Secure Data Communication Research Articles

LECast: A Low-Energy-Consumption Broadcast Protocol for UAV Blockchain Networks

With the continuous development of communication technology, drones are playing an important role in many fields, such as power transmission line inspection and agricultural pesticide spraying. In order to protect the data privacy and communication security of drones, many experts are considering blockchain as its enabling technology. However, due to their small size and limited power storage, drones cannot support energy-intensive blockchain applications. In addition, the future 6G communications need to implement an important key performance indicator, namely extremely low-power communications (ELPCs). As a consequence, research into green blockchain is becoming more and more popular. The broadcast of the blockchain is one of the most energy-intensive parts because it entails flooding and there are a lot of unnecessary communication processes. Therefore, in order to make blockchain more suitable for ELPC requirements in 6G communications and unmanned aerial vehicle (UAV) networks, we took the blockchain broadcast as an improvement candidate and designed LECast, a low-energy-consumption protocol. LECast first analyzes the energy consumption model of the communication between two drones and constructs the shortest-path broadcast tree (SPB Tree) for the UAV networks to minimize energy consumption. Meanwhile, to make the sending drone address the receiving drone in a more convenient way, we proposed an extended Huffman coding (EHC) scheme to name the drones. Furthermore, the other issues with the broadcast tree are reliability and security. When a channel fails, subsequent drones cannot smoothly receive the transaction or block data. As a result, we introduced multichannel transmission with splitting data (MTSD); that is, the transaction or block data are divided into segments and transmitted in parallel multiple times over multiple channels. Finally, through the analysis and simulation of LECast in terms of energy consumption, latency, throughput, reliability, security, and coverage rate, the advantages of LECast were confirmed, which could meet the requirements of ELPCs and be well applied to UAV networks.

Multi-Hider Reversible Data Hiding Using a Weighted Color Transfer and Modulus Operation

This paper proposes a multi-hider dual-image reversible data-hiding algorithm. Generating dual stego images, the proposed algorithm provides a high embedding capacity, exhibits high image quality, offers reversibility to restore the stego images, and suggests seven levels of secret message extraction. The message embedding in our algorithm contains two phases. In Phase-1, we embed an n-ary secret message vector followed by an m-ary secret message in each pixel using two distinct secret keys when rendering a new image from the source and target images using an optimal weighted color-transfer process. This phase produces a tentative stego image which exhibits a new color appearance, different from the input source image. In Phase-2, we introduce a weighted modulus operation to embed a k-ary secret message vector into a dual-pixel constructed from the tentative stego image. The message concealment is elaborately designed so that the hidden secret messages are intact. This phase produces dual stego images which carry three distinct secret messages. Using legitimate secret keys, an authorized receiver can extract one or parts of secret messages and recover the tentative stego color-transferred image without arousing any suspicion. The experimental results and analysis confirm that our scheme can resist RS and PVD steganalytic attacks. In addition, our algorithm provides both high embedding capacity and better image quality, outperforming its counterparts. To the best of our knowledge, our scheme is the first in the relevant literature that provides multi-hider reversible data hiding, thereby offering various levels of message extraction for secure data communication.

High Security Image Cryptographic Algorithm Using Chaotic Encryption Algorithm with Hash-LSB Steganography

In the latest, digital images are frequently used for storage, communication, medical imaging, etc. Such images possibly involve private data, which accordingly handed an important role to information security. Image encryption and steganography are two widely used techniques for providing information security. Cryptography is the technique of protecting information by encryption and decryption. In steganography, the message is inserted in an image with changes entitled a cover image. The principal purpose of the study is to improve the current secure data communication techniques, by combining cryptography with steganography. Steganography and cryptography are two examples of the multiple methods used to guarantee secrecy and privacy throughout data communication. The suggested approach achieves encryption and decryption utilizing chaotic encryption technique and employing hash-least significant bit (HLSB) as steganography which is a significant method of embedding data bits in LSB bits of RGB pixels of the cover image.  The suggested image encryption method is validated by experimental results to be highly secure and resistant to a variety of attacks. Additionally, compared to several conventional picture encryption techniques, it offers higher performance. Peak signal-to-noise ratio (PSNR) and mean square error (MSE)calculations are used to provide security evaluations. PSNR is infinite and MSE is 0 for a secret image. PSNR and MSE for the cover image are 65 and 0.02 dB respectively. The results of this research reveal a high level of similarities between stego and cover images, and between recovered and secret images, as seen in the histogram analysis of secret images. These techniques are accomplished effectively by using the MATLAB program.

