Biblio

Shared register emulations on top of message-passing systems provide an illusion of a simpler shared memory system which can make the task of a system designer easier. Numerous shared register applications have a considerably high read-to-write ratio. Thus, having algorithms that make reads more efficient than writes is a fair trade-off.Typically, such algorithms for reads and writes are asymmetric and sacrifice the stringent consistency condition atomicity, as it is impossible to have fast reads for multi-writer atomicity. Safety is a consistency condition that has has gathered interest from both the systems and theory community as it is weaker than atomicity yet provides strong enough guarantees like "strong consistency" or read-my-write consistency. One requirement that is assumed by many researchers is that of the reliable broadcast (RB) primitive, which ensures the "all or none" property during a broadcast. One drawback is that such a primitive takes 1.5 rounds to complete and requires server-to-server communication.This paper implements an efficient multi-writer multi-reader safe register without using a reliable broadcast primitive. Moreover, we provide fast reads or one-shot reads – our read operations can be completed in one round of client-to-server communication. Of course, this comes with the price of requiring more servers when compared to prior solutions assuming reliable broadcast. However, we show that this increased number of servers is indeed necessary as we prove a tight bound on the number of servers required to implement Byzantine-fault tolerant safe registers in a system without reliable broadcast.We extend our results to data stored using erasure coding as well. We present an emulation of single-writer multi-reader safe register based on MDS codes. The usage of MDS codes reduces storage and communication costs. On the negative side, we also show that to use MDS codes and at the same time achieve one-shot reads, we need even more servers.

Various Machine Learning techniques are considered to be "black-boxes" because of their limited interpretability and explainability. This cannot be afforded, especially in the domain of Cyber-Physical Systems, where there can be huge losses of infrastructure of industries and Governments. Supervisory Control And Data Acquisition (SCADA) systems need to detect and be protected from cyber-attacks. Thus, we need to adopt approaches that make the system secure, can explain predictions made by model, and interpret the model in a human-understandable format. Recently, Autoencoders have shown great success in attack detection in SCADA systems. Numerous interpretable machine learning techniques are developed to help us explain and interpret models. The work presented here is a novel approach to use techniques like Local Interpretable Model-Agnostic Explanations (LIME) and Layer-wise Relevance Propagation (LRP) for interpretation of Autoencoder networks trained on a Gas Pipelines Control System to detect attacks in the system.

Cloud computing (CC) systems prevail to be the widespread computational paradigms for offering immense scalable and elastic services. Computing resources in cloud environment should be scheduled to facilitate the providers to utilize the resources moreover the users could get low cost applications. The most prominent need in job scheduling is to ensure Quality of service (QoS) to the user. In the boundary of the third party the scheduling takes place hence it is a significant condition for assuring its security. The main objective of our work is to offer QoS i.e. cost, makespan, minimized migration of task with security enforcement moreover the proposed algorithm guarantees that the admitted requests are executed without violating service level agreement (SLA). These objectives are attained by the proposed Fuzzy Ant Bee Colony algorithm. The experimental outcome confirms that secured job scheduling objective with assured QoS is attained by the proposed algorithm.

With the advancements in technology, the ease of interconnectedness among devices has increased manifold, leading to the widespread usage of Internet of Things. Internet of Things has also reached our homes, often referred to as domestic Internet of Things. However, the security aspect of domestic Internet of Things has largely been under question as the increase in inter-device communication renders the system more vulnerable to adversaries. Largely popular blockchain technology is being extensively researched for integration into the Internet of Things framework in order to improve the security aspect of the framework. Blockchain, being a cryptographically linked set of data, has a few barriers which prevent it from being successfully integrated to Internet of Things. One of the major barrier is the high computational requirements and time latency associated with it. This work tries to address this research gap and proposes a novel scalable blockchain optimization for domestic Internet of Things. The proposed blockchain model uses a flow based filtering technique as an added security layer to facilitate the scenario. This work then evaluates the performance of the proposed model in various scenarios and compares it with that of traditional blockchain. The work presents a largely encompassing evaluation, explanation and assessment of the proposed model.

Automated techniques for rigorous floating-point round-off error analysis are a prerequisite to placing important activities in HPC such as precision allocation, verification, and code optimization on a formal footing. Yet existing techniques cannot provide tight bounds for expressions beyond a few dozen operators-barely enough for HPC. In this work, we offer an approach embedded in a new tool called SATIHE that scales error analysis by four orders of magnitude compared to today's best-of-class tools. We explain how three key ideas underlying SATIHE helps it attain such scale: path strength reduction, bound optimization, and abstraction. SATIHE provides tight bounds and rigorous guarantees on significantly larger expressions with well over a hundred thousand operators, covering important examples including FFT, matrix multiplication, and PDE stencils.

