Data Center Security Research Articles

Security, QoS and energy aware optimization of cloud-edge data centers using game theory and homomorphic encryption: Modeling and formal verification

Privacy and efficient data processing are crucial research areas in the field of outsourcing computing. Thus, the contribution of the paper is twofold. First of all, we develop a secure offloading technique that can process data while preserving privacy. Although RSA and Paillier cryptosystems are highly beneficial for mathematical operations on basic encrypted data due to their homomorphic properties, images are processed using permutation-ordered binary (POB) and Shamir's secret sharing (SSS). Second, a non-cooperative game theory model was formulated to examine the interactions between service providers, characterizing each edge as an M/M/1 system with an infinite queue. Through formal verification using rigorous mathematical reasoning, the suggested strategies for task offloading and resource allocation are shown to be effective in terms of security, quality of service (QoS), and energy consumption. The best-response dynamics approach is used to calculate the final strategy for each participant and assess how fairly resources are allocated. Finally, the potential of our approach is demonstrated numerically, revealing a faster convergence towards attaining Nash equilibrium in a duopoly cloud-edge data center market, granted that the necessary and sufficient conditions are met.

Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach

Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach

A TECHNIQUE FOR PROVIDING SECURED UNINTERRUPTED SERVICES OF A DATA CENTER

This paper presents a comprehensive technique for ensuring the secured, uninterrupted operation of data centers, addressing the dual challenges of resilience against disruptions and robust security against cyber-physical threats. By integrating advanced cryptographic methods, dynamic access control, AI-based anomaly detection, and sustainable power solutions, the proposed approach offers a novel framework for enhancing data center reliability and security. The study synthesizes multidisciplinary research, including cybersecurity, power engineering, and environmental sustainability, to construct a resilient infrastructure capable of supporting the critical functions dependent on data centers. The effectiveness of the technique is evaluated through a series of simulated scenarios, demonstrating its potential to significantly reduce downtime and mitigate security risks. This research contributes to the field by proposing a holistic strategy that not only secures data centers from emerging threats but also ensures their continuous operation, thereby supporting the digital ecosystem's growth and reliability.

Coupling of flexible phase change materials and servers for improving the security of data centers in emergency situations

Data centers with high-power servers cause massive energy consumption. The service object of each server is different, and the server will change from low-load to high-load when an emergency occurs, which increases heat generation. If the heat cannot be removed timely, the temperature will rise rapidly, affecting the operation and safety of servers. A common method is to change the parameters of the cooling system, which will lead to an exponential increase in energy consumption. In this paper, novel flexible phase change material (FPCM) is used for temperature control of servers. Firstly, paraffin/styrene-ethylene-butene-styrene block copolymer/expanded graphite (PA/SEBS/EG) with high flexibility, high thermal conductivity (0.72 W∙m−1∙K−1) and high enthalpy (184.77 kJ·kg−1) was prepared. Then, PA/SEBS/EG was placed above the server, and the temperature was reduced by 3.29 °C compared to without FPCM at an inlet temperature of 26 °C, and the temperature rising rate can be reduced by above 60 %, indicating that FPCM has excellent thermal management effect. Finally, Flotherm software was used to optimize the sizes and parameters of FPCM, the results show 53 × 51 × 16 mm FPCM with 12 wt% EG can reduce the temperature of 13.74 %, 14.03 %, and 14.61 % compared to without FPCM at 1.25, 1.5, 1.75 times power, respectively, and extend 771 s to exceed 80 °C under 1.75 times power. The lifespan can be increased by 3.53 times, and the cooling demand can be reduced by 24.73 %. This study provides solutions for the safe operation and energy consumption reduction of data centers.

Data Center Control Application With Fuzzy Logic

Data centers are systems that host devices utilizing recording and communication technologies, which are expected to operate securely and accurately. Consequently, transforming data centers into smart environments for control purposes has become a significant area of focus. In this study, we monitor the cabinet environment within data centers and ensure that the control system reaches the predetermined optimal state values in the event of undesirable situations. Threshold control was implemented for humidity and flame data, while fuzzy logic theory was applied to temperature data. Fuzzy clusters can be adjusted according to the data center's location at the user's request. This approach allows users to input desired optimal and threshold values into the system, which are then evaluated based on the situation. The designed system ensures data center security with minimal personnel involvement. Additionally, all problematic events are recorded in the system, enabling them to be viewed on a webpage and communicated to designated personnel via email. In the conducted study, the fuzzy-controlled temperature value outputs are reported as heating (40%), cooling (53%), and instances where the system does not perform heating or cooling.