Study on Cryptography, Steganography and Combination of both for Data Security

Secure data communication is a key factor today while communicating through the unreliable network. Nowadays almost all applications are internet-based and it is important that communication made confidential and secure. But network of so many people is always unreliable. Cryptography and steganography are two important techniques which is used for secret and reliable communication over the network. Cryptography is the process of protecting information using different cryptographic algorithms so that only the intended person can read and process it. Steganography is the process of protecting information by hiding it inside another file such as image, audio, video and so on. But in recent years cryptanalysis study becomes so strong that only cryptography or only steganography alone may not enough for securing data. So the combination of cryptography and steganography produce stronger method than previous. In recent years many works has done or proposed about combining different types of cryptography and steganography schemes to make the transmission of data more secure through unreliable medium efficiently. This paper will help to understand some recent works related to combining cryptography and steganography within a short period of time and also will help to precede further study.

ORTHOGONAL REGRESSED STEEPEST DESCENT DEEP PERCEPTIVE NEURAL LEARNING FOR IoT- AWARE SECURED BIG DATA COMMUNICATION

The Internet of Things (IoT) is a collection of interconnected intelligent devices that exists within the larger network known as the Internet. With the increasing popularity of IoT devices, massive data is generated day by day. The collected data need to be continuously uploaded to the cloud server. Besides, the transmission of data in the cloud environment is performed via the internet, which faces numerous threats. However, the security issue always lacks an effective big data communication. Therefore, a novel technique called Orthogonal Regressed Steepest Descent Deep Structured Perceptive Neural Learning based Secured Data Communication (ORSDDSPNL-SDC) is introduced with higher accuracy and lesser time consumption. The ORSDDSPNL-SDC technique comprises three phases, namely registration, user authentication, and secure data communication. In the ORSDDSPNL-SDC technique, the registration phase is carried out for creating the new ID, and password for each user in the cloud. The IoT device's data is then sent to a cloud server by the cloud user for storage. After that, the orthogonal regressed steepest descent multilayer deep perceptive neural learning is applied to examine the user_ ID with already registered ID based on Szymkiewicz–Simpson coefficient. Then the Maxout activation function is to classify the user as authorized or unauthorized. Finally, the steepest descent function is applied for minimizing the classification error and increasing the classification accuracy. In this way, the authorized or unauthorized user is identified. Then the secured communication is performed with the authorized cloud users. Experimental evaluation is carried out on the factors such as classification accuracy, classification time and error rate, and space complexity with respect to a number of users. The qualitative results and discussion indicate that the proposed ORSDDSPNL-SDC offers elevated performance with regard to achieving higher classification accuracy and minimum error as well as computation time when compared to the existing methods.

SeCoM: An Outsourced Cloud-Based Secure Communication Model for Advanced Privacy Preserving Data Computing and Protection

A malignant entity may access outsourced healthcare data in Internet of Things (IoT) and cloud environments during data sharing, analysis, and communication among the involved entities to procure sensitive information that can be misused or leaked to other illegal entities. To tackle this pivotal and challenging issue, this article presents a novel efficient, privacy-preserving, secure communication model (SeCoM) for healthcare data protection in the cloud and IoT systems by minimizing the threat of data leakage, identifying, and terminating malicious entities against data leakage, and addressing security threats. The model provides enhanced security to the system by synchronously performing secure data storage, analysis, sharing, and communication. The experimental results and comparison of the proposed model with existing approaches demonstrate that it significantly improves privacy, security, and detection efficiencies as well as data utilization of up to 9.49%, 77.17%, 83%, and 43.25% along with a reduction of leakage and unsafe communication links by 97.62% and 98%, respectively.