Fault effect simulation is a well-established technique for the qualification of robust embedded software and hardware as required by different safety standards. Our article introduces a Virtual Prototype based approach for the fault analysis and fast simulation of a set of automatically generated and target compiled software programs. The approach scales to different RISC-V ISA standard subset configurations and is based on an instruction and hardware register coverage for automatic fault injections of permanent and transient bitflips. The analysis of each software binary evaluates its opcode type and register access coverage including the addressed memory space. Based on this information dedicated sets of fault injected hardware models, i.e., mutants, are generated. The simulation of all mutants conducted with the different binaries finally identifies the cases with a normal termination though executed on a faulty hardware model. They are identified as a subject for further investigations and improvements by the implementation of additional hardware or software safety countermeasures. Our final evaluation results with automatic C code generation, compilation, analysis, and simulation show that QEMU provides an adequate efficient platform, which also scales to more complex scenarios.

Named Data Networking (NDN) is a new kind of architecture for future Internet, which is exactly satisfied with the rapidly increasing mobile requirement and information-depended applications that dominate today's Internet. However, the current verification-data accessed system is not safe enough to prevent data leakage because no strongly method to resist any device or user to access it. We bring up a lightweight verification based on hash functions and a fine-grained access control based on Schnorr Signature to address the issue seamlessly. The proposed scheme is scalable and protect data confidentiality in a NDN network.

The key feature of NDN is in-network caching that every router has its cache to store data for future use, thus improve the usage of the network bandwidth and reduce the network latency. However, in-network caching increases the security risks - cache pollution attacks (CPA), which includes locality disruption (ruining the cache locality by sending random requests for unpopular contents to make them popular) and False Locality (introducing unpopular contents in the router's cache by sending requests for a set of unpopular contents). In this paper, we propose a machine learning method, named Triangle Area Based Multivariate Correlation Analysis (TAB-MCA) that detects the cache pollution attacks in NDN. This detection system has two parts, the triangle-area-based MCA technique, and the threshold-based anomaly detection technique. The TAB-MCA technique is used to extract hidden geometrical correlations between two distinct features for all possible permutations and the threshold-based anomaly detection technique. This technique helps our model to be able to distinguish attacks from legitimate traffic records without requiring prior knowledge. Our technique detects locality disruption, false locality, and combination of the two with high accuracy. Implementation of XC-topology, the proposed method shows high efficiency in mitigating these attacks. In comparison to other ML-methods, our proposed method has a low overhead cost in mitigating CPA as it doesn't require attackers' prior knowledge. Additionally, our method can also detect non-uniform attack distributions.

The purpose of the work was to study the fault- tolerant pattern of secure access of computer system nodes to external network resources - the pattern of secure access `Connecting node'. The pattern of secure access `Connecting node' includes a group/cluster (or several groups) of routers, a computing node that includes hardware and software for information protection and communication channels that connect it to the end nodes of the computing system and the external network (network resources that are not controlled by the information protection system). The efficiency assessment and comparative analysis of options for designing a pattern of secure access `Connecting node' according to various efficiency criteria were carried out. In this work, an assessment of the individual and comprehensive efficiency index was carried out. It was assumed that the system is recoverable. The effectiveness of using some options of designing a pattern of secure access in terms of the operational availability factor, as well as a group of parameters - the operational availability factor, service delays of information protection system and the grade of information protection.

This work considers the trade-off between security and performance when revealing partial information about encrypted data computed on. The focus of our work is on information revealed through control flow side-channels when executing programs on encrypted data. We use quantitative information flow to measure security, running time to measure performance and program transformation techniques to alter the trade-off between the two. Combined with information flow policies, we perform a policy-aware security and performance trade-off (PASAPTO) analysis. We formalize the problem of PASAPTO analysis as an optimization problem, prove the NP-hardness of the corresponding decision problem and present two algorithms solving it heuristically. We implemented our algorithms and combined them with the Dataflow Authentication (DFAuth) approach for outsourcing sensitive computations. Our DFAuth Trade-off Analyzer (DFATA) takes Java Bytecode operating on plaintext data and an associated information flow policy as input. It outputs semantically equivalent program variants operating on encrypted data which are policy-compliant and approximately Pareto-optimal with respect to leakage and performance. We evaluated DFATA in a commercial cloud environment using Java programs, e.g., a decision tree program performing machine learning on medical data. The decision tree variant with the worst performance is 357% slower than the fastest variant. Leakage varies between 0% and 17% of the input.

