Biblio

The agricultural sector and other Micro, Small, and Medium Enterprises in India operate with more than 90% migrant workers searching for better employment opportunities far away from their native places. However, inherent challenges are far more for the migrant workers, most prominently their Identity. To the best of our knowledge, available literature lacks a comprehensive study on identity management components for user privacy and data protection mechanisms in identity management architecture. Self-Sovereign Identity is regarded as a new evolution in digital identity management systems. Blockchain technology and distributed ledgers bring us closer to realizing an ideal Self-Sovereign Identity system. This paper proposes a novel solution to address identity issues being faced by migrant workers. It also gives a holistic, coherent, and mutually beneficial Identity Management Solution for the migrant workforce in the Indian perspective towards e-Governance and Digital India.

Cloud computing plays major role in the development of accessing clouduser’s document and sensitive information stored. It has variety of content and representation. Cyber security and attacks in the cloud is a challenging aspect. Information security attains a vital part in Cyber Security management. It involves actions intended to reduce the adverse impacts of such incidents. To access the documents stored in cloud safely and securely, access control will be introduced based on cloud users to access the user’s document in the cloud. To achieve this, it is highly required to combine security components (e.g., Access Control, Usage Control) in the security document to get automatic information. This research work has proposed a Role Key Homomorphic Encryption Algorithm (RKHEA) to monitor the cloud users, who access the services continuously. This method provides access creation of session-based key to store the singularized encryption to reduce the key size from random methods to occupy memory space. It has some terms and conditions to be followed by the cloud users and also has encryption method to secure the document content. Hence the documents are encrypted with the RKHEA algorithm based on Service Key Access (SKA). Then, the encrypted key will be created based on access control conditions. The proposed analytics result shows an enhanced control over the documents in cloud and improved security performance.

The growing maturity of orchestration languages is contributing to the elaboration of cloud composite services, whose resources may be deployed over different distributed infrastructures. These composite services are subject to changes over time, that are typically required to support cloud properties, such as scalability and rapid elasticity. In particular, the migration of their elementary resources may be triggered by performance constraints. However, changes induced by this migration may introduce vulnerabilities that may compromise the resources, or even the whole cloud service. In that context, we propose an automated SMT1-based security framework for supporting the migration of resources in cloud composite services, and preventing the occurrence of new configuration vulnerabilities. We formalize the underlying security automation based on SMT solving, in order to assess the migrated resources and select adequate counter-measures, considering both endogenous and exogenous security mechanisms. We then evaluate its benefits and limits through large series of experiments based on a proof-of-concept prototype implemented over the CVC4 commonly-used open-source solver. These experiments show a minimal overhead with regular operating systems deployed in cloud environments.

A new Domain Name System (DNS) cache poisoning attack aiming at clients has emerged recently. It induced cloud users to visit fake web sites and thus reveal information such as account passwords. However, the design of current DNS defense architecture does not formally consider the protection of clients. Although the DNS traffic encryption technology can alleviate this new attack, its deployment is as slow as the new DNS architecture. Thus we propose a lightweight adaptive intelligent defense strategy, which only needs to be deployed on the client without any configuration support of DNS. Firstly, we model the attack and defense process as a static stochastic game with incomplete information under bounded rationality conditions. Secondly, to solve the problem caused by uncertain attack strategies and large quantities of game states, we adopt a deep reinforcement learning (DRL) with guaranteed monotonic improvement. Finally, through the prototype system experiment in Alibaba Cloud, the effectiveness of our method is proved against multiple attack modes with a success rate of 97.5% approximately.

In Cloud Computing, a wide range of virtual platforms are integrated and offer users a flexible pay-as-you-need service. Compared to conventional computing systems, the provision of an acceptable degree of resilience to cloud services is a daunting challenge due to the complexities of the cloud environment and the need for efficient technology that could sustain cloud advantages over other technologies. For a cloud guest resilience service solution, we provide architectural design, installation specifics, and performance outcomes throughout this article. Virtual Machine Manager (VMM) enables execution statistical test of the virtual machine states to be monitored and avoids to reach faulty states.

