In a cloud environment, traditional approaches are used to encrypt any data by using 0 and 1 that increase data security issues because of the presence of numerous malicious users and hackers over the internet. Deoxyribonucleic Acid (DNA) computing can be one of the best solutions to improve data security in which data are encrypted using the DNA bases: Thymine (T), Guanine (G), Cytosine (C) and Adenine (A). Along with data security, access control is another major issue of a cloud environment as the searching time of the owner of any data, the system overheard and the accessing time of a file or data are high during data access. A novel DNA computing based secure and fast Access Control Model (ACM) is proposed in this article to solve all these major problems. In the proposed scheme, the Cloud Service Provider (CSP) keeps a table or list for fast data accessing. Here, a 1024-bit DNA computing based random key is generated by using the user's secret information, and the same key is utilized for data encryption. Theoretical analysis along with many experimental results prove the efficiency and effectiveness of the proposed access control model over some well-known existing models. 

Fast and Secure Data Accessing by Using DNA Computing for the Cloud Environment

AI has been implemented in many sectors such as security, health, finance, national defense, etc. However, together with AI’s groundbreaking improvement, some people exploit AI to do harmful things. In parallel, there is rapid development in cloud computing technology, introducing a cloud-based AI system. Unfortunately, the vulnerabilities in cloud computing will also affect the security of AI services. We observe that compromising the training data integrity means compromising the results in the AI system itself. From this background, we argue that it is essential to keep the data integrity in AI systems. To achieve our goal, we build a data integrity architecture by following the National Institute of Standards and Technology (NIST) cybersecurity framework guidance. We also utilize blockchain technology and smart contracts as a suitable solution to overcome the integrity issue because of its shared and decentralized ledger. Smart contracts are used to automate policy enforcement, keep track of data integrity, and prevent data forgery. First, we analyze the possible vulnerabilities and attacks in AI and cloud environments. Then we draw out our architecture requirements. The final result is that we present five modules in our proposed architecture that fulfilled NIST framework guidance to ensure continuous data integrity provisioning towards secure AI environments.

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Toward Data Integrity Architecture for Cloud-Based AI Systems

The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision, video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, how to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge, for which the insufficient consideration will largely limit its application in safety-critical domains. One concern is that the present MLBSS development tends to be rush, and the latent vulnerabilities and privacy issues exposed to external users and attackers will be largely neglected and hard to be identified. Additionally, machine learning-based software systems exhibit different liabilities towards novel vulnerabilities at different development stages from requirement analysis to system maintenance, due to its inherent limitations from the model and data and the external adversary capabilities. In this work, we consider that security for machine learning-based software systems may arise by inherent system defects or external adversarial attacks, and the secure development practices should be taken throughout the whole lifecycle. While machine learning has become a new threat domain for existing software engineering practices, there is no such review work covering the topic. Overall, we present a holistic review regarding the security for MLBSS, which covers a systematic understanding from a structure review of three distinct aspects in terms of security threats. Moreover, it provides a thorough state-of-the-practice for MLBSS secure development. Finally, we summarise the literature for system security assurance, and motivate the future research directions with open challenges. We anticipate this work provides sufficient discussion and novel insights to incorporate system security engineering for future exploration.

Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges

A R T I C L E I N F O A B S T R A C T Article history: Received: 29 August, 2020 Accepted: 01 November, 2020 Online: 10 November, 2020 This paper is an extension of work originally presented in PM2.5 Prediction-based Weather Forecast Information and Missingness Challenges: A Case Study Industrial and Metropolis Areas, which focused on imputation algorithm to solve missingness challenge and demonstrated a basic prediction system to prove the proposed algorithm, II-MPA. Distributed and decentralized systems, recently, have been proven for their effectiveness in multiple perspectives. This paper introduces “Overmind”, the solution that governs and builds the network of decentralized machine learning as a prediction framework named after its functionality: it aims to discover a set of data and associated attributes for assigning machine learnings in the collaborative decentralized manner. Overmind also empowers feature transfer learning with data preservation. It demonstrates how discovered features are transferred and shared among synergic agents in the network. This model is tested and evaluated the accuracy against the traditional single machine learning prediction model in the original work. The results are satisfactory in both prediction performance and transfer learning.

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Overmind: A Collaborative Decentralized Machine Learning Framework

Cloud computing is a computing paradigm that provides on-demand, scalable as well as measured services to the end uses. In today’s era almost each and every business has huge dependency on this computing technology in terms of cost-saving, infrastructure, development platform, data processing, data analytics etc. The services provided by the cloud service providers (CSP) can be consumed by the end users anytime, anywhere by the web application over the internet. The security of the cloud infrastructure is of utmost importance and several research work involving various technologies are utilized so as to provide better and more accurate defence mechanism against cloud attacks. Machine learning is a technology that has proved to produce better results in securing the cloud environment in the recent times. Machine learning algorithms are trained on the various authentic datasets to build models that can automate the process of detecting the cloud attacks with higher accuracy in comparison with any other technology. This paper reviews some of the latest research papers that have employed machine learning as a security mechanism against cloud attacks.

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Machine Learning based Security for Cloud Computing: A Survey

Distributed Machine Learning (DML) is one of the core technologies for Artificial Intelligence (AI). However, in the existing distributed machine learning framework, the data integrity is not taken into account. If network attackers forge the data, modify the data, or destroy the data, the training model in the distributed machine learning system will be greatly affected, and the training results are led to be wrong. Therefore, it is crucial to guarantee the data integrity in the DML. In this paper, we propose a distributed machine learning oriented data integrity verification scheme (DML-DIV) to ensure the integrity of training data. Firstly, we adopt the idea of Provable Data Possession (PDP) sampling auditing algorithm to achieve data integrity verification so that our DML-DIV scheme can resist forgery attacks and tampering attacks. Secondly, we generate a random number, namely blinding factor, and apply the discrete logarithm problem (DLP) to construct proof and ensure privacy protection in the TPA verification process. Thirdly, we employ identity-based cryptography and two-step key generation technology to generate data owner’s public/private key pair so that our DML-DIV scheme can solve the key escrow problem and reduce the cost of managing the certificates. Finally, formal theoretical analysis and experimental results show the security and efficiency of our DML-DIV scheme.

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Xiao-Ping Zhao

Papers

Distributed Machine Learning Oriented Data Integrity Verification Scheme in Cloud Computing Environment