Information model operation and maintenance data network security construction based on partitioned blockchain

This study aims to: develop a Scramsis game that contains basic programming material for class X TKJ SMK Muhammadiyah 1 Wates and test the quality of the Scramsis game based on the ISO 25010 quality standard. This research is a research and development research using the Waterfall model which includes communication, planning, modeling, construction, and distribution. The functional suitability quality test was carried out by 2 developers, while the usability test was carried out by students of class X TKJ SMK Muhammadiyah 1 Wates. The results of this study are: (1) Game Scramsis has six main menus, namely let's play, how to play, settings, profiles, instructions, and exit. Scramsis has 3 different levels of difficulty.. Game Scramsis got a score of 100% on the functional suitability quality test which means very high quality, 100% on the compatibility quality test which means very high quality. Scramsis' performance efficiency test is seen from CPU and memory usage, the average CPU usage of Scramsis is 2.71%, when compared to the CPU limit set by little eye, which is 15%, this usage is still very safe. Scramsis' performance efficiency test is seen from CPU and memory usage, the average CPU usage of Scramsis is 2.71%, when compared to the CPU limit set by little eye, which is 15%, this usage is still very safe. The use of scramsis memory is 129 MB and does not experience force close. The usability test was carried out on class X TKJ. Scramsis' performance efficiency test is seen from CPU and memory usage, the average CPU usage of Scramsis is 2.71%, when compared to the CPU limit set by little eye, which is 15%, this usage is still very safe. The use of scramsis memory is 129 MB and does not experience force close. The usability test was carried out on class X TKJ SMK Muhammadiyah 1 Wates and got a score of 80.02% which was included in the quality category and the Cronbach alpha reliability value from the usability test questionnaire was 0.828 which means good.

Performance efficiency is important in mobile application development because mobile devices have limitations in terms of power and resources. Performance efficiency can be improved by applying architecture patterns. In this paper, we use the Model View ViewModel (MVVM) architecture. The application of the architecture is carried out to analyze how practical the application of the MVVM architecture pattern is in increasing performance efficiency in the mobile application. Performance efficiency is measured based on CPU usage, memory usage, and execution Time. The case study shows that the CPU usage and execution Time on MVVM are smaller than Base architecture pattern from the AR Ruler. This is due to the third-party library RxSwift in the MVVM architecture that increases the application's response so that CPU usage and execution time is better than Base architecture pattern. However, the existence of the third-party library RxSwift has a negative impact on memory usage, resulting in higher memory usage than the Base Architecture Pattern. The MVVM pattern is highly recommended for mobile application development to improve performance efficiency.

Line protection in HV/EHV/UHV lines is primarily by distance protection due to its selective, reliable and secure operation. With the wind farm penetration, the SIRs of lines are going to change drastically throughout the day. A case study has been performed in this paper to study the behavior of dynamic characteristics with respect to these changes. Analytically fault voltages and currents are calculated for the system. Distance relay implemented on hardware has been validated with these waveforms and inferences have been made to give recommendations for characteristics to be applied in practical systems for secure and reliable operation of distance relay.

In recent years the internet has become an important part of daily life for people around the world. 18 On the other hand, as the effectiveness of the internet increases, cybercrimes also increase. Over the last 15 years, cybersecurity has emerged as a way to accelerate the pace of change in cyberspace. Cybersecurity refers to the procedures a country or organization can use to protect its assets and information in cyberspace. Twenty years ago the term "cybersecurity" was little known to the public [1]. Cybersecurity affects not only people but also organizations or governments. In recent years, everything has been going well and it has used many technologies such as cybernetics, cloud computing, smartphones, and Internet of Things technology. Cyber attacks raise concerns about privacy, security, and financial security. Cybersecurity is a set of technologies, processes, and practices designed to prevent attacks, damage, and unauthorized access to networks, computers, operations, and machines. The main purpose of this article is to provide detailed information about types of cybersecurity, why cybersecurity is important, cybersecurity frameworks, cybersecurity tools, and problem points of cybersecurity [2].

The first ±800kV UHVDC transmission system is connected with the East China power grid around 2010 and its maximum transfer capacity will reach to 6.4GW. It brings new challenges and impacts on the network structure, operation security and reliability and system stability. In the paper, East China power gird configuration and load level for 2012 is adopted as base case for study. The power system simulation tools, such as PSS/E、BPA, etc, are used to study the system security and reliability under conditions of typical UHVDC operation modes. Technical issues associated with impacts of the ±800kV UHVDC connection on voltage stability, power transfer capability (ATC), system reliability are particularly studied. Some useful conclusions and suggestions for transmission system planning, operation security and stability considering the UHVDC connection are drawn in the end.

