Biblio

Autonomous robots are increasingly widespread in our society. These robots need to be safe, reliable, respectful of privacy, not manipulable by external agents, and capable of offering explanations of their behavior in order to be accountable and acceptable in our societies. Companies offering robotic services will need to provide mechanisms to address these issues using High Performance Computing (HPC) facilities, where logs and off-line forensic analysis could be addressed if required, but these solutions are still not available in software development frameworks for robots. The aim of this paper is to discuss the implications and interactions among cybersecurity, safety, and explainability with the goal of making autonomous robots more trustworthy.

Aiming at the security problems of traditional smart grid data security storage strategy, this paper proposes a smart grid data security storage strategy based on cloud computing platform. Based on the analysis of cloud computing and cloud storage, the security storage of smart grid data is modeled to improve the security storage performance of power system. The dynamic key mechanism is introduced to obtain the initial key information in the key chain and generate the dynamic secret key. The hyperchaotic system is used to obtain the modified bit plane code in the key chain to form the context and decision of data storage. MQ arithmetic encoder is used for entropy coding to generate the corresponding data storage compressed code stream, and the smart grid data storage key is improved. Combined with encryption processing and decryption processing, the secure storage of smart grid data is realized. The experimental results show that the smart grid data security storage strategy based on cloud computing platform increases the security of smart grid data storage.

Cloud computing has included an essential part of its industry and statistics garage is the main service provided, where a huge amount of data can be stored in a virtual server. Storing data in public platforms may be vulnerable to threats. Consequently, the obligation of secure usage and holistic backup of statistics falls upon the corporation providers. Subsequently, an affordable and compliant mechanism of records auditing that permits groups to audit the facts stored in shared clouds whilst acting quick and trouble- unfastened healing might be a fairly sought-after cloud computing task concept. There is a lot of advantage in growing this domain and there is considerable precedence to follow from the examples of dropbox, google power among others.

Fog Computing was envisioned to solve problems like high latency, mobility, bandwidth, etc. that were introduced by Cloud Computing. Fog Computing has enabled remotely connected IoT devices and sensors to be managed efficiently. Nonetheless, the Fog-Cloud paradigm suffers from various security and privacy related problems. Blockchain ensures security in a trustless way and therefore its applications in various fields are increasing rapidly. In this work, we propose a Fog-Cloud architecture that enables Blockchain to ensure security, scalability, and privacy of remotely connected IoT devices. Furthermore, our proposed architecture also efficiently manages common problems like ever-increasing latency and energy consumption that comes with the integration of Blockchain in Fog-Cloud architecture.

The network security problem brought by the cloud computing has become an important issue to be dealt with in information construction. Since anomaly detection and attack detection in cloud environment need to find the vulnerability through the reverse analysis of data flow, it is of great significance to carry out the reverse analysis of unknown network protocol in the security application of cloud environment. To solve this problem, an improved mining method on bitstream protocol association rules with unknown type and format is proposed. The method combines the location information of the protocol framework to make the frequent extraction process more concise and accurate. In addition, for the frame separation problem of unknown protocol, we design a hierarchical clustering algorithm based on Jaccard distance and a frame field delimitation method based on the proximity of information entropy between bytes. The experimental results show that this technology can correctly resolve the protocol format and realize the purpose of anomaly detection in cloud computing, and ensure the security of cloud services.

IPsec is widely used for internet security because it offers confidentiality, integrity, and authenticity also protects from replay attacks. IP Security depends on numerous frameworks, organization propels, and cryptographic techniques. IPsec is a heavyweight complex security protocol suite. Because of complex architecture and implementation processes, security implementers prefer TLS. Because of complex implementation, it is impractical to manage over the IoT devices. We propose a simplified and lite version of internet security protocol implemented with only ESP. For encryption, we use AES, RAS-RLP public key cryptography.

