receivers and small memory usage at recoders compared with the conventional Fulcrum recoding. This further reduces the resources needed for Fulcrum codes at the recoders.

In this paper, we present our test automation work applied on national e-health portal for residents in Norway which has over million monthly visits. The focus of the work is threefold: delegating automation tasks and increasing reusability of test artifacts; metrics for estimating efficiency when creating test artifacts and designing robust automated test cases. Delegating (part of) test automation tasks from technical specialist (e.g. programmer - expensive resource) to non-technical specialist (e.g. domain expert, functional tester) is carried out by transforming low level test artifacts into high level test artifacts. Such transformations not only reduce dependency on specialists with coding skills but also enables involving more stakeholders with domain knowledge into test automation. Furthermore, we propose simple metrics which are useful for estimating efficiency during such transformations. Examples of the new metrics are implementation creation efficiency and test creation efficiency. We describe how we design automated test cases in order to reduce the number of false positives and minimize code duplication in the presence of test data challenge (i.e. using same test data both for manual and automated testing). We have been using our test automation solution for over three years. We successfully applied test automation on 2 out of 6 Scrum teams in Helsenorge. In total there are over 120 automated test cases with over 600 iterations (as of today).

The security of lattice-based cryptosystems that are secure for the post-quantum period is based on the difficulty of the shortest vector problem (SVP) and the closest vector problem (CVP). In the literature, many sieving algorithms are proposed to solve these hard problems. In this paper, efficient implementation of HashSieve sieving algorithm is discussed. A modular software library to have an efficient implementation of HashSieve algorithm is developed. Modular software library is used as an infrastructure in order for the HashSieve efficient implementation to be better than the sample in the literature (Laarhoven's standard HashSieve implementation). According to the experimental results, it is observed that HashSieve efficient implementation has a better running time than the example in the literature. It is concluded that both implementations are close to each other in terms of the memory space used.

Public key encryption with equality test (PKEET) enables the testing whether two ciphertexts encrypt the same message. Identity-based encryption with equality test (IBEET) simplify the certificate management of PKEET, which leads to many potential applications such as in smart city applications or Wireless Body Area Networks. Lee et al. (ePrint 2016) proposed a generic construction of IBEET scheme in the standard model utilising a 3-level hierachy IBE together with a one-time signature scheme, which can be instantiated in lattice setting. Duong et al. (ProvSec 2019) proposed the first direct construction of IBEET in standard model from lattices. However, their scheme achieve CPA security only. In this paper, we improve the Duong et al.'s construction by proposing an IBEET in standard model which achieves CCA2 security and with smaller ciphertext and public key size.

Anomaly detection (AD) is very important across several real-world problems in the heavy industries and Internet-of-Things (IoT) domains. Traditional methods so far have categorized anomaly detection into (a) unsupervised, (b) semi-supervised and (c) supervised techniques. A relatively unexplored direction is the development of context aware anomaly detection systems which can build on top of any of these three techniques by using side information. Context can be captured from a different modality such as semantic graphs encoding grouping of sensors governed by the physics of the asset. Process flow diagrams of an operational plant depicting causal relationships between sensors can also provide useful context for ML algorithms. Capturing such semantics by itself can be pretty challenging, however, our paper mainly focuses on, (a) designing and implementing effective anomaly detection pipelines using sparse Gaussian Graphical Models with various statistical distance metrics, and (b) differentiating these pipelines by embedding contextual semantics inferred from graphs so as to obtain better KPIs in practice. The motivation for the latter of these two has been explained above, and the former in particular is well motivated by the relatively mediocre performance of highly parametric deep learning methods for small tabular datasets (compared to images) such as IoT sensor data. In contrast to such traditional automated deep learning (AutoAI) techniques, our anomaly detection system is based on developing semantics-driven industry specific ML pipelines which perform scalable computation evaluating several models to identify the best model. We benchmark our AD method against state-of-the-art AD techniques on publicly available UCI datasets. We also conduct a case study on IoT sensor and semantic data procured from a large thermal energy asset to evaluate the importance of semantics in enhancing our pipelines. In addition, we also provide explainable insights for our model which provide a complete perspective to a reliability engineer.