Development of quality object-oriented software contains security as an integral aspect of that process. During that process, a ceaseless burden on the developers was posed in order to maximize the development and at the same time to reduce the expense and time invested in security. In this paper, the authors analyzed metrics for object-oriented software in order to evaluate and identify the relation between metric value and security of the software. Identification of these relations was achieved by study of software vulnerabilities with code level metrics. By using OWASP classification of vulnerabilities and experimental results, we proved that there was relation between metric values and possible security issues in software. For experimental code analysis, we have developed special software called SOFTMET.

The usage of connected devices and their role within our daily- and business life gains more and more impact. In addition, various derivations of Cyber-Physical Systems (CPS) reach new business fields, like smart healthcare or Industry 4.0. Although these systems do bring many advantages for users by extending the overall functionality of existing systems, they come with several challenges, especially for system engineers and architects. One key challenge consists in achieving a sufficiently high level of security within the CPS environment, as sensitive data or safety-critical functions are often integral parts of CPS. Being system of systems (SoS), CPS complexity, unpredictability and heterogeneity complicate analyzing the overall level of security, as well as providing a way to detect ongoing attacks. Usually, security metrics and frameworks provide an effective tool to measure the level of security of a given component or system. Although several comprehensive surveys exist, an assessment of the effectiveness of the existing solutions for CPS environments is insufficiently investigated in literature. In this work, we address this gap by benchmarking a carefully selected variety of existing security metrics in terms of their usability for CPS. Accordingly, we pinpoint critical CPS challenges and qualitatively assess the effectiveness of the existing metrics for CPS systems.

IoT and big data are energetic technology of the world for quite a time, and both of these have become a necessity. On the one side where IoT is used to connect different objectives via the internet, the big data means having a large number of the set of structured, unstructured, and semi-structured data. The device used for processing based on the tools used. These tools help provide meaningful information used for effective management in different domains. Some of the commonly faced issues with the inadequate about the technologies are related to data privacy, insufficient analytical capabilities, and this issue is faced by in different domains related to the big data. Data analytics tools help discover the pattern of data and consumer preferences which is resulting in better decision making for the organizations. The major part of this work is to review different types of data analytics techniques for the effective management of big data using IoT. For the effective management of the ABD solution collection, analysis and control are used as the components. Each of the ingredients is described to find an effective way to manage big data. These components are considered and used in the validation criteria. The solution of effective data management is a stage towards the management of big data in IoT devices which will help the user to understand different types of elements of data management.

with the advent of Cloud Computing a new era of computing has come into existence. No doubt, there are numerous advantages associated with the Cloud Computing but, there is other side of the picture too. The challenges associated with it need a more promising reply as far as the security of data that is stored, in process and in transit is concerned. This paper put forth a cloud computing model that tries to answer the data security queries; we are talking about, in terms of the four cryptographic techniques namely Homomorphic Encryption (HE), Verifiable Computation (VC), Secure Multi-Party Computation (SMPC), Functional Encryption (FE). This paper takes into account the various cryptographic techniques to undertake cloud computing security issues. It also surveys these important (existing) cryptographic tools/techniques through a proposed Cloud computation model that can be used for Big Data applications. Further, these cryptographic tools are also taken into account in terms of CIA triad. Then, these tools/techniques are analyzed by comparing them on the basis of certain parameters of concern.

Data mining visualization is an important aspect of big data visualization and analysis. The impact of the nature-inspired algorithm along with the impact of computing traditions for the complete visualization of the storage and data communication needs have been studied. This paper also explores the possibilities of the hybridization of data mining in terms of association of cloud computing. It also explores the data analytical view in the exploration of these approaches in terms of data storage in big data. Based on these aspects the methodological advancement along with the problem statements has been analyzed. This will help in the exploration of computational capability along with the new insights in this domain.

Big data provides a way to handle and analyze large amount of data or complex set. It provides a systematic extraction also. In this paper a hybrid security analysis based on intelligent adaptive learning in big data has been discussed with the current trends. This paper also explores the possibility of cloud computing collaboration with big data. The advantages along with the impact for the overall platform evaluation has been discussed with the traditional trends. It has been useful in the analysis and the exploration of future research. This discussion also covers the computational variability and the connotation in terms of data reliability, availability and management in big data with data security aspects.

Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the back-ground knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This is important for the cybersecurity domain, especially for malware detection and mitigation, as not all problems have a single, known, correct answer. Often, security researchers have to resort to guided trial and error to understand the presence of a malware and mitigate it.In this paper, we incorporate prior knowledge, represented as Cybersecurity Knowledge Graphs (CKGs), to guide the exploration of an RL algorithm to detect malware. CKGs capture semantic relationships between cyber-entities, including that mined from open source. Instead of trying out random guesses and observing the change in the environment, we aim to take the help of verified knowledge about cyber-attack to guide our reinforcement learning algorithm to effectively identify ways to detect the presence of malicious filenames so that they can be deleted to mitigate a cyber-attack. We show that such a guided system outperforms a base RL system in detecting malware.

A metric and structure of computing 2020 is proposed in the form of Top 12 Technology Trends, which will influence on investment in science, education and industry in developing countries. The primary social and technological problem of the protection of society and critical facilities through the creation of Global Intelligent Cyber Security is formulated. The axioms of the constructive formation of developing countries on the basis of the adoption of moral relations are formulated. Models, methods and algorithms of cyber-social computing are proposed that are focused on processing big data, searching for keywords and test fragments. New characteristic equations of similarity - differences between the processes and phenomena are synthesized for the exact information retrieval by keywords in cyber-physical space. A computing model of the development of the Universe is formulated, where the binary interactions of entities and forms are harmonic functions of the phase state. A structure of interactive computing of the creative process based on a metric assessment of the development status with world achievements is proposed.

In the past decades, learning an effective distance metric between pairs of instances has played an important role in the classification and retrieval task, for example, the person identification or malware retrieval in the IoT service. The core motivation of recent efforts focus on improving the metric forms, and already showed promising results on the various applications. However, such models often fail to produce a reliable metric on the ambiguous test set. It happens mainly due to the sampling process of the training set, which is not representative of the distribution of the negative samples, especially the examples that are closer to the boundary of different categories (also called hard negative samples). In this paper, we focus on addressing such problems and propose an adaptive margin deep adversarial metric learning (AMDAML) framework. It exploits numerous common negative samples to generate potential hard (adversarial) negatives and applies them to facilitate robust metric learning. Apart from the previous approaches that typically depend on the search or data augmentation to find hard negative samples, the generation of adversarial negative instances could avoid the limitation of domain knowledge and constraint pairs' amount. Specifically, in order to prevent over fitting or underfitting during the training step, we propose an adaptive margin loss that preserves a flexible margin between the negative (include the adversarial and original) and positive samples. We simultaneously train both the adversarial negative generator and conventional metric objective in an adversarial manner and learn the feature representations that are more precise and robust. The experimental results on practical data sets clearly demonstrate the superiority of AMDAML to representative state-of-the-art metric learning models.

Data value (DV) is a novel concept that is introduced as one of the Big Data phenomenon features. While continuing an investigation of the DV ontology and its relationship with the data quality (DQ) on the conceptual level, this paper researches possible applications and use of the DV in the practical design of security and privacy protection systems and tools. We present a novel approach to DV evaluation that maps DQ metrics into DV value. Developed methods allow DV and DQ use in a wide range of application domains. To demonstrate DQ and DV concept employment in real tasks we present two real-life scenarios. The first use case demonstrates the DV use in crowdsensing application design. It shows up how DV can be calculated by integrating various metrics characterizing data application functionality, accuracy, and security. The second one incorporates the privacy consideration into DV calculus by exploring the relationship between privacy, DQ, and DV in the defense against web-site fingerprinting in The Onion Router (TOR) networks. These examples demonstrate how our methods of the DV and DQ evaluation may be employed in the design of real systems with security and privacy consideration.

Initially, legitimate users were working under a normal web browser to do all activities over the internet [1]. To get more secure service and to get protection against Bot activity, the legitimate users switched their activity from Normal web browser to low latency anonymous communication such as Tor Browser. The Traffic monitoring in Tor Network is difficult as the packets are traveling from source to destination in an encrypted fashion and the Tor network hides its identity from destination. But lately, even the illegitimate users such as attackers/criminals started their activity on the Tor browser. The secured Tor network makes the detection of Botnet more difficult. The existing tools for botnet detection became inefficient against Tor-based bots because of the features of the Tor browser. As the Tor Browser is highly secure and because of the ethical issues, doing practical experiments on it is not advisable which could affect the performance and functionality of the Tor browser. It may also affect the endanger users in situations where the failure of Tor's anonymity has severe consequences. So, in the proposed research work, Private Tor Networks (PTN) on physical or virtual machines with dedicated resources have been created along with Trusted Middle Node. The motivation behind the trusted middle node is to make the Private Tor network more efficient and to increase its performance.

