In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.

A Recent Survey on Internet of Things (IoT) Communication Protocols

A Feature Similarity Machine Learning Model for DDoS Attack Detection in Modern Network Environments for Industry 4.0

Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.

https://www.mdpi.com/2073-8994/14/6/1095/pdf?version=1653623569

Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

Now-a-days, Cybersecurity attacks are becoming increasingly sophisticated and presenting a growing threat to individuals, private and public sectors, especially the Denial Of Service attack (DOS) and its variant Distributed Denial Of Service (DDOS). Dealing with these dangerous threats by using traditional mitigation solutions suffers from several limits and performance issues. To overcome these limitations, Machine Learning (ML) has become one of the key techniques to enrich, complement and enhance the traditional security experiences. In this context, we focus on one of the key processes that improve and optimize Machine Learning DOS-DDOS predicting models: DOS-DDOS feature selection process, particularly the wrapper process. By studying different DOS-DDOS datasets, algorithms and results of several research projects, we have reviewed and evaluated the impact on used wrapper strategies, number of DOS-DDOS features, and many commonly used metrics to evaluate DOS-DDOS prediction models based on the optimized DOS-DDOS features. In this paper, we present three important dashboards that are essential to understand the performance of three wrapper strategies commonly used in DOS-DDOS ML systems: heuristic search algorithms, meta-heuristic search and random search methods. Based on this review and evaluation study, we can observe some of wrapper strategies, algorithms, DOS-DDOS features with a relevant impact can be selected to improve the DOS-DDOS ML existing solutions.

/pdf/predicting-dos-ddos-attacks-review-and-evaluation-study-of-59byit7xme.pdf

Predicting DOS-DDOS Attacks: Review and Evaluation Study of Feature Selection Methods based on Wrapper Process

The problem of identifying Distributed Denial of Service (DDos) attacks is fundamentally a classification problem in machine learning. In relevance to Cloud Computing, the task of identification of DDoS attacks is a significantly challenging problem because of computational complexity that has to be addressed. Fundamentally, a Denial of Service (DoS) attack is an intentional attack attempted by attackers from single source which has an implicit intention of making an application unavailable to the target stakeholder. For this to be achieved, attackers usually stagger the network bandwidth, halting system resources, thus causing denial of access for legitimate users. Contrary to DoS attacks, in DDoS attacks, the attacker makes use of multiple sources to initiate an attack. DDoS attacks are most common at network, transportation, presentation and application layers of a seven-layer OSI model. In this paper, the research objective is to study the problem of DDoS attack detection in a Cloud environment by considering the most popular CICIDS 2017 benchmark dataset and applying multiple regression analysis for building a machine learning model to predict DDoS and Bot attacks through considering a Friday afternoon traffic logfile.

A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression

Data security is a key concern in cloud computing . With the increase in attempts to hack a virtual cloud server in order to access the data stored there privately, more and more measures are being taken to ensure a reliable and secure cloud. Enhancements are made on traditional cryptography algorithms so that they are more suited to the distributed, virtual and multi-tenant environment of the cloud. Block-chaining is the newest technique in the fields of computing. It is basically a growing list of records, called as “blocks”, which are linked and secured using cryptography. In this project, we are aiming to implement an encryption algorithm which works on the principles of block-chains, to ensure the security and integrity of a cloud-based system. The infallible nature of block-chains is what makes it perfect for cloud server security.

Server Security in Cloud Computing Using Block-Chaining Technique

The cloud computing is a computing technology that allows us to share the pool of configurable sources where the data of individuals or the organisations can be stored remotely those can be accessible on-demand high quality applications. Even though there are many advantages associated with cloud computing still it brings new challenges in terms of security provided for the data storage providers as sensitive information of individuals is stored in these cloud storage providers. The owners of data expect a cloud data storage provider to be ensured with high servicelevel requirements. To ensure the deployment of cloud data storage service with security levels, some efficient methods has to be designed for the verification of the correctness of data. This paper proposes architecture for cloud computing that has a trusted entity with expertise and capability to assess cloud storage security in assistance of data owner request. The main aim of this paper is to enable public risk auditing protocols with which data owners can gain trust in cloud.

/pdf/cloud-data-storage-services-considering-public-audit-for-2rqacwpc7s.pdf

Cloud Data Storage Services Considering Public Audit for Security

DDoS attacks are the attacks carried in distributed environments. For this to be achieved attackers usually stagger the network bandwidth, halt system resources thus causing denial of access for legitimate users. The problem of identifying Distributed Denial of Service attacks in cloud environments is a significantly challenging research problem because of the computational complexity issues that are to be addressed. In this paper, the research objective is to study the problem of DDoS attack detection in cloud environment by considering CICIDS 2017 benchmark dataset and build a multiple regression-based machine learning model. In addition to evaluating the MAPE and prediction accuracy values of the multiple regression model, visualizations of residual plots and fit charts of the model are also studied.

Swathi Sambangi

Papers

A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression

A Feature Similarity Machine Learning Model for DDoS Attack Detection in Modern Network Environments for Industry 4.0

Server Security in Cloud Computing Using Block-Chaining Technique

Cloud Data Storage Services Considering Public Audit for Security

Multi Linear Regression Model to Detect Distributed Denial of Service Attacks in Cloud Environments