Energy efficiency evaluation (EEE) is a research difficulty in the field of cloud computing. The current research mainly considers the relevant energy efficiency indicators of cloud systems and weights the interrelationship between energy consumption, system performance and QoS requirements. However, it lacks a combination of subjective and objective, qualitative and quantitative evaluation method to accurately evaluate the energy efficiency of cloud systems. We propose a novel EEE method based on the QoS parameter specification for cloud systems (EEE-QoS). Firstly, it reduces the metric values that affect QoS requirements to the same dimension range and then establishes a belief rule base (BRB). The best-worst method is utilized to determine the initial weights of the premise attributes in the BRB model. Then, the BRB model parameters are optimized by the mean-square error, the activation weight is calculated, and the activation rules of the evidence reasoning algorithm are integrated to evaluate the belief of the conclusion. The quantitative and qualitative evaluation of the energy efficiency of cloud systems is realized. The experiments show that the proposed method can accurately and objectively evaluate the energy efficiency of cloud systems.

Over the last few years, there has been an increasing number of studies about facial emotion recognition because of the importance and the impact that it has in the interaction of humans with computers. With the growing number of challenging datasets, the application of deep learning techniques have all become necessary. In this paper, we study the challenges of Emotion Recognition Datasets and we also try different parameters and architectures of the Conventional Neural Networks (CNNs) in order to detect the seven emotions in human faces, such as: anger, fear, disgust, contempt, happiness, sadness and surprise. We have chosen iCV MEFED (Multi-Emotion Facial Expression Dataset) as the main dataset for our study, which is relatively new, interesting and very challenging.

This paper presents a new algorithm for image encryption by extending the Knight's Tour Problem (KTP). The idea behind the proposed algorithm is to generate a Knight Tour (KT) matrix (m,n) and then divide the image according to the size of knight tour matrix into several sub matrices. Finally, apply n-neighborhood addition modulo encryption algorithm according to the solution of KT matrix over each m × n partition of the image. The proposed algorithm provides image encryption without using the cover images. Results obtained from experiments have shown that the proposed algorithm is efficient, simple and does not disclose any information from encrypted image.

Recently, new perspective areas of chaotic encryption have evolved, including fuzzy logic encryption. The presented work proposes an image encryption system based on two chaotic mapping that uses fuzzy logic. The paper also presents numerical calculations of some parameters of statistical analysis, such as, histogram, entropy of information and correlation coefficient, which confirm the efficiency of the proposed algorithm.

Preserving medical data is of utmost importance to stake holders. There are not many laws in India about preservation, usability of patient records. When data is transmitted across the globe there are chances of data getting tampered intentionally or accidentally. Tampered data loses its authenticity for diagnostic purpose, research and various other reasons. This paper proposes an authenticity based ECDSA algorithm by signature verification to identify the tampering of medical image files and alerts by the rules of authenticity. The algorithm can be used by researchers, doctors or any other educated person in order to maintain the authenticity of the record. Presently it is applied on medical related image files like DICOM. However, it can support any other medical related image files and still preserve the authenticity.

The market landscape has undergone dramatic change because of globalization, shifting marketing conditions, cost pressure, increased competition, and volatility. Transforming the operation of businesses has been possible because of the astonishing speed at which technology has witnessed the change. The automotive industry is on the edge of a revolution. The increased customer expectations, changing ownership, self-driving vehicles and much more have led to the transformation of automobiles, applications, and services from artificial intelligence, sensors, RFID to big data analysis. Large automobiles industries have been emphasizing the collection of data to gain insight into customer's expectations, preferences, and budgets alongside competitor's policies. Statistical methods can be applied to historical data, which has been gathered from various authentic sources and can be used to identify the impact of fixed and variable marketing investments and support automakers to come up with a more effective, precise, and efficient approach to target customers. Proper analysis of supply chain data can disclose the weak links in the chain enabling to adopt timely countermeasures to minimize the adverse effects. In order to fully gain benefit from analytics, the collaboration of a detailed set of capabilities responsible for intersecting and integrating with multiple functions and teams across the business is required. The effective role played by big data analysis in the automobile industry has also been expanded in the research paper. The research paper discusses the scope and challenges of big data. The paper also elaborates on the working technology behind the concept of big data. The paper illustrates the working of MapReduce technology that executes in the back end and is responsible for performing data mining.

