Biblio

From the past few years cloud services are so popular and are being used by many people from various domains for various purposes such as data storage, e-mails, backing up data and much more. While there were many options to perform such things why did people choose cloud? The answer is clouds are more flexible, convenient, reliable and efficient. Coming to security of data over cloud, it is secure to store data over cloud rather than storing data locally as there is chance of some computer breakdown or any natural disaster may also occur. There are also many threats for data security over cloud namely data breaching, lack of access-key management and much more. As the data has been processed and being stored online for various purposes, there is a clear requirement for data security. Many organizations face various challenges while storing their data over cloud such as data leakages, account hijacking, insufficient credentials and so on. So to overcome these challenges and safeguard the data, various encryption techniques were implemented. However, even though encryption is used, the data still needs to be decrypted in order to do any type of operation. As a result, we must choose a manner in which the data can be analyzed, searched for, or used in any other way without needing to be decoded. So, the objective is to introduce a technique that goes right for the above conditions mentioned and for data security over cloud.

Healthcare has become one of the most important aspects of people’s lives, resulting in a surge in medical big data. Healthcare providers are increasingly using Internet of Things (IoT)-based wearable technologies to speed up diagnosis and treatment. In recent years, Through the Internet, billions of sensors, gadgets, and vehicles have been connected. One such example is for the treatment and care of patients, technology—remote patient monitoring—is already commonplace. However, these technologies also offer serious privacy and data security problems. Data transactions are transferred and logged. These medical data security and privacy issues might ensue from a pause in therapy, putting the patient’s life in jeopardy. We planned a framework to manage and analyse healthcare large data in a safe manner based on blockchain. Our model’s enhanced privacy and security characteristics are based on data sanitization and restoration techniques. The framework shown here make data and transactions more secure.

DDoS attacks, one of the oldest forms of cyberthreats, continue to be a favorite tool of mass interruption, presenting cybersecurity hazards to practically every type of company, large and small. As a matter of fact, according to IDC, DDoS attacks are predicted to expand at an 18 percent compound annual growth rate (CAGR) through 2023, indicating that it is past time to enhance investment in strong mitigation systems. And while some firms may assume they are limited targets for a DDoS assault, the amount of structured internet access to power corporation services and apps exposes everyone to downtime and poor performance if the infrastructure is not protected against such attacks. We propose using correlations between missing packets to increase detection accuracy. Furthermore, to ensure that these correlations are calculated correctly.

Cloud provides access to shared pool of resources like storage, networking, and processing. Distributed denial of service attacks are dangerous for Cloud services because they mainly target the availability of resources. It is important to detect and prevent a DDoS attack for the continuity of Cloud services. In this review, we analyze the different mechanisms of detection and prevention of the DDoS attacks in Clouds. We identify the major DDoS attacks in Clouds and compare the frequently-used strategies to detect, prevent, and mitigate those attacks that will help the future researchers in this area.

Intelligent, smart, Cloud, reconfigurable manufac-turing, and remote monitoring, all intersect in modern industry and mark the path toward more efficient, effective, and sustain-able factories. Many obstacles are found along the path, including legacy machineries and technologies, security issues, and software that is often hard, slow, and expensive to adapt to face unforeseen challenges and needs in this fast-changing ecosystem. Light-weight, portable, loosely coupled, easily monitored, variegated software components, supporting Edge, Fog and Cloud computing, that can be (re)created, (re)configured and operated from remote through Web requests in a matter of milliseconds, and that rely on libraries of ready-to-use tasks also extendable from remote through sub-second Web requests, constitute a fertile technological ground on top of which fourth-generation industries can be built. In this demo it will be shown how starting from a completely virgin Docker Engine, it is possible to build, configure, destroy, rebuild, operate, exclusively from remote, exclusively via API calls, computation networks that are capable to (i) raise alerts based on configured thresholds or trained ML models, (ii) transform Big Data streams, (iii) produce and persist Big Datasets on the Cloud, (iv) train and persist ML models on the Cloud, (v) use trained models for one-shot or stream predictions, (vi) produce tabular visualizations, line plots, pie charts, histograms, at real-time, from Big Data streams. Also, it will be shown how easily such computation networks can be upgraded with new functionalities at real-time, from remote, via API calls.

Cloud computing has become an integral part of medical big data. The cloud has the capability to store the large data volumes has attracted more attention. The integrity and privacy of patient data are some of the issues that cloud-based medical big data should be addressed. This research work introduces data integrity auditing scheme for cloud-based medical big data. This will help minimize the risk of unauthorized access to the data. Multiple copies of the data are stored to ensure that it can be recovered quickly in case of damage. This scheme can also be used to enable doctors to easily track the changes in patients' conditions through a data block. The simulation results proved the effectiveness of the proposed scheme.