Integrity verification of cloud data is of great importance for secure and effective cloud storage since attackers can change the data even though it is encrypted. Traditional integrity verification schemes only let the client know the integrity status of the remote data. When the data is corrupted, the system cannot hold the server accountable. Besides, almost all existing schemes assume that the users are credible. Instead, especially in a dynamic operation environment, users can deny their behaviors, and let the server bear the penalty of data loss. To address the issues above, we propose an accountable dynamic storage integrity verification (ADS-IV) scheme which provides means to detect or eliminate misbehavior of all participants. In the meanwhile, we modify the Invertible Bloom Filter (IBF) to recover the corrupted data and use the Mahalanobis distance to calculate the degree of damage. We prove that our scheme is secure under Computational Diffie-Hellman (CDH) assumption and Discrete Logarithm (DL) assumption and that the audit process is privacy-preserving. The experimental results demonstrate that the computational complexity of the audit is constant; the storage overhead is \$O(\textbackslashtextbackslashsqrt n )\$, which is only 1/400 of the size of the original data; and the whole communication overhead is O(1).As a result, the proposed scheme is not only suitable for large-scale cloud data storage systems, but also for systems with sensitive data, such as banking systems, medical systems, and so on.

In a cloud data center, the client requests are catered by placing the services in its servers. Such services are deployed through a sandboxing platform to ensure proper isolation among services from different users. Due to the lightweight nature, containers have become increasingly popular to support such sandboxing. However, for supporting effective and efficient data center resource usage with minimum resource footprints, improving the containers' consolidation ratio is significant for the cloud service providers. Towards this end, in this paper, we propose an exciting direction to significantly boost up the consolidation ratio of a data-center environment by effectively managing the containers' states. We observe that many cloud-based application services are event-triggered, so they remain inactive unless some external service request comes. We exploit the fact that the containers remain in an idle state when the underlying service is not active, and thus such idle containers can be checkpointed unless an external service request comes. However, the challenge here is to design an efficient mechanism such that an idle container can be resumed quickly to prevent the loss of the application's quality of service (QoS). We have implemented the system, and the evaluation is performed in Amazon Elastic Compute Cloud. The experimental results have shown that the proposed algorithm can manage the containers' states, ensuring the increase of consolidation ratio.

The Open Data Cube (ODC), created and facilitated by the Committee on Earth Observation Satellites (CEOS), is an open source software architecture that continues to gain global popularity through the integration of analysis-ready data (ARD) on cloud computing frameworks. In 2021, CEOS released a new ODC sandbox that provides global users with a free and open programming interface connected to Google Earth Engine datasets. The open source toolset allows users to run application algorithms using a Google Colab Python notebook environment. This tool demonstrates rapid creation of science products anywhere in the world without the need to download and process the satellite data. Basic operation of the tool will support many users but can also be scaled in size and scope to support enhanced user needs. The creation of the ODC sandbox was prompted by the migration of many CEOS ARD satellite datasets to the cloud. The combination of these datasets in an interoperable data cube framework will inspire the creation of many new application products and advance open science.

Simulating quantum field theories on a quantum computer is one of the most exciting fundamental physics applications of quantum information science. Dynamical time evolution of quantum fields is a challenge that is beyond the capabilities of classical computing, but it can teach us important lessons about the fundamental fabric of space and time. Whether we may answer scientific questions of interest using near-term quantum computing hardware is an open question that requires a detailed simulation study of quantum noise. Here we present a large scale simulation study powered by a multi-node implementation of qsim using the Google Cloud Platform. We additionally employ newly-developed GPU capabilities in qsim and show how Tensor Processing Units — Application-specific Integrated Circuits (ASICs) specialized for Machine Learning — may be used to dramatically speed up the simulation of large quantum circuits. We demonstrate the use of high performance cloud computing for simulating ℤ2 quantum field theories on system sizes up to 36 qubits. We find this lattice size is not able to simulate our problem and observable combination with sufficient accuracy, implying more challenging observables of interest for this theory are likely beyond the reach of classical computation using exact circuit simulation.

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.

This paper examines the fast approaching highly secure and content centric data sharing architecture Named Data Networking. The content name plays the key role in NDN. Most of the users are interested only in the content or information and thereby the host centric internet architecture is losing its importance. Different naming conventions and caching strategies used in Named Data Networking based applications have been discussed in this study. The convergence of NDN with the vehicular networks and the ongoing studies in it will make the path to Intelligent Transportation system more optimized and efficient. It describes the future internet and this idea has taken root in most of the upcoming IOT applications which are going to conquer every phase of life. Though it is in its infancy stage of development, NDN will soon take over traditional IP Architecture.

The development and popularization of big data technology bring more convenience to users, it also bring a series of computer network security problems. Therefore, this paper will briefly analyze the network security threats faced by users under the background of big data, and then combine the application function of computer network security defense system based on big data to propose an architecture design of computer network security defense system based on big data.

This paper proposes a novel network intrusion detection algorithm based on the combination of Subspace Clustering (SSC) and BP neural network. Firstly, we perform a subspace clustering algorithm on the network data set to obtain different subspaces. Secondly, BP neural network intrusion detection is carried out on the data in different subspaces, and calculate the prediction error value. By comparing with the pre-set accuracy, the threshold is constantly updated to improve the ability to identify network attacks. By comparing with K-means, DBSCAN, SSC-EA and k-KNN intrusion detection model, the SSC-BP neural network model can detect the most attacked networks with the lowest false detection rate.

The utilization of "cloud storage services (CSS)", empowering people to store their data in cloud and avoid from maintenance cost and local data storage. Various data integrity auditing (DIA) frameworks are carried out to ensure the quality of data stored in cloud. Mostly, if not all, of current plans, a client requires to utilize his private key (PK) to generate information authenticators for knowing the DIA. Subsequently, the client needs to have hardware token to store his PK and retain a secret phrase to actuate this PK. In this hardware token is misplaced or password is forgotten, the greater part of existing DIA plans would be not able to work. To overcome this challenge, this research work suggests another DIA without "private key storage (PKS)"plan. This research work utilizes biometric information as client's fuzzy private key (FPK) to evade utilizing hardware token. In the meantime, the plan might in any case viably complete the DIA. This research work uses a direct sketch with coding and mistake correction procedures to affirm client identity. Also, this research work plan another mark conspire that helps block less. Verifiability, yet in addition is viable with linear sketch Keywords– Data integrity auditing (DIA), Cloud Computing, Block less Verifiability, fuzzy biometric data, secure cloud storage (SCS), key exposure resilience (KER), Third Party Auditor (TPA), cloud audit server (CAS), cloud storage server (CSS), Provable Data Possession (PDP)

Cloud storage technology enables users to outsource local data to cloud service provider (CSP). In spite of its copious advantages, how to ensure the integrity of data has always been a significant issue. A variety of provable data possession (PDP) scheme have been proposed for cloud storage scenarios. However, the participation of centralized trusted third-party auditor (TPA) in most of the previous work has brought new security risks, because the TPA is prone to the single point of failure. Furthermore, the existing schemes do not consider the fair arbitration and lack an effective method to punish the malicious behavior. To address the above challenges, we propose a novel blockchain-based decentralized data integrity auditing scheme without the need for a centralized TPA. By using smart contract technique, our scheme supports automatic compensation mechanism. DO and CSP must first pay a certain amount of ether for the smart contract as deposit. The CSP gets the corresponding storage fee if the integrity auditing is passed. Otherwise, the CSP not only gets no fee but has to compensate DO whose data integrity is destroyed. Security analysis shows that the proposed scheme can resist a variety of attacks. Also, we implement our scheme on the platform of Ethereum to demonstrate the efficiency and effectiveness of our scheme.

Nowadays , cloud -based technology has been enlarged depends on the human necessities in the world. A lot of technologies is discovered that serve the people in different ways of cloud -based security and best resource allocation. Cloud-based technology is the essential factor to the resources like hardware, software for effective resource utilization . The securing applications enabled security mechanism enables the vital role for cloud -based web security through the secured password. The violation of data by the unauthorized access of users concerns many web developers and application owners . Web security enables the cloud-based password management system that illustrates the data storage and the web passwords access through the "Cloud framework". Web security, End-to-end passwords , and all the browser -based passwords could belong to the analysis of web security . The aim is to enhance system security. Thus, sensitive data are sustained with security and privacy . In this paper , the proposed Password Management via cloud-based web security gets to attain . An efficient Double Layer Password Encryption (DLPE ) algorithm to enable the secured password management system . Text -based passwords continue to be the most popular method of online user identification . They safeguard internet accounts with important assets against harmful attempts on passwords. The security of passwords is dependent on the development of strong passwords and keeping them from being stolen by intruders . The proposed DLPE algorithm perceived the double - layer encryption system as an effective security concern. When the data user accesses the user Login , the OTP generates via mail /SMS , and the original message is encrypted using public key generation. Then the text of data gets doubly encrypted through the cloud framework . The private key is used to decipher the cipher text . If the OTP gets matched , the text is to be decrypted over the text data . When double encryption happens , the detection of data flaws, malicious attacks , application hackers gets reduced and the strong password enabled double-layer encryption attained the secured data access without any malicious attackers . The data integrity , confidentiality enabled password management . The ability to manage a distributed systems policy like the Double Layer Password encryption technique enables password verification for the data used to highly secure the data or information.

Distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. Processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential Processing Techniques. This paper proposes a Multi-Core Parallel Processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing.

The advance of smart eHealthcare and cloud computing techniques has propelled an increasing number of healthcare centers to outsource their healthcare data to the cloud. Meanwhile, in order to preserve the privacy of the sensitive information, healthcare centers tend to encrypt the data before outsourcing them to the cloud. Although the data encryption technique can preserve the privacy of the data, it inevitably hinders the query functionalities over the outsourced data. Among all practical query functionalities, the similarity range query is one of the most popular ones. However, to our best knowledge, many existing studies on the similarity range query over outsourced data still suffer from the efficiency issue in the query process. Therefore, in this paper, aiming at improving the query efficiency, we propose an efficient privacy-preserving similarity range query scheme based on the precomputed distance technique. In specific, we first introduce a pre-computed distance based similarity range query (PreDSQ) algorithm, which can improve the query efficiency by precomputing some distances. Then, we propose our privacy-preserving similarity query scheme by applying an asymmetric scalar-product-preserving encryption technique to preserve the privacy of the PreDSQ algorithm. Both security analysis and performance evaluation are conducted, and the results show that our proposed scheme is efficient and can well preserve the privacy of data records and query requests.

The popularity of cloud storage among data users is due to easy maintenance, and no initial infrastructure setup cost as compared to local storage. However, although the data users outsource their data to cloud storage (a third party) still, they concern about their physical data. To check whether the data stored in the cloud storage has been modified or not, public auditing of the data is required before its utilization. To audit over vast outsourced data, the availability of the auditor is an essential requirement as nowadays, data owners are using mobile devices. But unfortunately, a single auditor leads to a single point of failure and inefficient to preserve the security and correctness of outsourced data. So, we introduce a distributed public auditing scheme which is based on peer-to-peer (P2P) architecture. In this work, the auditors are organized using a distributed hash table (DHT) mechanism and audit the outsourced data with the help of a published hashed key of the data. The computation and communication overhead of our proposed scheme is compared with the existing schemes, and it found to be an effective solution for public auditing on outsourced data with no single point of failure.

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.

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.

This paper describes a design of IoT enabled real-time bus tracking system. In this work a bus tracking mobile phone app is developed, using that people can exactly locate the bus status and time to bus arrival at bus-stop. This work uses high-frequency RFID tags at buses and RFID receivers at busstops and with NodeMCU real-time RIFD tagging (bus running) information is collected and uploaded on the cloud. Users can access the bus running and status from the cloud on the mobile app in real-time.

Cyber resiliency in Cloud computing is one of the most important capability of an enterprise network that provides continues ability to withstand and quick recovery from the adversary conditions. This capability can be measured through cybersecurity risk assessment techniques. However, cybersecurity risk management studies in cloud computing resiliency approaches are deficient. This paper proposes resilient cloud cybersecurity risk assessment tailored specifically to Dropbox with two methods: technical-based solution motivated by a cybersecurity risk assessment of cloud services, and a target personnel-based solution guided by cybersecurity-related survey among employees to identify their knowledge that qualifies them withstand to any cyberattack. The proposed work attempts to identify cloud vulnerabilities, assess threats and detect high risk components, to finally propose appropriate safeguards such as failure predicting and removing, redundancy or load balancing techniques for quick recovery and return to pre-attack state if failure happens.

Cloud computing adoption is on the rise. Many organizations have decided to shift their workload to the cloud to benefit from the scalability, resilience, and cost reduction characteristics. Mobile Cloud Computing (MCC) is an emerging computing paradigm that also provides many advantages to mobile users. Mobile devices function on wireless internet connectivity, which entails issues of limited bandwidth and network congestion. Hence, the primary focus of Web applications in MCC is on improving performance by quickly fulfilling customer's requests to improve service satisfaction. This paper investigates a new approach to caching data in these applications using Redis, an in-memory data store, to enhance Quality of Service. We highlight the two implementation approaches of fetching the data of an application either directly from the database or from the cache. Our experimental analysis shows that, based on performance metrics such as response time, throughput, latency, and number of hits, the caching approach achieves better performance by speeding up the data retrieval by up to four times. This improvement is of significant importance in mobile devices considering their limitation of network bandwidth and wireless connectivity.