In this paper, we present the Cloud Property Graph (CloudPG), which bridges the gap between static code analysis and runtime security assessment of cloud services. The CloudPG is able to resolve data flows between cloud applications deployed on different resources, and contextualizes the graph with runtime information, such as encryption settings. To provide a vendorand technology-independent representation of a cloud service's security posture, the graph is based on an ontology of cloud resources, their functionalities and security features. We show, using an example, that our CloudPG framework can be used by security experts to identify weaknesses in their cloud deployments, spanning multiple vendors or technologies, such as AWS, Azure and Kubernetes. This includes misconfigurations, such as publicly accessible storages or undesired data flows within a cloud service, as restricted by regulations such as GDPR.

Cloud computing, supported by advancements in virtualisation and distributed computing, became the default options for implementing the IT infrastructure of organisations. Medical data and in particular medical images have increasing storage space and remote access requirements. Cloud computing satisfies these requirements but unclear safeguards on data security can expose sensitive data to possible attacks. Furthermore, recent changes in legislation imposed additional security constraints in technology to ensure the privacy of individuals and the integrity of data when stored in the cloud. In contrast with this trend, current data security methods, based on encryption, create an additional overhead to the performance, and often they are not allowed in public cloud servers. Hence, this paper proposes a mechanism that combines data fragmentation to protect medical images on the public cloud servers, and a NoSQL database to secure an efficient organisation of such data. Results of this paper indicate that the latency of the proposed method is significantly lower if compared with AES, one of the most adopted data encryption mechanisms. Therefore, the proposed method is an optimal trade-off in environments with low latency requirements or limited resources.

Cloud computing is one of the greatest innovations and emerging technologies of the century. It incorporates networks, databases, operating systems, and virtualization technologies thereby bringing the security challenges associated with these technologies. Security Measures such as two-factor authentication, intrusion detection systems, and data backup are already in place to handle most of the security threats and vulnerabilities associated with these technologies but there are still other threats that may not be easily detected. Such a threat is a malicious user gaining access to the Virtual Machines (VMs) of other genuine users and using the Virtual Machine resources for their benefits without the knowledge of the user or the cloud service provider. This research proposes a model for proactive monitoring and detection of anomalies in VM resource usage. The proposed model can detect and pinpoint the time such anomaly occurred. Isolation Forest and One-Class Support Vector Machine (OCSVM) machine learning algorithms were used to train and test the model on sampled virtual machine workload trace using a combination of VM resource metrics together. OCSVM recorded an average F1-score of 0.97 and 0.89 for hourly and daily time series respectively while Isolation Forest has an average of 0.93 and 0.80 for hourly and daily time series. This result shows that both algorithms work for the model however OCSVM had a higher classification success rate than Isolation Forest.

The cloud infrastructure is not new to the society from past one decade. But even in recent time, the companies started migrating from local services to cloud services for better connectivity and for other requirements, this is due to companies financial limitations on existing infrastructure, they are migrating to less cost and hire and fire support based cloud infrastructures. But the proposed cloud infrastructure require security on event logs accessed by different end users on the cloud environment. To adopt the security on local services to cloud service based infrastructure, it need better identify management between end users. Therefore this paper presents the related works of user identity as a service for each user involving in cloud service and the accessing permission and protection will be monitored and controlled by the cloud security infrastructures.

Cloud computing is one of the greatest and authoritative paradigms in computing as it provides access and use of various third-party services at a lower cost. However, there exist various security challenges facing cloud computing especially in the aspect of data privacy and this is more critical when dealing with sensitive personal or organization's data. Cloud service providers encrypt data during transfer from the local hard drive to the cloud server and at the server-side, the only problem is that the encryption key is stored by the service provider meaning they can decrypt your data. This paper discusses how cloud security can be enhanced by using client-side data encryption (pre-internet encryption), this will allow the clients to encrypt data before uploading to the cloud and store the key themselves. This means that data will be rendered to the cloud in an unreadable and secure format that cannot be accessed by unauthorized persons.

Cloud security includes the strategies which works together to guard data and infrastructure with a set of policies, procedures, controls and technologies. These security events are arranged to protect cloud data, support supervisory obedience and protect customers' privacy as well as setting endorsement rules for individual users and devices. The partition-based handling and encryption mechanism which provide fine-grained admittance control and protected data sharing to the data users in cloud computing. Graph partition problems fall under the category of NP-hard problems. Resolutions to these problems are generally imitative using heuristics and approximation algorithms. Partition problems strategy is used in bi-criteria approximation or resource augmentation approaches with a common extension of hyper graphs, which can address the storage hierarchy.