Data-driven transferred energy management strategy for hybrid electric vehicles via deep reinforcement learning

Real-time applications of energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are the harshest requirements for researchers and engineers. Inspired by the excellent problem-solving capabilities of deep reinforcement learning (DRL), this paper proposes a real-time EMS via incorporating the DRL method and transfer learning (TL). The related EMSs are derived from and evaluated on the real-world collected driving cycle dataset from Transportation Secure Data Center (TSDC). The concrete DRL algorithm is proximal policy optimization (PPO) belonging to the policy gradient (PG) techniques. For specification, many source driving cycles are utilized for training the parameters of deep networks based on PPO. The learned parameters are transformed into the target driving cycles under the TL framework. The EMSs related to the target driving cycles are estimated and compared in different training conditions. Simulation results indicate that the presented transfer DRL-based EMS could effectively reduce the time consumption and guarantee the control performance.

Authentication, access, and monitoring system for critical areas with the use of artificial intelligence integrated into perimeter security in a data center.

Perimeter security in data centers helps protect systems and the data they store by preventing unauthorized access and protecting critical resources from potential threats. According to the report of the information security company SonicWall, in 2021, there was a 66% increase in the number of ransomware attacks. In addition, the message from the same company indicates that the total number of cyber threats detected in 2021 increased by 24% compared to 2019. Among these attacks, the infrastructure of data centers was compromised; for this reason, organizations include elements Physical such as security cameras, movement detection systems, authentication systems, etc., as an additional measure that contributes to perimeter security. This work proposes using artificial intelligence in the perimeter security of data centers. It allows the automation and optimization of security processes, which translates into greater efficiency and reliability in the operations that prevent intrusions through authentication, permit verification, and monitoring critical areas. It is crucial to ensure that AI-based perimeter security systems are designed to protect and respect user privacy. In addition, it is essential to regularly monitor the effectiveness and integrity of these systems to ensure that they function correctly and meet security standards.

Security and storage improvement in distributed cloud data centers by increasing reliability based on particle swarm optimization and artificial immune system algorithms

SummaryOne of the major concerns in security and storage improvement of data centers is due to the fact that there is a possibility of access to cloud information, and the distance between data centers for data transfer causes cloud storage problems. In this article, an encryption method using artificial immune system (AIS) is leveraged to secure sensitive data in data centers and particle swarm optimization (PSO) algorithm is exploited to improve the selection of storage services in distributed cloud data centers to store and transfer particular a data set between data centers. To obtain satisfied performance considering the transmitting cost, and smallest transmitting distance, we exploited one discrete PSO algorithm and AIS method and called PSO_AIS. To evaluate the proposed PSO_AIS algorithm, we designed the experiments and simulated it in MATLAB software. In the simulation, we conducted experiments on 13 data centers located in different locations in Iran. The experimental results expose that the proposed algorithm reduces 30% the distance and cost to select a particular data center for a particular data set. Experiments have been carried‐out to demonstrate the effectiveness of the proposed encryption method, which is based on AIS. The results showed that the encoding performance of the proposed method is better than other methods.

Sequential tree recognition method of sensitive data in energy big data center based on rule matching

In this paper, the sequential tree recognition method of sensitive data in energy big data center based on rule matching is studied, to accurately identify sensitive data in energy big data center, and improve the operation security of energy big data center. The RETE rule matching algorithm is used to match the sensitive data rules of the energy big data center. The algorithm automatically finds the optimal rete topology, reduces the join intermediate node data, and realizes rule matching. The data cut points after rule matching are divided into balanced cutting points and unbalanced cutting points. The maximum sorting mutual information only exists at the unbalanced cut points. The ordered decision tree can be constructed by traversing the unbalanced cutting points. The data to be identified can be retrieved in the form of data flow to obtain the word frequency, regional information and sensitivity of sensitive words, and the sensitive data can be identified according to the sensitivity calculation results. The experimental results show that the proposed method can effectively identify the sensitive data of energy big data center with high recognition accuracy, and can be applied to the practical application of energy big data center.

Enhancing Data Center Security: Comparative Analysis and Integration of OpenSSL, Venafi and Sensu

Data centers play a role, in today’s infrastructure supporting operations with vast networks of servers and storage systems. Safeguarding these data centers from cyber threats requires tools for managing SSL certificates and ongoing monitor- ing. This research assesses the effectiveness of OpenSSL, Venafi and Sensu in bolstering data center security. While OpenSSL offers support Venafi stands out for its automated certificate management and Sensu provides adaptable and scalable mon- itoring solutions. The combined use of these tools is suggested to enhance security measures and operational effectiveness within data centers. Recommendations for industry implementation and potential areas, for study are also explored.