The trend of rapid development of mobile technologies has highlighted new challenges and threats regarding the information security by the using cookies in mobile learning (m-learning) systems. In order to overcome these challenges and threats, this paper has identified two main objectives. First, to give a review of most common types to cookies and second, to consider the challenges and threats regarding cookies with aspects that are directly related to issues of security and privacy. With these objectives is possible to bridge security gaps in m-learning systems. Moreover, the identified potential challenges and threats are discussed with the given proposals of pragmatic solutions for their mitigating or reducing. The findings of this research may help students to rise security awareness and security behavior in m-learning systems, and to better understand on-going security challenges and threats in m-learning systems.

Mobile browsers have become one of the main mediators of our online activities. However, as web pages continue to increase in size and streaming media on-the-go has become commonplace, mobile data plan constraints remain a significant concern for users. As a result, data-saving features can be a differentiating factor when selecting a mobile browser. In this paper, we present a comprehensive exploration of the security and privacy threat that data-saving functionality presents to users. We conduct the first analysis of Android's data-saving browser (DSB) ecosystem across multiple dimensions, including the characteristics of the various browsers' infrastructure, their application and protocol-level behavior, and their effect on users' browsing experience. Our research unequivocally demonstrates that enabling data-saving functionality in major browsers results in significant degradation of the user's security posture by introducing severe vulnerabilities that are not otherwise present in the browser during normal operation. In summary, our experiments show that enabling data savings exposes users to (i) proxy servers running outdated software, (ii) man-in-the-middle attacks due to problematic validation of TLS certificates, (iii) weakened TLS cipher suite selection, (iv) lack of support of security headers like HSTS, and (v) a higher likelihood of being labelled as bots. While the discovered issues can be addressed, we argue that data-saving functionality presents inherent risks in an increasingly-encrypted Web, and users should be alerted of the critical savings-vs-security trade-off that they implicitly accept every time they enable such functionality.

The Global Demand for agriculture and dairy products is rising. Demand is expected to double by 2050. This will challenge agriculture markets in a way we have not seen before. For instance, unprecedented demand to increase in dairy farm productivity of already shrinking farms, untethered perpetual access to veterinarians by small dairy farms, economic engines of the developing countries, for animal husbandry and, finally, unprecedented need to increase productivity of veterinarians who're already understaffed, over-stressed, resource constrained to meet the current global dairy demands. The lack of innovative solutions to address the challenge would result in a major obstacle to achieve sustainable food future and a colossal roadblock ending economic disparities. The paper proposes a novel innovative data driven framework cropped by data generated using dairy Sensors and by mathematical formulations using Solvers to generate an exclusive veterinarian daily farms prioritized visit list so as to have a greater coverage of the most needed farms performed in-time and improve small farmers access to veterinarians, a precious and highly shortage & stressed resource.

Recently, the rapid development of Internet of things (IoT) has resulted in the generation of a considerable amount of data, which should be stored. Therefore, it is necessary to develop methods that can easily capture, save, and modify these data. The data generated using IoT contain private information; therefore sufficient security features should be incorporated to ensure that potential attackers cannot access the data. Researchers from various fields are attempting to achieve data security. One of the major challenges is that IoT is a paradigm of how each device in the Internet infrastructure is interconnected to a globally dynamic network. When searching in dynamic cloud-stored data, sensitive data can be easily leaked. IoT data storage and retrieval from untrusted cloud servers should be secure. Searchable symmetric encryption (SSE) is a vital technology in the field of cloud storage. SSE allows users to use keywords to search for data in an untrusted cloud server but the keywords and the data content are concealed from the server. However, an SSE database is seldom used by cloud operators because the data stored on the cloud server is often modified. The server cannot update the data without decryption because the data are encrypted by the user. Therefore, dynamic SSE (DSSE) has been developed in recent years to support the aforementioned requirements. Instead of decrypting the data stored by customers, DSSE adds or deletes encrypted data on the server. A number of DSSE systems based on linked list structures or blind storage (a new primitive) have been proposed. From the perspective of functionality, extensibility, and efficiency, these DSSE systems each have their own advantages and drawbacks. The most crucial aspect of a system that is used in the cloud industry is the trade-off between performance and security. Therefore, we compared the efficiency and security of multiple DSSE systems and identified their shortcomings to develop an improved system.