Safety and security of complex critical infrastructures is very important for economic, environmental and social reasons. The interdisciplinary and inter-system dependencies within these infrastructures introduce difficulties in the safety and security design. Late discovery of safety and security design weaknesses can lead to increased costs, additional system complexity, ineffective mitigation measures and delays to the deployment of the systems. Traditionally, safety and security assessments are handled using different methods and tools, although some concepts are very similar, by specialized experts in different disciplines and are performed at different system design life-cycle phases.The methodology proposed in this paper supports a concurrent safety and security Defense in Depth (DiD) assessment at an early design phase and it is designed to handle safety and security at a high level and not focus on specific practical technologies. It is assumed that regardless of the perceived level of security defenses in place, a determined (motivated, capable and/or well-funded) attacker can find a way to penetrate a layer of defense. While traditional security research focuses on removing vulnerabilities and increasing the difficulty to exploit weaknesses, our higher-level approach focuses on how the attacker's reach can be limited and to increase the system's capability for detection, identification, mitigation and tracking. The proposed method can assess basic safety and security DiD design principles like Redundancy, Physical separation, Functional isolation, Facility functions, Diversity, Defense lines/Facility and Computer Security zones, Safety classes/Security Levels, Safety divisions and physical gates/conduits (as defined by the International Atomic Energy Agency (IAEA) and international standards) concurrently and provide early feedback to the system engineer. A prototype tool is developed that can parse the exported project file of the interdisciplinary model. Based on a set of safety and security attributes, the tool is able to assess aspects of the safety and security DiD capabilities of the design. Its results can be used to identify errors, improve the design and cut costs before a formal human expert inspection. The tool is demonstrated on a case study of an early conceptual design of a complex system of a nuclear power plant.

The development of control systems of increased reliability is highly relevant due to their widespread introduction in various sectors of human activity, including those where failure of the control system can lead to serious or catastrophic consequences. The increase of the reliability of control systems is directly related with the reliability of control computers (so called intellectual centers) since the computer technology is the basis of modern control systems. One of the possible solutions to the development of highly reliable control computers is the practical implementation of the provisions of the theory of structural stability, which involves the practical solution of two main tasks - this is the task of functional adaptation and the preceding task of functional diagnostics. This article deals with the issues on the assessment of computational complexity of the implementation of the adaptation process of structural and sustainable control computer. The criteria of computational complexity are the characteristics of additionally attracted resources, such as the temporal characteristics of the adaptation process and the characteristics of the involved amount of memory resources of the control computer involved in the implementation of the adaptation process algorithms.

End-to-end encryption is now ubiquitous on the internet. By securing network communications with TLS, parties can insure that in-transit data remains inaccessible to collection and analysis. In the IoT domain however, end-to-end encryption can paradoxically decrease user privacy, as many IoT devices establish encrypted communications with the manufacturer's cloud backend. The content of these communications remains opaque to the user and in several occasions IoT devices have been discovered to exfiltrate private information (e.g., voice recordings) without user authorization. In this paper, we propose Inspection-Friendly TLS (IF-TLS), an IoT-oriented, TLS-based middleware protocol that preserves the encryption offered by TLS while allowing traffic analysis by middleboxes under the user's control. Differently from related efforts, IF-TLS is designed from the ground up for the IoT world, adding limited complexity on top of TLS and being fully controllable by the residential gateway. At the same time it provides flexibility, enabling the user to offload traffic analysis to either the gateway itself, or cloud-based middleboxes. We implemented a stable, Python-based prototype IF-TLS library; preliminary results show that performance overhead is limited and unlikely to affect quality-of-experience.

Over the years, a number of vulnerability scoring frameworks have been proposed to characterize the severity of known vulnerabilities in software-dependent systems. These frameworks provide security metrics to support decision-making in system development and security evaluation and assurance activities. When used in this context, it is imperative that these security metrics be sound, meaning that they can be consistently measured in a reproducible, objective, and unbiased fashion while providing contextually relevant, actionable information for decision makers. In this paper, we evaluate the soundness of the security metrics obtained via several vulnerability scoring frameworks. The evaluation is based on the Method for DesigningSound Security Metrics (MDSSM). We also present several recommendations to improve vulnerability scoring frameworks to yield more sound security metrics to support the development of secure software-dependent systems.

Edge computing (EC) is a new developing computing technology where data are collected, and analysed nearer to the edge or sources of the data. Cloud to the edge, intelligent applications and analytics are part of the IoT applications and technology. Edge computing technology aims to bring cloud computing features near to edge devices. For time-sensitive applications in cloud computing, architecture massive volume of data is generated at the edge and stored and analysed in the cloud. Cloud infrastructure is a composition of data centres and large-scale networks, which provides reliable services to users. Traditional cloud computing is inefficient due to delay in response, network delay and congestion as simultaneous transactions to the cloud, which is a centralised system. This paper presents a literature review on cloud-based edge computing technologies for delay-sensitive applications and suggests a conceptual model of edge computing architecture. Further, the paper also presents the implementation of QoS support edge computing paradigm in Python for further research to improve the latency and throughput for time-sensitive applications.