Data Theft Attacks Research Articles

Secure Authentication and Data Transmission for Patients Healthcare Data in Internet of Medical Things

Currently, data transmission is an expanding area in healthcare, enabling health practitioners to examine, assess, and manage patients using mobile communication technologies. To identify and analyze a patient, healthcare providers need to access the physician's Electronic Medical Record (EMR), which may contain extensive audiovisual big data such as MRIs, CT scans, PET scans, X-rays, and more. To ensure accessibility and scalability for healthcare workers and consumers, the EMR needs to be stored in large data repositories on cloud servers. However, due to the sensitive nature of medical information stored in the cloud, the healthcare profession faces numerous security challenges, with data theft attacks being one of the most critical vulnerabilities. This research focuses on protecting medically sensitive data in the cloud by leveraging cloud computing facilities. The upgraded AES approach ensures that confidential data is securely accessible and stored. In addition, improved Elliptic Curve Cryptography (ECC) is utilized for key generation and validation. A hybrid optimization approach, combining robust optimization and genetic algorithms, is employed to select unique and distinct keys. Decryption is performed using deep neural networks, and Convolutional Neural Networks (CNN) enable batch encryption of multiple documents. The comparison between old methods and the proposed approach is based on encryption time, decryption time, and security strength.

Security and reliability of N-version cloud-based task solvers with individual version cancellation under data theft attacks

As one of the enabling technologies for cyber-physical systems and Internet of Things systems, the cloud computing provides cost-effective resources in an on-demand manner. This merit lends the cloud to running critical services that need redundancy to achieve high reliability. This paper models a cloud service using the N-version programming (NVP) redundancy technique that creates and runs multiple task solver versions (TSVs) in parallel to perform a requested service and decides the output using the threshold voting. A malicious attacker may get an unauthorized access to a user's data when the user's and attacker's virtual machines co-reside in the same cloud server. To reduce the chance of the co-residence attack success and users’ expense, an individual TSV cancellation policy is implemented, which removes a TSV's virtual machine from its host server immediately once this TSV completes the task execution. A probabilistic method is proposed to evaluate the task reliability and data security under the considered cloud service model. Constrained optimization problems are further formulated and solved, which find the optimal number of TSVs maximizing the task reliability subject to providing a desired level of data security. Examples are presented to demonstrate interactions and impacts of different parameters on the task reliability and data security, as well as on the optimization solutions.

Cloud Data Security Model Using Modified Decoy Technique In Fog Computing For E-Healthcare

In recent years, the cloud provides the facility of storing the possible files to a remote database which can be retrieved on demand. Healthcare cloud infrastructure is used to store the medical records which in turn help in managing and tracking the records of the user. The primary care providers use Electronic Medical Record (EMR) for diagnosing and evaluating medical data. Big data analytics is an important technology in enormous business areas which includes banking, social media, machine sensor data and medical welfare. Healthcare cloud provides various storage facilities such as Google drive, dropbox business and one drive in which security issues like data theft attacks to be considered as the serious security breaches of the cloud. In this paper, a modified decoy technique is proposed in order to ensure more security for the user’s Medical Big Data (MBD). The tri-party authentication key uses key agreement protocol which is modelled by bilinear pairing cryptography. The proposed system detects the attacker and sends the information such as IP address, access time and date to the user. It also provides the facility to block the account from further access. Using the triple-des algorithm in bilinear pairing the parameters like throughput is increased and computational complexity is decreased.

Enhanced Secure User Data on Cloud using Cloud Data Centre Computing and Decoy Technique

In recent years the storage of cloud platform was increased a lot. Large set of personal and business data can be stored and accessed at anywhere at any time. Data’s are stored in a secured manner using various secured and optimized algorithms. Over this data theft and modifications are done in a highly manner. Therefore this creates a various security risks and data theft problems in cloud. There is need of security technique to overcome this problem. A proposed technique called cloud data centre technique is a new model for securing data from different attacks and security risks. This technique incorporate the FEBER and CUSCUM algorithms, which will monitor the user activity and then identify the legitimacy and prevent from any unauthorized user access. This data center technique can be used to detect and avoid data theft attacks by malicious insiders. The anonymous and miss use of data also prevented in a very good manner while compared to other techniques.