Securing cloud configurations is an elusive task, which is left up to system administrators who have to base their decisions on "trial and error" experimentations or by observing good practices (e.g., CIS Benchmarks). We propose a knowledge, AND/OR, graphs approach to model cloud deployment security objects and vulnerabilities. In this way, we can capture relationships between configurations, permissions (e.g., CAP\_SYS\_ADMIN), and security profiles (e.g., AppArmor and SecComp). Such an approach allows us to suggest alternative and safer configurations, support administrators in the study of what-if scenarios, and scale the analysis to large scale deployments. We present an initial validation and illustrate the approach with three real vulnerabilities from known sources.

Any decentralized, biased distributed network is susceptible to the Sybil malicious attack, in which a malicious node masquerades as numerous different nodes, collectively referred to as Sybil nodes, causing the network to become unresponsive. Cloud computing environments are characterized by their loosely linked nature, which means that no node has comprehensive information of the entire system. In order to prevent Sybil attacks in cloud computing systems, it is necessary to detect them as soon as they occur. The network’s ability to function properly A Sybil attacker has the ability to construct. It is necessary to have multiple identities on a single physical device in order to execute a concerted attack on the network or switch between networks identities in order to make the detection process more difficult, and thereby lack of accountability is being promoted throughout the network. The purpose of this study is to Various varieties of Sybil assaults have been documented, including those that occur in Peer-to-peer reputation systems, self-organizing networks, and other similar technologies. The topic of social network systems is discussed. In addition, there are other approaches in which it has been urged over time that they be reduced or eliminated Their potential risks are also thoroughly investigated.

Distributed computation and AI processing at the edge has been identified as an efficient solution to deliver real-time IoT services and applications compared to cloud-based paradigms. These solutions are expected to support the delay-sensitive IoT applications, autonomic decision making, and smart service creation at the edge in comparison to traditional IoT solutions. However, existing solutions have limitations concerning distributed and simultaneous resource management for AI computation and data processing at the edge; concurrent and real-time application execution; and platform-independent deployment. Hence, first, we propose a novel three-layer architecture that facilitates the above service requirements. Then we have developed a novel platform and relevant modules with integrated AI processing and edge computer paradigms considering issues related to scalability, heterogeneity, security, and interoperability of IoT services. Further, each component is designed to handle the control signals, data flows, microservice orchestration, and resource composition to match with the IoT application requirements. Finally, the effectiveness of the proposed platform is tested and have been verified.

Internet of Things (IoT), Cloud and Augmented Reality (AR) are the emerging and developing technologies and are at the horizon and hype of their life cycle. Lots of commercial applications based on IoT, cloud and AR provide unrestricted access to data. The real-time applications based on these technologies are at the cusp of their innovations. The most frequent security attacks for IoT, cloud and AR applications are DDoS attacks. In this paper a detailed account of various DDoS attacks that can be the hindrance of many important sensitive services and can degrade the overall performance of recent services which are purely based on network communications. The DDoS attacks should be dealt with carefully and a set of a new generations of algorithm need to be developed to mitigate the problems caused by non-repudiation kinds of attacks.

The fifth-generation (5G) mobile telecom network has been garnering interest in both academia and industry, with better flexibility and higher performance compared to previous generations. Along with functionality improvements, new attack vectors also made way. Network operators and regulatory organizations wish to have a more precise idea about the security posture of 5G environments. Meanwhile, various security metrics for IT environments have been around and attracted the community’s attention. However, 5G-specific factors are less taken into consideration.This paper considers such 5G-specific factors to identify potential gaps if existing security metrics are to be applied to the 5G environments. In light of the layered nature and multi-ownership, the paper proposes a new approach to the modular computation of security metrics based on cross-layer projection as a means of information sharing between layers. Finally, the proposed approach is evaluated through simulation.

This paper presents an implementation of a novel approach, utilizing hybrid data partitioning, to secure sensitive data and improve query performance. In this novel approach, vertical and horizontal data partitioning are combined together in an approach that called hybrid partitioning and the new approach is implemented using Microsoft SQL server to generate divided/partitioned relations. A group of proposed rules is applied to the query request process using query binning (QB) and Metadata of partitioning. The proposed approach is validated using experiments involving a collection of data evaluated by outcomes of advanced stored procedures. The suggested approach results are satisfactory in achieving the properties of defining the data security: non-linkability and indistinguishability. The results of the proposed approach were satisfactory. The proposed novel approach outperforms a well-known approach called PANDA.