Country's economic growth and high policy target makes transition towards renewable source of generation. This transition in power system from synchronous machines to power electronic based renewable generation is challenge for system operation in maintaining demand-generation balance. This balance needs to be maintained for secure and reliable system operations. However, any imbalance due to inevitable contingency like loss of load or generation may cause events of frequency fall or rise. The inceptive stage of frequency deviation is controlled by system inertia. However, due to power electronics-based interface, renewable generation provides zero or less inertia support. Along with, large integration of renewable systems would displace large fleet of synchronous units and thus declines system inherent inertia. In this context, this paper gives a detailed trends of inertia emulation in low carbon power systems. Various sources of inertia along with its controlled strategies are discussed. This paper will provide a detailed review of inertia emulation which techniques to provide an insight for system operation for secure and reliable operations in future power system.

This paper focuses on the performance analysis of integrating Network Attached Storage (NAS) with a GSM Gateway to store voice call recordings in a Unified Communication (UC) environment. The current research builds upon the findings and methodologies established in a previous publication from the broader research project., which aims to eliminate the necessity of storing voice call recordings on the UC server. The existing research has primarily concentrated on the integration process, but there has been a lack of investigation into an unexamined performance gap. This study seeks to fill this gap by conducting a comprehensive analysis of the performance metrics of such an integrated system. The experimental setup includes the Open-Source Elastix UC platform, an AMD Sempron processor, a Zyxel NAS540 4-bay storage device and an EasyPhone GOIP GSM Gateway. Evaluation criteria include data privacy, server load, and network load. Performance metrics for data privacy include access control levels, data redundancy, remote access, hardware failure, RAID configuration, encryption, and dependency. Metrics for server load include CPU usage percentage, memory/RAM usage, and local storage. Network load metrics include throughput, latency, congestion, security, and isolation. The research demonstrates increased data privacy through the secure storage of recordings on NAS compared to the UC server. The server load is reduced and CPU and memory usage are lower by offloading storage tasks to the NAS. The increased network load does not impair overall network performance due to the use of a separate NAS gateway. This research is necessary as it offers methods to enhance UC environments, enabling secure, efficient, and scalable voice call recording storage. Key findings include improved access control and encryption measures for data privacy, reduced CPU and memory usage under server load, and maintained network performance despite increased load.

In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding & Identity Management, Data Integrity & Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.

Chapter 32 - Power Grid Resilience

In this paper, a window function based feature mining method for the operation and maintenance of big data in information system is proposed. The time clustering feature vector is combined with window function to reduce the dimension of operation and maintenance data of high-dimensional information system. The operation and maintenance data feature subset is segmented according to the similar feature level, and the redundant features of operation and maintenance data are removed to complete the information system operation and maintenance big data feature mining. The simulation results show that the proposed method has better clustering effect, fewer iterations and shorter mining time.

As critical communication infrastructure, optical networks have a vital role in safe and dependable transmission of massive amounts of data, supporting essential societal services. However, these networks are inherently vulnerable to a multitude of deliberate, man-made attacks targeting service disruption at the physical layer. Physical-layer attack techniques can range in their scope and effects, level of sophistication, locality, detectability, etc. An example of a relatively unsophisticated attack method is a deliberate fiber cut, typically targeting critical network elements (e.g., links with the highest betweenness) and resulting in straightforward transmission interruption [1]. More refined attack techniques rely on the insertion of harmful signal (e.g. in- and out-of-band jamming) [2], or on external tampering with the fiber to degrade the transmission quality (e.g., polarization scrambling via fiber squeezing) [3]. Diverse attack techniques cause different effects, which complicates their detectability. For example, some attacks add unfilterable noise, some reduce the power of the affected optical channels, while some inflict changes in the state of polarization too quick for the coherent receiver to compensate [3]. Therefore, monitoring only the spectrum [4], or individual signal parameters such as the power, optical signal-to-noise ratio (OSNR), or presence of errors may result in inaccurate diagnostics and root cause attribution. This obstacle in quick recovery of affected services is further pronounced for newly emerging attack techniques whose effects may deviate from the attack signatures previously known to the network management system [5].The complexity of the evolving physical-layer security landscape and the intricate interplay of different optical performance monitoring (OPM) parameters in the presence of diverse attack methods can greatly benefit from the application of machine learning techniques capable of deep data analysis. In this talk, we present how different data analytics and machine learning approaches can be applied to interpret the OPM data reported from the commercially available coherent receivers to identify anomalous operation and trigger security threat warnings. The analytical techniques are applied to experimental data obtained from an operator’s metropolitan testbed subjected to in- and out-of-band jamming, and external polarization scrambling attacks. We begin with an analysis of the optical signal degradation caused by the different attack methods. We then investigate the application of several supervised learning approaches that, once trained on the experimental data, can detect the presence of an attack and identify its type and intensity. The accuracy of several classifiers is scrutinized, along with the relevance of OPM parameters reported by the coherent receivers and the impact of missing features. To gain insight into the potential of the network to detect emerging, previously unseen attack techniques, we further analyse the performance of unsupervised learning techniques that detect the anomalies in signal parameters introduced by attacks. The presented findings help achieve timely and accurate detection of physical-layer attacks and serve as a prerequisite for fast and effective attack response and network recovery.

