Information Security: A Coordinated Strategy to Guarantee Data Security in Cloud Computing

This paper discusses different techniques and specialized procedures which can be used to effectively protect data from the owner to the cloud and then to the user. The next step involves categorizing the data using three encryption parameters provided by the user, which are Integrity, Availability, and Confidentiality (IAC). The data is secured through various methods such as SSL and MAC protocols to ensure data integrity checks, searchable encryption, and splitting the data into three parts for cloud storage. Dividing the data into three portions not only enhances security but also facilitates easier access. Access to the encrypted data requires the user to provide the login information and password of the owner. This paper also studies critical security issues like unauthorized servers, brute force attacks, threats from cloud service providers, and loss of user identity and password

Performance Comparison Analysis of Classification Methodologies for Effective Detection of Intrusions

Intrusion detection systems (IDS) are critical in many applications, including cloud environments. The intrusion poses a security threat and extracts privacy data and information from the cloud. The user has an Internet function that allows him to store personal information in the cloud environment. The cloud can be affected by various issues such as data loss, data breaches, lower security and lack of privacy due to some intruders. A single intrusion incident can result in data within computer and network systems being quickly stolen or deleted. Additionally, intrusions can cause damage to system hardware, resulting in significant financial losses and exposing critical IT infrastructure to risk. To overcome these issues, the study employs the performance comparison analysis of Autoencoder Convolutional neural network (AE+CNN), Random K-means clustering assisted deep neural network (RF+K-means+DNN), Autoencoder K-means clustering assisted long short term memory (AE+K-means+LSTM), Alexnet+Bi-GRU, AE+Alexnet+Bi-GRU and Wild horse AlexNet assisted Bi-directional Gated Recurrent Unit (WABi-GRU) models to choose the best methodology for effective detection of intrusions. The data needed for the analysis is collected from CICIDS2018, UNSW-NB15 and NSL-KDD datasets. The collected data are pre-processed using data normalization and data cleaning. Finally, through this research, the best model suitable for effective intrusion detection can be identified and used for further processes. The proposed models, such as RF+K-means+DNN, AE+K-Means+LSTM, AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU can obtain an accuracy of 99.278%, 99.33%, 99.45%, 99.50%, 99.65% for the CICIDS dataset 2018 for binary classification. In multi-class classification, the AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU can attain accuracy of 99.819%, 99.852% and 99.890%. In NSL-KDD, the AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU achieve accuracy of 99.34%, 99.546% and 99.7%. In UNSW-NB 15 dataset, AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU achieve accuracy of 99.313%, 99.399% and 99.53%. AlexNet Bi-GRU-based models can obtain better performances than other existing models

A Novel Hybrid Spotted Hyena-Swarm Optimization (HS-FFO) Framework for Effective Feature Selection in IOT Based Cloud Security Data

Internet of Things (IoT) has gained its major insight in terms of its deployment and applications. Since IoT exhibits more heterogeneous characteristics in transmitting the real time application data, these data are vulnerable to many security threats. To safeguard the data, machine and deep learning based security systems has been proposed. But this system suffers the computational burden that impedes threat detection capability. Hence the feature selection plays an important role in designing the complexity aware IoT systems to defend the security attacks in the system. This paper propose the novel ensemble of spotted hyena with firefly algorithm to choose the best features and minimise the redundant data features that can boost the detection system's computational effectiveness.  Firstly, an effective firefly optimized feature correlation method is developed.  Then, in order to enhance the exploration and search path, operators of fireflies are combined with Spotted Hyena to assist the swarms in leaving the regionally best solutions. The experimentation has been carried out using the different IoT cloud security datasets such as NSL-KDD-99 , UNSW and CIDCC -001 datasets and contrasted with ten cutting-edge feature extraction techniques, like PSO (particle swarm optimization), BAT, Firefly, ACO(Ant Colony Optimization), Improved PSO, CAT, RAT, Spotted Hyena, SHO and  BOC(Bee-Colony Optimization) algorithms. Results demonstrates the proposed hybrid model has achieved the better feature selection mechanism with less convergence  time and aids better for intelligent threat detection system with the high performance of detection

Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks

The relevance of this type of network is associated with the development and improvement of protocols, methods, and tools to verify routing policies and algorithmic models describing various aspects of SDN, which determined the purpose of this study. The main purpose of this work is to develop specialized methods to estimate the maximum end-to-end delay during packet transmission using SDN infrastructure. The methods of network calculus theory are used to build a model for estimating the maximum transmission delay of a data packet. The basis for this theory is obtaining deterministic evaluations by analyzing the best and worst-case scenarios for individual parts of the network and then optimally combining the best ones. It was found that the developed method of theoretical evaluation demonstrates high accuracy. Consequently, it is shown that the developed algorithm can estimate SND performance. It is possible to conclude the configuration optimality of elements in the network by comparing the different possible configurations. Furthermore, the proposed algorithm for calculating the upper estimate for packet transmission delay can reduce network maintenance costs by detecting inconsistencies between network equipment settings and requirements. The scientific novelty of these results is that it became possible to calculate the achievable upper data delay in polynomial time even in the case of arbitrary tree topologies, but not only when the network handlers are located in tandem. Doi: 10.28991/ESJ-2022-06-05-010 Full Text: PD