The era of big data continues to progress, and many new practices and applications are being advanced. One such application is big data in healthcare. In this application, big data, which includes patient information and measurements, must be transmitted and managed in smart and secure ways. In this study, we propose a novel big data cloud system, s2Cloud, standing for Smart and Secure Cloud. s2Cloud can enable health care systems to improve patient monitoring and help doctors gain crucial insights into their patients' health. This system provides an interactive website that allows doctors to effectively manage patients and patient records. Furthermore, both real-time and historical functions for big data management are supported. These functions provide visualizations of patient measurements and also allow for historic data retrieval so further analysis can be conducted. The security is achieved by protecting access and transmission of data via sign up and log in portals. Overall, the proposed s2Cloud system can effectively manage healthcare big data applications. This study will also help to advance other big data applications such as smart home and smart world big data practices.

Recent work on intrusion-tolerance has shown that resilience to sophisticated network attacks requires system replicas to be deployed across at least three geographically distributed sites. While commodity data centers offer an attractive solution for hosting these sites due to low cost and management overhead, their use raises significant confidentiality concerns: system operators may not want private data or proprietary algorithms exposed to servers outside their direct control. We present a new model for Byzantine Fault Tolerant replicated systems that moves toward “intrusion tolerance as a service”. Under this model, application logic and data are only exposed to servers hosted on the system operator's premises. Additional offsite servers hosted in data centers can support the needed resilience without executing application logic or accessing unencrypted state. We have implemented this approach in the open-source Spire system, and our evaluation shows that the performance overhead of providing confidentiality can be less than 4% in terms of latency.

Vehicles play an integral part in the life of a human being by facilitating in everyday tasks. The major concern that arises with this fact is that the rate of vehicle thefts have increased exponentially and retrieving them becomes almost impossible as the responsible party completely alters the stolen vehicles, leaving them untraceable. Ultimately, tracking and monitoring of vehicles using on-vehicle sensors is a promising and an efficient solution. The Internet of Things (IoT) is expected to play a vital role in revolutionizing the Security and Safety industry through a system of sensor networks by periodically sending the data from the sensors to the cloud for storage, from where it can be accessed to view or take any necessary actions (if required). The main contributions of this paper are the implementation and results of the prototype of a vehicle tracking and monitoring system. The system comprises of an Arduino UNO board connected to the Global Positioning System (GPS) module, Neo-6M, which senses the exact location of the vehicle in the form of latitude and longitude, and the ESP8266 Wi-Fi module, which sends the data to the Application Programming Interface (API) Cloud service, ThingSpeak, for storage and analyzing. An Android based mobile application is developed that utilizes the stored data from the Cloud and presents the user with the findings. Results show that the prototype is not only simple and cost effective, but also efficient and can be readily used by everyone from all walks of life to protect their vehicles.

After the emergence of the cloud architecture, many companies migrate their data from conventional storage i.e., on bare metal to the cloud storage. Since then huge amount of data was stored on cloud servers, which later resulted in redundancy of huge amount of data. Hence in this cloud world, many data de-duplication techniques has been widely used. Not only the redundancy but also made data more secure and privacy of the existing data were also increased. Some techniques got limitations and some have their own advantages based on the requirements. Some of the attributes like data privacy, tag regularity and interruption to brute-force attacks. To make data deduplication technique more efficient based on the requirements. This paper will discuss schemes that brace user-defined access control, by allowing the service provider to get information of the information owners. Thus our scheme eliminates redundancy of the data without breaching the privacy and security of clients that depends on service providers. Our lastest deduplication scheme after performing various algorithms resulted in conclusion and producing more efficient data confidentiality and tag consistency. This paper has discussion on various techniques and their drawbacks for the effectiveness of the deduplication.

Cloud computing has changed how humans use their technological expertise. It indicates a transition in the use of computers as utilitarian instruments with radical applications in general. However, as technology advances, the number of hazards increases and crucial data protection has become increasingly challenging due to extensive internet use. Every day, new encryption methods are developed, and much research is carried out in the search for a reliable cryptographic algorithm. The AES algorithm employs an overly simplistic algebraic structure. Each block employs the same encryption scheme, and AES is subject to brute force and MITM attacks. AES have not provide d sufficient levels of security; the re is still a need to put further le vels of protection over them. In this regard, DNA cryptography allows you to encrypt a large quantity of data using only a few amount of DNA. This paper combines two methodologies, a DNA-based algorithm and the AES Algorithm, to provide a consi derably more secure data security platform. The DNA cryptography technology and the AES approach are utilized for data encryption and decryption. To improve cloud security, DNA cryptography and AES provide a technologically ideal option.