A Cross-Layer Defense Method for Blockchain Empowered CBTC Systems Against Data Tampering Attacks

Due to the high integration of wireless communication and networking technologies, the communication-based train control (CBTC) systems are exposed to additional cyber-attack surfaces, allowing sophisticated attackers to combine cyber attack vectors with physical attack means to achieve malicious goals. Notably, the decentralized authentication features are missing in existing communication protocols which make the CBTC be easily compromised by data tampering attacks, and lead to serious operational accidents. With outstanding advantages in decentralized authentication, blockchain provides new effective solutions for decentralized identity authentication in CBTC. Consequently, it is critical to study the complex physical consequences of cyber breaches from a cross-layer defense perspective. In this paper, we propose a novel cross-layer defense method for cyber security in blockchain empowered CBTC against data tampering attacks. In the physical layer, the joint Kalman filter and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2} $ </tex-math></inline-formula> detector is proposed for the train state estimation and detection. In the cyber layer, an asymmetric encryption-based secure communication protocol with identity authentication and the blockchain-based distributed key management system with the adaptive consensus mechanism are designed for data communication security. Considering the unavailable direct observation of the CBTC cyber security states, a partially observable Markov (POMDP) decision model is constructed to derive the optimal adaptive consensus strategies for balancing cyber security and efficiency. Extensive simulation results show that the proposed blockchain empowered CBTC cross-layer defense method can effectively improve the cyber security protection capability and minimize the impact of data tampering attacks on the train operation.

A New hyper chaotic algorithm for energy video communication security

The usage of the cameras in facilities in the energy sector is becoming more and more common. In addition, with the widespread use of SCADA and the increase in automation of camera-based applications, ensuring security in video data communication has become more and more important. In this study, methods that have been successful in providing image security in previous studies in Ref.[1] have been improved to ensure the security of video data communication. These methods use Kurt-Modified Chua’s Circuit (KMCC) as random number generator. Proposed algorithms are efficient for energy sector secure video communication because of using hyperchaotic random number generator and also bit level scrambling, diffusion encryption to each frame of video.

Comprehensive Review of Renewable Energy Communication Modeling for Smart Systems

Due to the rising trends in the adoption of smart systems such as smart grids, smart homes, and vehicle-to-grid, there has been a lot of research interest in these areas. To manage these complex systems effectively and intelligently, a reliable, high-speed, and secure data communication network is very essential. The key distinguishing feature between smart systems and traditional ones is that smart systems use a two-way communication system while traditional systems usually use one-way communication. The requirements and techniques needed to ensure safe, secure, and reliable communication in smart systems have been the focus of many researchers in recent times. This work is aimed at providing a comprehensive, all-encompassing, up-to-date review of smart systems communication to ascertain the research directions as well as challenges. This review will guide other researchers in delving into smart systems communication to identify potential research problems and future research directions or research gaps.

NIDD: an intelligent network intrusion detection model for nursing homes

AbstractIn nursing homes using technologies such as IoT, big data, cloud computing, and machine learning, there is a constant risk of attacks such as Brute Force FTP, Brute Force SSH, Web Attack, Infiltration, and Botnet during data communication between individual terminals and the cloud server. Therefore, effectively identifying network communication data is essential to protect data communication security between individual terminals and the cloud server. Aiming at the data mentioned above regarding communication security issues, we propose an intelligent intrusion detection model NIDD (Network Intelligent Data Detection) model that combines deep convolution generation adversarial network (DCGAN) with Light Gradient Boosting Machine (LightGBM) and Shapley Additive exPlanations (SHAP). The NIDD model first generates new attack samples by learning the feature distribution of the existing attack sample data and effectively expands the rare attack samples. Secondly, we use the Light Gradient Boosting Machine (LightGBM) algorithm as the base classifier to train the dataset and start to build the intrusion detection model. Then use Shapley Additive exPlanations (SHAP) to analyze the contribution of the classification results, and adjust the model parameters according to the analysis results. Finally, we obtain the optimal model for the intelligent detection model of network intrusion. This paper conducts experimental tests on the NSL-KDD dataset. The experimental results show that the NIDD model built based on Light Gradient Boosting Machine can detect Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet, PROBE, R2L, and U2R attacks with an accuracy of 99.76%. Finally, we re-verified the NIDD model on the CIC-IDC-2018 dataset. The results once again proved that the NIDD model could solve the data communication security between the nursing robot and the cloud server and the data before the IoT terminal and the cloud server. Communication security provides a sufficient guarantee.

Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques.

A comparative study on HIPAA technical safeguards assessment of android mHealth applications.

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate. The Health Insurance Portability and Accountability Act (HIPAA) provides guidelines to the app developers so that the apps must be compliant with required and addressable Technical Safeguards. However, most mobile app developers, including mHealth apps are not aware of HIPAA security and privacy regulations. Therefore, a research opportunity has emerged to develop an analytical framework to assist the developer to maintain a secure and HIPAA-compliant source code and raise awareness among consumers about the privacy and security of sensitive and personal health information. We proposed an Android source code analysis framework that evaluates twelve HIPAA Technical Safeguards to check whether a mHealth application is HIPAA compliant or not. The implemented meta-analysis and data-flow analysis algorithms efficiently identify the risk and safety features of mHealth apps that violate HIPAA regulations. Furthermore, we addressed API level checking for secure data communication mandated by recent CMS guidelines between third-party mobile health apps and EHR systems. Experimentally, a web-based tool has been developed for evaluating the efficacy of analysis techniques and algorithms. We have investigated 200 top popular Medical and Health & Fitness category Android apps collected from Google Play Store. We identified from the comparative analysis of the HIPAA rules assessment results that authorization to access sensitive resources, data encryption-decryption, and data transmission security is the most vulnerable features of the investigated apps. We provided recommendations to app developers about the most common mistake made at the time of app development and how to avoid these mistakes to implement secure and HIPAA-compliant apps. The proposed framework enables us to develop an IDE plugin for mHealth app developers and a web-based interface for mHealth app consumers.

Integrated QKD and QRNG Photonic Technologies

Quantum key distribution (QKD) and quantum random number generation (QRNG) will play a key role in the future of data communication security. QKD can be used by trusted parties to share a secret key, leading to long-term secure communications. QRNG allows the production of fully unpredictable and certifiable random numbers, which can be used in numerous applications, including cryptography and large stochastic simulations. Both QKD and QRNG can be implemented using commercially available photonic components, which are mostly bulky and assembled into a discrete module. Although this approach can benefit from the superior performance and maturity of each individual component, it does not guarantee the large-scale deployment and commercial viability of the technology. A preferable alternative relies on integrating some or all functionalities in a single photonic integrated circuit (PIC) fabricated at the wafer scale, in this way reducing size, weight, complexity, cost, and power consumption. Integrated photonics can thus dramatically improve the performance and reliability of QKD systems and QRNG devices. Here, we review recent literature on designs, related materials, and fabrication processes, which is representative of state-of-the-art PIC-based QKD and QRNG. We conclude the paper by pointing out the challenges that need to be addressed in the future for reaching a wide-scale deployment of these integrated technologies.

IoV-SDCM: An IoV Secure Data Communication Model Based on Network Encoding and Relay Collaboration

Internet of Vehicles (IoV) is a significant 5G application scenario. As it developed rapidly, more and more vehicles are connected to Internet of Vehicles. The data security and privacy are the premises to ensure its service quality in an open communication network. This paper proposes IoV-SDCM, a secure data communication model in IoV. It includes a self-organizing relay forwarding network and an assured delivery mechanism. The relay forwarding network is used for constructing a dynamic collaboration network with the vehicle as the node. The security delivery mechanism is that network coding is used for data fragmentation and re-encoding to improve network communication reliability. Homomorphic encryption is used to encrypt and protect the encoding vector, improving information leakage and anticollusion attacks. The theoretical proof proves that the model has the ability of data transmission confidentiality and better antiattack capabilities, while it has privacy protection capabilities. Furthermore, the experiment verifies that the model also has the advantage of high and stable data delivery efficiency.

Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks

The Wireless Medium Access Control (WMAC) protocol functions by handling various data frames in order to forward them to neighbor sensor nodes. Under this circumstance, WMAC policies need secure data communication rules and intrusion detection procedures to safeguard the data from attackers. The existing secure Medium Access Control (MAC) policies provide expected and predictable practices against channel attackers. These security policies can be easily breached by any intelligent attacks or malicious actions. The proposed Wireless Interleaved Honeypot-Framing Model (WIHFM) newly implements distributed honeypot-based security mechanisms in each sensor node to act reactively against various attackers. The proposed WIHFM creates an optimal Wireless Sensor Network (WSN) channel model, Wireless Interleaved Honeypot Frames (WIHFs), secure hash-based random frame-interleaving principles, node-centric honeypot engines, and channel-covering techniques. Compared to various existing MAC security policies, the proposed model transforms unpredictable IHFs into legitimate frame sequences against channel attackers. Additionally, introducing WIHFs is a new-fangled approach for distributed WSNs. The successful development of the proposed WIHFM ensures resilient security standards and neighbor-based intrusion alert procedures for protecting MAC frames. Particularly, the proposed wireless honeypot methodology creates a novel idea of using honeypot frame traps against open wireless channel attacks. The development of a novel wireless honeypot traps deals with various challenges such as distributed honeypot management principles (node-centric honeypot, secretly interleaved-framing principles, and interleaving/de-interleaving procedures), dynamic network backbone management principles (On Demand Acyclic Connectivity model), and distributed attack isolation policies. This effort provides an effective wireless attack-trapping solution in dynamic WSNs. The simulation results show the advantage of the proposed WIHFM over the existing techniques such as Secure Zebra MAC (SZ-MAC), Blockchain-Assisted Secure-Routing Mechanism (BASR), and the Trust-Based Node Evaluation (TBNE) procedure. The experimental section confirms the proposed model attains a 10% to 14% superior performance compared to the existing techniques.

A Secure LEACH Protocol for Efficient CH Selection and Secure Data Communication in WSNs

Due to the applications of wireless sensor networks (WSNs) in distinct fields, like civilian and military, environmental monitoring, and target tracking, etc., it became the important research area for researchers. Therefore, an efficient organization of network topology is needed for multi-hop data aggregation based on the energy constrained WSNs with large scale deployment for making the routing task simpler, balancing the load efficiently, and enhancing the network lifetime. The low-energy adaptive clustering hierarchy (LEACH) is an architecture of application-specific protocol, which is used for WSNs. However, this LEACH protocol suffered from more energy consumption in network without considering the cluster heads (CHs) distribution on a rotation basis. In addition, the complex security mechanisms are not sufficient due to limited bandwidth and other restrictions that the sensor nodes have. Thus, it is essential to improve the energy efficiency, CH stability and secure data transmission in WSN’s, which is the main objective of this work. Motivated from these three challenges, this article proposes a stable and secure LEACH (SS-LEACH), in which a new and improved protocol of LEACH is proposed for enhancing the stability of CH and energy efficiency as it considers the nodes’ consumed energy ratio (CER) for CH selection and random number generation, here after the proposed LEACH is named as CER-LEACH. In addition, it aims to prevent the previously elected CH node and it will get another chance in the current round. This method correlates the threshold used in traditional LEACH with the energy consumption ratio of each node. Further, it also introduces a hybrid reputation-based data transmission (HRDT) scheme for secure data transmission. With the proposed CER-LEACH protocol, WSNs achieves better performance in terms of secured communication, network lifetime, and energy consumption based on the analysis of simulation results as compared to state-of-art approaches.