Introduction: The substantial energy usage in the cloud computing system is a significant drawback both for the cloud providers along with the cloud service users. In order to decrease the amount of energy used, it is necessary to implement virtualization techniques. Objectives: this paper employs a GA to optimize the migration of VMs and reduce energy usage in a cloud environment. Methods: The VM consolidation method effectively handles cloud resources, meeting the needs of both cloud consumers and suppliers. Moreover, it helps in enhancing the efficiency of servers while simultaneously decreasing the excessive energy usage in data canters. Nevertheless, the unnecessary activities of the VM consolidation technique result in inadequate VM selection and improper VM assignment, causing low performance, QoS, and violations of SLAs. Data center management struggles with energy consumption. VM migration and placement works well for this. Data centers require energy-saving solutions without affecting other parameters. Results: The performance of the proposed method has been evaluated utilizing factors like CPU usage, memory usage, network speed, power usage, and SLA breaches. Conclusions: The comparative analysis of the proposed approach with existing methods highlights its effectiveness and trustworthiness.

This article presents a hybrid method that statically evaluates several aspects regarding network-based model, text, as well as permissions, and monitors several activities dynamically; those activities include system call logs, memory usage, as well as the CPU usage. Thus, the designed machine learning-based model can easily distinguished, as well as identify Android ransomware among different and varied types of malware. According to the results, the machine learning-based approach proposed for Android ransomware classification and detection is able to classify unknown malware that exhibits the same static and dynamic behavior; it can prevent adversarial evasion attacks. Moreover, it was found that the data from permissions and system calls are more valuable to detect ransomware. These features and other considered static and dynamic features detect and classify Android Ransomware with high accuracy.

In recent cloud computing research, the allocation of virtual resources has become a major concern. The Virtual Machine (VM) dynamic deployment technique is one of the key virtual resource management techniques. Virtual resource management predicts the resources on the virtual machine and manages accordingly. The study of the Virtual Machine resource dynamic deployment focuses on the fine-grained resource adjustment strategy. So, there is a need for predicting the usage of resources for the virtual machine to avoid delays, overprovisioning and under provisioning. Time series forecasting models are used because predictions of cloud resources are dependent on time. In this paper, different time series models and ensemble techniques have been fitted to predict resource usage for successive hours. Different time series analysis techniques were implemented to study the dataset and to choose the appropriate model. Various types of resources such as CPU usage, disk read times, disk write times and memory usage are predicted using the above models.

Cyberattacks are rapidly evolving both in terms of techniques and frequency, from low-level attacks through to sophisticated Advanced Persistent Threats (APTs). There is a need to consider how testbed environments such as cyber ranges can be readily deployed to improve the examination of attack characteristics, as well as the assessment of defences. Whilst cyber ranges are not new, they can often be computationally expensive, require an extensive setup and configuration, or may not provide full support for areas such as logging or ongoing learning. In this paper, we propose GoibhniUWE, a container-based cyber range that provides a flexible platform for investigating the full lifecycle of a cyberattack. Adopting a modular approach, users can seamlessly switch out existing, containerised vulnerable services and deploying multiple different services at once, allowing for the creation of complex and realistic deployments. The range is fully instrumented with logging capabilities from a variety of sources including Intrusion Detection Systems (IDSs), service logging, and network traffic captures. To demonstrate the effectiveness of our approach, we deploy the GoibhniUWE range under multiple conditions to simulate various vulnerable environments, reporting on and comparing key metrics such as CPU and memory usage. We simulate complex attacks which span multiple services and networks, with logging at multiple levels, modelling an Advanced Persistent Threat (APT) and their associated Tactics, Techniques, and Procedures (TTPs). We find that even under continuous, active, and targeted deployment, GoibhniUWE averaged a CPU usage of less than 50%, in an environment using four single-core processors, and memory usage of less than 4.5 GB.