Prevention of Data Theft Attacks in Infrastructure as a service Cloud through Trusted Computing

Dispensed computing is a developmental model that permits cloud consumer to name for assets with on-request management and use the assets utilizing pay in step with use version. Due to this pliability, the dispensed computing implementing colossal safety dangers. Consistent with the pc security Alliance, an insider chance is a noteworthy chance in distributed computing situation. This threat quite influences the data trustworthiness and classification of cloud client applications, administrations, or statistics. In this paper, we generally have a tendency to specializing in corporation reliable attacks on consumer virtual machines and anticipated a structure that upholds facts respectability and privateness, littlest TPM activities, and decreased and irrefutable reliable figuring base. Our exploratory consequences show the us of the united states that custom remember-visor hyper-visor playacting brilliant and it will prevent and word the business enterprise legitimate assaults in cloud surroundings

An efficient fog computing for comprising approach to avoid data theft attack

Cloud computing has become a new paradigm shift in the IT world because of its revolutionary model of computing. It provides flexibility, scalability, and reliability and decreased operational and support expenses for an organization. The Enterprise edition software’s are very costly and maintaining a separate IT team and maintaining their own servers is very expensive and that’s the reason why most of the companies are opting for Cloud computing over enterprise edition of the software. However, few organization cloud customers are not willing to step to cloud computing up on a big scale because of the safety problems present in cloud computing. One more disadvantage of Cloud is it’s not suitable for another revolutionary technology i.e.IoT(Internet of things)In this paper we are going to present the Advantages of Fog Computing and Decoy technology to address the security in cloud computing by extending it into fog computing.Fog Computing is a new paradigm in which the computing power moves to the edge of the network. So, it’s also called as Edge Computing.

A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility With Pairing-Based Cryptography

Nowadays, telemedicine is an emerging healthcare service where the healthcare professionals can diagnose, evaluate, and treat a patient using telecommunication technology. To diagnose and evaluate a patient, the healthcare professionals need to access the electronic medical record (EMR) of the patient, which might contain huge multimedia big data including X-rays, ultrasounds, CT scans, and MRI reports. For efficient access and supporting mobility for both the healthcare professionals as well as the patients, the EMR needs to be kept in big data storage in the healthcare cloud. In spite of the popularity of the healthcare cloud, it faces different security issues; for instance, data theft attacks are considered to be one of the most serious security breaches of healthcare data in the cloud. In this paper, the main focus has been given to secure healthcare private data in the cloud using a fog computing facility. To this end, a tri-party one-round authenticated key agreement protocol has been proposed based on the bilinear pairing cryptography that can generate a session key among the participants and communicate among them securely. Finally, the private healthcare data are accessed and stored securely by implementing a decoy technique.

Fog Computing: Securing the cloud and preventing insider attacks in the cloud

Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. Because of these new computing and communication paradigm there arise data security challenges. Even though existing techniques use security mechanisms, data theft attacks prevention fails. To overcome this we can use decoy technology to secure data stored in cloud. Although, Fog Computing is defined as the extension of the Cloud Computing paradigm, its distinctive characteristics in the location sensitivity, wireless connectivity, and geographical accessibility create new security and forensics issues and challenges which have not been well studied in Cloud security and Cloud forensics. We monitor data access in the cloud and detect abnormal data access patterns. When unauthorized access is suspected and then verified using challenge questions, we launch a disinformation attack by returning large amounts of decoy information to the attacker. This protects against the misuse of the user's real data. Experiments conducted in a local file setting provide evidence that this approach may provide unprecedented levels of user data security in a Cloud environment.

Fog Computing: Comprehensive Approach for Security Data Theft Attack Using Elliptic Curve Cryptography and Decoy Technology

Fog computing extends cloud computing, provides the services like data, compute, storage and application to end user. It improves the quality of service and also reduces latency. According to Cisco, due to its wide geographical distribution, the Fog computing is well suited for real time analytics and big data. This article, by exploitation advantages of Fog computing Paradigm, analyzes its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks, Wireless Sensor and Actuator Networks, Decentralized Smart Building Control, IoT and Cyber-physical systems and software defined networks, and reviews Comprehensive Approach for security Data Theft Attack the use of Elliptic Curve Cryptography (ECC) and decoy technology in such a constrained environment along with the other two aspects of ECC, namely its security and efficiency. In the article, the performance of ECC is compared with the other PKC applications which should prove that ECC performs well and is more suitable for Fog environments.

Security Solutions for Cloud Computing

In the recent times, most organizations in the world have increasingly realized the importance of cloud platforms. Cloud computing is an emerging technology to store and access personal data along with business information from remote locations. One of the benefits of using cloud computing is the ability to tap into huge quantities of both structured and unstructured data. With every new technology there are certain drawbacks associated with it and cloud is no exception. Unlike others, Cloud environment also faces challenges such as Distributed denial of service (DDoS) attacks, data thefts, particularly when they are insider attacks. Increase in the number of DDoS attacks is one of the latest issues. We have studied various intrusion detection techniques that includes Cloud trace back model, use of back propagation neural network and Virtual Intrusion Detection Systems (V-IDS). Now these attacks can be prevented using the proposed Intrusion Prevention System i.e. Service-based Intrusion Prevention System in Cloud Computing (SIPSCC). A technique called fog computing can be used to detect and prevent data theft attacks by malicious insiders is studied. General Terms Cloud Computing Security