Cloud storage, in general, is a collection of Computer Technology resources provided to consumers over the internet on a leased basis. Cloud storage has several advantages, including simplicity, reliability, scalability, convergence, and cost savings. One of the most significant impediments to cloud computing's growth is security. This paper proposes a security approach based on cloud security. Cloud security now plays a critical part in everyone's life. Due to security concerns, data is shared between cloud service providers and other users. In order to protect the data from unwanted access, the Security Service Algorithm (SSA), which is called as MONcrypt is used to secure the information. This methodology is established on the obfuscation of data techniques. The MONcrypt SSA is a Security as a Service (SaaS) product. When compared to current obfuscation strategies, the proposed methodology offers a better efficiency and smart protection. In contrast to the current method, MONcrypt eliminates the different dimensions of information that are uploaded to cloud storage. The proposed approach not only preserves the data's secrecy but also decreases the size of the plaintext. The exi sting method does not reduce the size of data until it has been obfuscated. The findings show that the recommended MONcrypt offers optimal protection for the data stored in the cloud within the shortest amount of time. The proposed protocol ensures the confidentiality of the information while reducing the plaintext size. Current techniques should not reduce the size of evidence once it has been muddled. Based on the findings, it is clear that the proposed MONcrypt provides the highest level of protection in the shortest amount of time for rethought data.

To ensure a secure Cloud-based Electronic Health Record (EHR) system, we need to encrypt data and impose field-level access control to prevent malicious usage. Since the attributes of the Users will change with time, the encryption policies adopted may also vary. For large EHR systems, it is often necessary to search through the encrypted data in realtime and perform client-side computations without decrypting all patient records. This paper describes our novel cloud-based EHR system that uses Attribute Based Encryption (ABE) combined with Semantic Web technologies to facilitate differential access to an EHR, thereby ensuring only Users with valid attributes can access a particular field of the EHR. The system also includes searchable encryption using keyword index and search trapdoor, which allows querying EHR fields without decrypting the entire patient record. The attribute revocation feature is efficiently managed in our EHR by delegating the revision of the secret key and ciphertext to the Cloud Service Provider (CSP). Our methodology incorporates advanced security features that eliminate malicious use of EHR data and contributes significantly towards ensuring secure digital health systems on the Cloud.

Cloud Computing is a technology which provides flexibility through scalability. Like, Cloud computing, nowadays, Fog computing is considered more revolutionary and dynamic technology. But the main problem with the Fog computing is to take care of its security as in this also person identification is done by single Sign-In system. To come out from the security problem raised in Fog computing, an innovative approach has been suggested here. In the present paper, an approach has been proposed that combines different biometric techniques to verify the authenticity of a person and provides a complete model that will be able to provide a necessary level of verification and security in fog computing. In this model, several biometric techniques have been used and each one of them individually helps extract out more authentic and detailed information after every step. Further, in the presented paper, different techniques and methodologies have been examined to assess the usefulness of proposed technology in reducing the security threats. The paper delivers a capacious technique for biometric authentication for bolstering the fog security.

Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed to protect the cloud from attacks using attack signatures. Most of them may seem effective and efficient; however, there are many drawbacks such as the attack detection performance and the system maintenance. Recently, learning-based methods for security applications have been proposed for cloud anomaly detection especially with the advents of machine learning techniques. However, most researchers do not consider the attack classification which is an important parameter for proposing an appropriate countermeasure for each attack type. In this paper, we propose a new firewall model called Secure Packet Classifier (SPC) for cloud anomalies detection and classification. The proposed model is constructed based on collaborative filtering using two machine learning algorithms to gain the advantages of both learning schemes. This strategy increases the learning performance and the system's accuracy. To generate our results, a publicly available dataset is used for training and testing the performance of the proposed SPC. Our results show that the accuracy of the SPC model increases the detection accuracy by 20% compared to the existing machine learning algorithms while keeping a high attack detection rate.