Jaccard Index Cat Gradient Boosting Classification for Secured Big Data Communication

Big data is observed as a novel field dealing with datasets that are too complex in providing indispensable services for daily chores and also discovering hidden patterns. Network security has become a major issue due to big data analytics, which offers unlimited research potential. More specifically, secure data communication without a third party is a major concern. Also, as large, heterogeneous, and complex data sets emerge, existing security mechanisms cannot provide or address network threats quickly or accurately. Therefore, along with the decrease in time, accuracy and error rate are other research concerns. Accordingly, an accurate and timely big data-based secure method called Jaccard Index Cat Gradient Boosting Classification-based Secured Data Communication (JICGBC-SDC) using the Internet of Things is presented. Firstly, for each cloud user, user registration is performed by acquiring information from various sensors. Second, information is collected from the registered cloud users by means of the Jaccard Index Cat Gradient Boosting Classifier algorithm. Such a proposed algorithm imposes a lower error rate and minimizes classification time, ensuring the most reliable and secured data communication between cloud users. To ensure secure data communication, weak learners' results are combined to form a strong classifier. The proposed method is implemented in Java and tested on the CloudSim simulator for classification accuracy, classification time, and error rate. The experimental results reveal the JICGBC-SDC method increases the performance of secured data communication for error rate by 77%, classification time by 79% and classification accuracy by 25% as compared to the state-of-the-art work.

Evaluating the Impact of Cryptographic Algorithms on Network Performance

Cryptographic algorithms enable secure data communication over public insecure networks. Though they enhance network security, complex cryptographic operations consume substantial amounts of computing resources, introducing significant network overhead costs. This study aims to find the cryptographic algorithm that can efficiently utilize network resources. The study evaluates three cryptographic algorithms with different file formats on varying numbers of node densities. The NS-3 simulator was used to measure latency, data throughput, end-to-end delay, packet delivery ratio, and packet loss of files in text, image, and audio formats. The results find AES as better than DES and 3DES for a large number of node densities for the three file formats in terms of latency, data throughput, end-to-end delay, and packet delivery ratio. However, DES has the lowest packet loss as AES records the highest packet loss. The findings provide researchers avenues for further research and the practitioners the choice of suitable algorithms based on the overhead performance.

HSC-MET: Heterogeneous signcryption scheme supporting multi-ciphertext equality test for Internet of Drones.

Internet of Drones (IoD) is considered as a network and management architecture, which can enable unmanned aerial vehicles (UAVs) to collect data in controlled areas and conduct access control for UAVs. However, the current cloud-assisted IoD scheme cannot efficiently achieve secure communication between heterogeneous cryptosystems, and does not support multi-ciphertext equality tests. To improve the security and performance of traditional schemes, we propose a heterogeneous signcryption scheme (HSC-MET) that supports multi-ciphertext equality test. In this paper, we use a multi-ciphertext equality test technique to achieve multi-user simultaneous retrieval of multiple ciphertexts safely and efficiently. In addition, we adopt heterogeneous signcryption technology to realize secure data communication from public key infrastructure (PKI) to certificateless cryptography (CLC). At the same time, the proposed scheme based on the computation without bilinear pairing, which greatly reduces the computational cost. According to the security and performance analysis, under the random oracle model (ROM), the confidentiality, unforgeability and number security of HSC-MET are proved based on the computational Diffie-Hellman (CDH) problem.

An Empirical Client Cloud Environment to Secure Data Communication with Alert Protocol

Cloud computing is a technology that provides resources and utility services based on user demand. Due to this demand, efficient cloud security protocols are highly required, especially at the time of data communication for user authentication and data aggregation. The data communication scenarios are majorly affected by the security threats in the cloud computing environment. This article provides a practical approach to developing an efficient and empirical cloud framework in terms of cloud protocol. The framework uses fuzzy c-means (FCM) algorithm to group data, and calculation is done individually or associatively to rank the text data. Uploaded data are passed to a simple additive weighting (SAW) algorithm for ranking and making decision selection. The framework executes in three phases, namely data preprocessing, clustering, and automatic data security with an alert mechanism. The process is completely automated so there is no need of considering the individual files for the processing and the data held will be appropriately correlated with the sharing inter-cloud environment. To inspect security issues, the proposed framework is secured by three different security algorithms. The encryption process is completed by Rivest Cipher 6 (RC6); the substitution process is done by Advanced Encryption Standard (AES); and key generation is done by RC6, AES, and Rivest-Shamir-Adleman (RSA) approaches collectively. Based on the given situations, these standard approaches were automatically applied separately or collectively. Unauthorized access trapping and data deletion mechanism are also provided in the proposed framework. The experimental results with a comparative study depicted the effectiveness of the proposed work.