Series Editorial: Cloud and Edge Computing

Cloud and Edge Computing are currently undergoing a sub-stantive transformation on several fronts, from applications to hardware, and from architectures to devices. This issue of the Cloud and Edge Computing Series solicited articles in the area of Cloud and Edge Computing to address the main issues concerned with evolving processes, supporting pedagogies, and applications in cloud computing, networking, and storages technologies. There have been advances on both the cloud and edge computing fronts that have affected this line of technology. Paradigms, such as computing in virtualization-based architectures, issues on geo-graphical constraints for deploying clouds, and the use of SDN/NFV in clouds, for example, were of special interest for this issue of the Series. Nevertheless, our attention was also focused on data center network (DCN) architectures, security, load balancing, and application data streaming supported by evidence from simulations, analysis, or experiments. Cloud and Edge Computing are also incorporated with the Internet of Things (IoT) ecosystem. IoT requiring multiple access edge computing systems is gaining momentum and considered to be deployed in smart cities, public safety, and e-health. The last, but not least important, factor we expected to see in articles was the standardization aspects of Cloud and Edge Computing technology. We were especially interested in articles that could address communications standards in networking for clouds, fogs, and edge computing.

Building Secure Data Center Networks

Building Secure Data Center Networks

Evaluation of Asian Countries using Data Center Security Index: A Spherical Fuzzy AHP-based EDAS Approach

The motivation of this paper is to assess and compare Asia Pacific Accreditation Cooperation (APAC) countries’ risk profiles that impact data centers’ (DCs) effectiveness. Because of the importance of DCs, security to protect a DC from external threats and attacks is an issue to be addressed. However, the conflicted nature of the evaluation criteria (such as infrastructure, energy, natural, business, political, and legal risks) makes it difficult to assess the countries’ profiles. The objective of this paper is to provide a systematic evaluation methodology using two different multi-criteria decision making (MCDM) approaches, namely spherical fuzzy analytical hierarchy process (SF-AHP) and EDAS (evaluation based on distance from average solution). While SF-AHP is preferred to prioritize the main and sub-criteria under imprecise decision making environment, EDAS is used to rank the countries’ DC profiles embedding the obtained criteria weights. The results of the methods have been compared with the ranking results presented in the report called as Data Centre Security Index (DCSI) is published by Technology Research Project Corporate (TRPC). Consequently, Singapore, Hong Kong and New Zealand rank in the top three due to their high electrification rates and secure internet servers.

Understanding In-Depth About Data Centre Security: Core Concepts & Market Growth

Abstract: Enterprises often perceive Data Centers as a key resource and demand dedicated security initiatives. Further, the emergence of security of the Data Center is playing a critical role in security-critical applications and data in a virtualized environment through optimal safeguarding. The providers of the Data Center have increased their focus. They have shown their participation in the market by enhancing products to augment the increasing demand for Data Center security solutions to address the rising needs of growing industries. This paper discusses various concepts of Data Center security along with its projected market growth on a global level. Keywords: Data Centers, Data Center Security, Data Center Security Importance, Data Center Security Market Growth, Data Center Security Practices

A Review of P4 Programmable Data Planes for Network Security

Network attacks show a trend of increased attack intensity, enhanced diversity, and more concealed attack methods, which put forward higher requirements for the performance of network security equipment. Unlike the SDN (software defined network) switch with a fixed-function data plane, switches with programmable data planes can help users realize more network protocols. Programming Protocol-independent Packet Processors (P4) is proposed to define the operations of the data plane and to implement user’s applications, e.g., data center networks, security, or 5G. This paper provides a review of research papers on solving network security problems with P4-based programmable data plane. The work can be organized into two parts. In the first part, the programming language P4, P4 program, architectures, P4 compilers, P4 Runtime, and P4 target are introduced according to the workflow model. The advantages of P4-based programmable switching in solving network security are analyzed. In the second part, the existing network security research papers are divided into four parts according to the perspectives of passive defense, active defense, and combination of multiple technologies. The schemes in each category are compared, and the core ideas and limitations are clarified. In addition, a detailed comparison is made for the research on the performance of P4 targets. Finally, trends and challenges related to the P4-based programmable data plane are discussed.