Most cyber-attacks and data breaches in cloud infrastructure are due to human errors and misconfiguration vulnerabilities. Cloud customer-centric tools are imperative for mitigating these issues, however existing cloud security models are largely unable to tackle these security challenges. Therefore, novel security mechanisms are imperative, we propose Risk-driven Fault Injection (RDFI) techniques to address these challenges. RDFI applies the principles of chaos engineering to cloud security and leverages feedback loops to execute, monitor, analyze and plan security fault injection campaigns, based on a knowledge-base. The knowledge-base consists of fault models designed from secure baselines, cloud security best practices and observations derived during iterative fault injection campaigns. These observations are helpful for identifying vulnerabilities while verifying the correctness of security attributes (integrity, confidentiality and availability). Furthermore, RDFI proactively supports risk analysis and security hardening efforts by sharing security information with security mechanisms. We have designed and implemented the RDFI strategies including various chaos engineering algorithms as a software tool: CloudStrike. Several evaluations have been conducted with CloudStrike against infrastructure deployed on two major public cloud infrastructure: Amazon Web Services and Google Cloud Platform. The time performance linearly increases, proportional to increasing attack rates. Also, the analysis of vulnerabilities detected via security fault injection has been used to harden the security of cloud resources to demonstrate the effectiveness of the security information provided by CloudStrike. Therefore, we opine that our approaches are suitable for overcoming contemporary cloud security issues.

For optimal use of cloud resources and to reduce the latency of cloud users, the cloud computing model extends the services such as networking facilities, computational capabilities and storage facilities based on demand. Due to the dynamic behavior, distributed paradigm and heterogeneity present among the processing elements, devices and service oriented pay per use policies; the cloud computing environment is having its availability, security and privacy issues. Among these various issues one of the important issues in cloud computing paradigm is DDoS attack. This paper put in plain words the DDoS attack, its detection as well as prevention mechanisms in cloud computing environment. The inclusive study also explains about the effects of DDoS attack on cloud platform and the related defense mechanisms required to be considered.

This paper proposes and analyzes a new virtual machine (VM) placement technique called Group Instance to deal with co-location attacks in public Infrastructure-as-a-Service (IaaS) clouds. Specifically, Group Instance organizes cloud users into groups with pre-determined sizes set by the cloud provider. Our empirical results obtained via experiments with real-world data sets containing million of VM requests have demonstrated the effectiveness of the new technique. In particular, the advantages of Group Instance are three-fold: 1) it is simple and highly configurable to suit the financial and security needs of cloud providers, 2) it produces better or at least similar performance compared to more complicated, state-of-the-art algorithms in terms of resource utilization and co-location security, and 3) it does not require any modifications to the underlying infrastructures of existing public cloud services.

FPGAs are becoming a common sight in cloud environments and new usage paradigms, such as FPGA-as-a-Service, have emerged. This development poses a challenge to traditional FPGA security models, as these are assuming trust between the user and the hardware owner. Currently, the user cannot keep bitstream nor data protected from the hardware owner in an FPGA-as-a-service setting. This paper proposes a security model where the chip manufacturer takes the role of root-of-trust to remedy these security problems. We suggest that the chip manufacturer creates a Public Key Infrastructure (PKI), used for user bitstream protection and data encryption, on each device. The chip manufacturer, rather than the hardware owner, also controls certain security-related peripherals. This allows the user to take control over a predefined part of the programmable logic and set up a protected enclave area. Hence, all user data can be provided in encrypted form and only be revealed inside the enclave area. In addition, our model enables secure and concurrent multi-tenant usage of remote FPGAs. To also consider the needs of the hardware owner, our solution includes bitstream certification and affirming that uploaded bitstreams have been vetted against maliciousness.

Cloud computing is a new kind of computing model which allows users to effectively rent virtualized computing resources on pay as you go model. It offers many advantages over traditional models in IT industries and healthcare as well. However, there is lack of trust between CSUs and CSPs to prevent the extensive implementation of cloud technologies amongst industries. Different models are developed to overcome the uncertainty and complexity between CSP and CSU regarding suitability. Several researchers focused on resource optimization, scheduling and service dependability in cloud computing by using fuzzy logic. But, data storage and security using fuzzy logic have been ignored. In this paper, a trust evaluation model is proposed for cloud computing security using fuzzy theory. Authors evaluates how fuzzy logic increases efficiency in trust evaluation. To validate the effectiveness of proposed FTEM, authors presents a case study of healthcare organization.

Cloud, Software-Defined Networking (SDN), and Network Function Virtualization (NFV) technologies have introduced a new era of cybersecurity threats and challenges. To protect cloud infrastructure, in our earlier work, we proposed Software Defined Security Service (SDS2) to tackle security challenges centered around a new policy-based interaction model. The security architecture consists of three main components: a Security Controller, Virtual Security Functions (VSF), and a Sec-Manage Protocol. However, the security architecture requires an agile and specific protocol to transfer interaction parameters and security messages between its components where OpenFlow considers mainly as network routing protocol. So, The Sec-Manage protocol has been designed specifically for obtaining policy-based interaction parameters among cloud entities between the security controller and its VSFs. This paper focuses on the design and the implementation of the Sec-Manage protocol and demonstrates its use in setting, monitoring, and conveying relevant policy-based interaction security parameters.

Cloud security is one of the major issues that worry individuals and organizations about cloud computing. Therefore, defending cloud systems against attacks such asinsiders' attacks has become a key demand. This paper investigates insider threat in cloud relational database systems(cloud RDMS). It discusses some vulnerabilities in cloud computing structures that may enable insiders to launch attacks, and shows how load balancing across multiple availability zones may facilitate insider threat. To prevent such a threat, the paper suggests three models, which are Peer-to-Peer model, Centralized model and Mobile-Knowledgebase model, and addresses the conditions under which they work well.

Taking care of security at the cloud is a major issue that needs to be carefully considered and solved for both individuals as well as organizations. Organizations usually expect more trust from employees as well as customers in one hand. On the other hand, cloud users expect their private data is maintained and secured. Although this must be case, however, some malicious outsiders of the cloud as well as malicious insiders who are cloud internal users tend to disclose private data for their malicious uses. Although outsiders of the cloud should be a concern, however, the more serious problems come from Insiders whose malicious actions are more serious and sever. Hence, insiders' threats in the cloud should be the top most problem that needs to be tackled and resolved. This paper aims to find a proper solution for the insider threat problem in the cloud. The paper presents a Mobile Edge Computing (MEC) mitigation model as a solution that suits the specialized nature of this problem where the solution needs to be very close to the place where insiders reside. This in fact gives real-time responses to attack, and hence, reduces the overhead in the cloud.

Incident Handling for Cloud Infrastructures focuses on how the clustering based and non-clustering based algorithms can be implemented. Our research focuses in identifying anomalies and suspicious activities that might happen inside a Cloud Infrastructure over available datasets. A brief study has been conducted, where a network statistics dataset the NSL-KDD, has been chosen as the model to be worked upon, such that it can mirror the Cloud Infrastructure and its components. An important aspect of cloud security is to implement anomaly detection mechanisms, in order to monitor the incidents that inhibit the development and the efficiency of the cloud. Several methods have been discovered which help in achieving our present goal, some of these are highlighted as the following; by applying algorithm such as the Local Outlier Factor to cancel the noise created by irrelevant data points, by applying the DBSCAN algorithm which can detect less denser areas in order to identify their cause of clustering, the K-Means algorithm to generate positive and negative clusters to identify the anomalous clusters and by applying the Isolation Forest algorithm in order to implement decision based approach to detect anomalies. The best algorithm would help in finding and fixing the anomalies efficiently and would help us in developing an Incident Handling model for the Cloud.

Cloud provides a low maintenance and affordable storage to various applications and users. The data owner allows the cloud users to access the documents placed in the cloud service provider based on the user's access control vector provided to the cloud users by the data owners. In such type of scenarios, the confidentiality of the documents exchanged between the cloud service provider and the users should be maintained. The existing approaches used to provide this facility are not computation and communication efficient for performing key updating in the data owner side and the key recovery in the user side. This paper discusses the key management services provided to the cloud users. Remote key management and client-side key management are two approaches used by cloud servers. This paper also aims to discuss the method for destroying the encryption/decryption group keys for shared data to securing the data after deletion. Crypto Shredding or Crypto Throw technique is deployed for the same.