HANDLING WORK FROM HOME SECURITY ISSUES IN SALESFORCE

Security is a vital component when it is identified with an endeavor record or our genuine materials. To protect our home or valuable things like gold, cash we use bank storage administrations or underground secret storage spaces at home. Similarly, IT enterprises put tremendous measure of capital in expanding security to its business and the archives. Associations use cryptography procedures to get their information utilizing progressed encryption calculations like SHA-256, SHA-512, RSA-1024, RSA-2048 pieces’ key encryption and Elliptic Curve Cryptography (ECC) calculations. These industry standard calculations are difficult to break. For instance, to break RSA-2048-piece encryption key, an old-style PC needs around 300 trillion years. As indicated by the continuous examination, a quantum PC can break it in 10seconds, yet such a quantum PC doesn\u27t yet exist. Despite the fact that these cryptographic calculations guarantee an awesome degree of safety, there will be dependably a space for breaking the security. Programmers will attempt new techniques to break the security. Thus, the association likewise should continue to utilize new strategies to build the level and nature of the security. Now it is time to check how the security aspect is taken care of when the IT employees are at work from home. The 2020 year has made many professionals work from home because of the Covid-19 pandemic. The Covid-19 has transformed almost all organizations to work from home, this has become standard advice, and technology plays an important role during work from home to monitor the employee works and provide security when the work is being carried away from their respective organization. Employees\u27 information security awareness will become one of the most important parts of safeguarding against nefarious information security practices during this work from home. Most of the workers like the expediency of work from home and the flexibility provided for the employees. But in this situation, workers need guarantees that their privacy is secured when using company laptops and phones. Cyber security plays an important role in maintaining a secured environment when working from home. This work focusses on managing the security break attack in the course of work from home. The focus of the study is on dealing with security breaches that occur when salespeople operate from home. The problem of security isn\u27t new. Security issues existed prior to the lockdown or pandemic, but because the staff was working from the office at the time, the system administrator was available to address them. However, how can an employee\u27s laptop and account be secured when working from home? MFH\u27s salesforce has leveraged a variety of innovative technologies to address security concerns during their tenure. Because the IT behemoth Salesforce has made it possible for all employees, including freshly hired ones, to seek WFH on a permanent basis. To address the security breach difficulties faced by employees, the organization used a number of new approaches, including tracking working hours, raising password difficulty, employing VPN (virtual private network), mandating video during meetings, continuously checking right to use control, and MFA (multi-factor authentication). Improvement of existing multi-factor authentication (MFA) is the focused topic discussed in the thesis. To add an additional step of protection to the login process Blockchain technology is proposed and to identify the employee identification a hybrid recognition model is proposed using face and fingerprint recognition. This leads to the employee going through multiple processes to authenticate his or her identity in numerous ways in order to access the business laptop. This procedure entails connecting his or her laptop to his or her mobile phone or email account. Keywords: MFA, WFH, Cyber Security, Encryption, Decryption

Multipath Routing in Cloud Computing using Fuzzy based Multi-Objective Optimization System in Autonomous Networks

Intelligent houses and buildings, autonomous automobiles, drones, robots, and other items that are successfully incorporated into daily life are examples of autonomous systems and the Internet of Things (IoT) that have advanced as research areas. Secured data transfer in untrusted cloud applications has been one of the most significant requirements in the cloud in recent times. In order to safeguard user data from unauthorised users, encrypted data is stored on cloud servers. Existing techniques offer either security or efficiency for data transformation. They fail to retain complete security while undergoing significant changes. This research proposes novel technique in multipath routing based energy optimization of autonomous networks. The main goal of this research is to enhance the secure data transmission in cloud computing with network energy optimization. The secure data transmission is carried out using multi-authentication attribute based encryption with multipath routing protocol. Then the network energy has been optimized using multi-objective fuzzy based reinforcement learning. The experimental analysis has been carried out based on secure data transmission and energy optimization of the network. The parameters analysed in terms of scalability of 79%, QoS of 75%, encryption time of 42%, latency of 96%, energy efficiency of 98%, end-end delay of 45%

AI-driven approaches for optimizing the energy efficiency of integrated energy system

To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion

Empowering Non-Terrestrial Networks with Artificial Intelligence: A Survey

6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs

Towards Designing Energy-Efficient Secure Hashes

In computer security, cryptographic algorithms and protocols are required to ensure security of data and applications. This research investigates techniques to reduce the energy consumed by cryptographic hash functions. The specific hash functions considered are Message Digest-2 (MD2), Message Digest-5 (MD5), Secure Hash Algorithm-1 (SHA-1) and Secure Hash Algorithm-2 (SHA-2). The discussion around energy conservation in handheld devices like laptops and mobile devices is gaining momentum. Research has been done at the hardware and operating system levels to reduce the energy consumed by these devices. However, research on conserving energy at the application level is a new approach. This research is motivated by the energy consumed by anti-virus applications which use computationally intensive hash functions to ensure security. To reduce energy consumption by existing hash algorithms, the generic energy complexity model, designed by Roy et al. [Roy13], has been applied and tested. This model works by logically mapping the input across the eight available memory banks in the DDR3 architecture and accessing the data in parallel. In order to reduce the energy consumed, the data access pattern of the hash functions has been studied and the energy complexity model has been applied to hash functions to redesign the existing algorithms. These experiments have shown a reduction in the total energy consumed by hash functions with different degrees of parallelism of the input message, as the energy model predicted, thereby supporting the applicability of the energy model on the different hash functions chosen for the study. The study also compared the energy consumption by the hash functions to identify the hash function suitable for use based on required security level. Finally, statistical analysis was performed to verify the difference in energy consumption between MD5 and SHA2

Definition and specification of connectivity and QoE/QoS management mechanisms – final report

This document summarizes the WP5 work throughout the project, describing its functional architecture and the solutions that implement the WP5 concepts on network control and orchestration. For this purpose, we defined 3 innovative controllers that embody the network slicing and multi tenancy: SDM-C, SDM-X and SDM-O. The functionalities of each block are detailed with the interfaces connecting them and validated through exemplary network processes, highlighting thus 5G NORMA innovations. All the proposed modules are designed to implement the functionality needed to provide the challenging KPIs required by future 5G networks while keeping the largest possible compatibility with the state of the art