IT world is migrating towards utilizing cloud computing as an essential data storing and exchanging platform. With the amelioration of technology, a colossal amount of data is generating with time. Cloud computing provides an enormous data storage capacity with the flexibility of accessing it without the time and place restrictions with virtualized resources. Healthcare industries spawn intense amounts of data from various medical instruments and digital records of patients. To access data remotely from any geographical location, the healthcare industry is moving towards cloud computing. EHR and PHR are patient's digital records, which include sensitive information of patients. Apart from all the proficient service provided by cloud computing, security is a primary concern for various organizations. To address the security issue, several cryptographic techniques implemented by researchers worldwide. In this paper, a vigorous cryptographic method discussed which is implemented by combining DNA cryptography and Elliptic Curve Cryptography to protect sensitive data in the cloud.

DDoS attacks have grown in popularity as a tactic for potential hackers, cyber blackmailers, and cyberpunks. These attacks have the potential to put a person unconscious in a matter of seconds, resulting in severe economic losses. Despite the vast range of conventional mitigation techniques available today, DDoS assaults are still happening to grow in frequency, volume, and intensity. A new network paradigm is necessary to meet the requirements of today's tough security issues. We examine the available detection and mitigation of DDoS attacks techniques in depth. We classify solutions based on detection of DDoS attacks methodologies and define the prerequisites for a feasible solution. We present a novel methodology named D3 for detecting and mitigating DDoS attacks using cuckoo filter.

In light of the progress achieved by the technology sector in the areas of internet speed and cloud services development, and in addition to other advantages provided by the cloud such as reliability and easy access from anywhere and anytime, most data owners find an opportunity to take advantage of the cloud to store data. However, data owners find a challenge that was and is still facing them in the field of outsourcing, which is protecting sensitive data from leakage. Researchers found that partitioning data into partitions, based on data sensitivity, can be used to protect data from leakage and to increase performance by storing the partition, which contains sensitive data in an encrypted form. In this paper, we review the methods used in designing partitions and dividing data approaches. A hybrid data partitioning approach is proposed to improve these techniques. We consider the frequency attack types used to guess the sensitive data and the most important properties that must be available in order for the encryption to be strong against frequency attacks.

The Transient Public Key Infrastructure or T-PKI is introduced in this paper that allows a transactional approach to attestation, where a Trusted Platform Module (TPM) can stay anonymous to a verifier. In cloud computing and IoT environments, attestation is a critical step in ensuring that the environment is untampered with. With attestation, the verifier would be able to ascertain information about the TPM (such as location, or other system information) that one may not want to disclose. The addition of the Direct Anonymous Attestation added to TPM 2.0 would potentially solve this problem, but it uses the traditional RSA or ECC based methods. In this paper, a Lattice-based approach is used that is both quantum safe, and not dependent on creating a new key pair in order to increase anonymity.

Digitalisation of domains such as medical and railway utilising cloud and networking technologies such as 5G and forthcoming 6G systems presents additional security challenges. The establishment of the identity, integrity and provenance of devices, services and other functional components removed a number of attack vectors and addresses a number of so called zero-trust security requirements. The addition of trusted hardware, such as TPM, and related remote attestation integrated with the networking and cloud infrastructure will be necessary requirement.

Practical activities usually require the ability to simultaneously work with internal, distributed information resources and access to the Internet. The need to solve this problem necessitates the use of appropriate administrative and technical methods to protect information. Such methods relate to the idea of domain isolation. This paper considers the principles of implementation and properties of an "Endpoint Cloud Terminal" that is general-purpose software tool with built-in security instruments. This apparatus solves the problem by combining an arbitrary number of isolated and independent workplaces on one hardware unit, a personal computer.

Cloud computing provides an open architecture and resource sharing computing platform with pay-per-use model. It is now a popular computing platform and most of the new internet based computing services are on this innovation supported environment. We consider it as innovation supported because developers are more focused here on the service design, rather on arranging the infrastructure, network, management of the resources, etc. These all things are available in cloud computing on hired basis. Now, a big question arises here is the security of data or privacy of data because the service provider is already using the infrastructure, network, storage, processors, and other more resources from the third party. So, the security or privacy of the portable user's data is the main motivation for writing this research paper. In this paper, we are proposing an innovative perturbation algorithm MAP() to secure the portable user's data on the cloud server.

With almost unlimited storage capacity and low maintenance cost, cloud storage becomes a convenient and efficient way for data sharing among cloud users. However, this introduces the challenges of access control and privacy protection when data sharing for multiple groups, as each group usually has its own encryption and access control mechanism to protect data confidentiality. In this paper, we propose a multiple-group data sharing scheme with privacy preservation in the cloud. This scheme constructs a flexible access control framework by using group signature, ciphertext-policy attribute-based encryption and broadcast encryption, which supports both intra-group and cross-group data sharing with anonymous access. Furthermore, our scheme supports efficient user revocation. The security and efficiency of the scheme are proved thorough analysis and experiments.