PENERAPAN METODE PIECES DALAM ANALISIS SISTEM INFORMASI DATA PERBAIKAN BARANG PADA PT. PETRA ABADI INTEGRASI

Information technology and computers are developing rapidly, making it easier for companies to process existing data into information needed for the continuity and progress of a company. PT. Petra Abadi Intergrasi is a private company engaged in services. Such as networking solutions, fiber optic solutions, data center solutions, communication solutions, security solutions, maintenance services and hardware repair especially in the technology sector. This company still uses bookkeeping in its operational activities, there are several problems, such as discrepancies in information, related to goods repair and information known to customers. Inaccurate goods repair reports and slow provision of reports and irregular goods repair data. Lack of customer information, resulting in irregular job desks and miscommunication with customers. The results of the analysis show that the company requires an appropriate and accurate service improvement. For this reason, a better product repair system in operational systems and customer service is needed.

Application of Wireless Sensor Networks in the Sensitive Data Security of Intelligent Data Center under the Big Data Environment

In the big data environment, wireless sensor networks (WSN) are constantly improving themselves and are widely used in all walks of life. The WSN collects, processes and transmits the information of the sensed object in the monitoring area. In recent years, WSN have also made good progress in environmental change detection, traffic control, and sensitive data security management. The purpose of this article is to study the application of WSN in the security management of sensitive data in the data center under the big data environment. This article first describes the characteristics of WSN, and discusses the security management requirements of sensitive data in WSN based on big data technology. Secondly, it analyzes the problems in the security management of sensitive data and conducts research from different directions. Finally, in response to these problems, the implementation of the WSN in the security management of sensitive data provides some help to solve the problem. Thereby effectively reducing the energy consumption of network nodes, reducing the amount of data transmitted by the network, and extending the life cycle of the WSN. Experimental research results show that the most demanded level in security management is data confidentiality, which is 47.15%, followed by data freshness and data integrity, which are 28.49% and 24.36%, respectively. In summary, it is necessary to increase efforts to improve the security management of sensitive data.

Combat, casualties, and compensation: Evidence from Iraq and Afghanistan

AbstractOur research examines the effect of combat deployments to Iraq and Afghanistan on casualties and combat exposure compensation policy. We use restricted data from the Defense Manpower Data Center (DMDC) and Social Security Administration (SSA) to construct a panel of all US Active Duty service members having served at some point during the years 2001–2012. Casualties disproportionately occur at higher rates among (i) young, white, males, (ii) enlisted personnel, (iii) less educated personnel, and (iv) those in combat job types. Our estimates indicate that overall US military personnel who deployed in an individual year to Iraq or Afghanistan had a 45 per 100,000 higher probability of death than non‐deployed military personnel who remained stateside. The increased fatal injury risk of deployed US military personnel is 15 times higher than the national average civilian workplace fatality rate, but roughly equal to the fatal injury risk faced in some of the most dangerous civilian occupations. Our estimates suggest a compensating wage differential equal to $861 per month would be appropriate in comparison to the current average of $1238 per month in danger pay provided to US military personnel deployed into combat zones. We recommend a revenue‐neutral approach in adjusting the pay structure for military members. For example, the military should consider increasing bonuses or base pay while simultaneously decreasing danger pay by an equivalent amount. Furthermore, it may be beneficial to adjust danger pay by service or job type to reflect fatality risk more accurately.

Information security threats encountered by Malaysian public sector data centers

<span>D</span><span lang="EN-AU">ata centers are primarily the main targets of cybercriminals and security threats as they host various critical information and communication technology (ICT) services. Identifying the threats and </span><span>managing the risks associated with data centers have become a major challenge as this will enable organizations to optimize their resources to focus on the most hazardous threats to prevent the potential risks and damages. The objective of this paper is to identify major ICT security threats to data centers in the Malaysian public sector and their causes. The data for this study was collected through interview sessions. A total of 33 respondents from various government organizations were interviewed. The results revealed that the technical threats, </span><span lang="EN-MY">spyware, phishing, bluesnarfing threats, social engineering and virus, trojan, malware, ransomware, viral websites threats are the major categories of threats often encountered by the malaysian public sector organizations. The</span><span> causes for these threats </span><span lang="EN-MY">are lack of budget, competent personnel, and manpower for security tasks, user awareness; lack of </span><span>compliances and monitoring; insufficient security policies and procedures as well as deliberate cyber attacks. The outcome of this study will give a greater degree of awareness and understanding to the ICT security officers, who are entrusted with data center security.</span>

Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Secondly, a large portion of time series data have complex seasonality features. Thirdly, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency.