Control Services Research Articles

A modified lightweight authenticated key agreement protocol for Internet of Drones

Drones can be embedded in the system Internet of Drones can make a military observation of a region, a transport system embedded with sensors. Drones played a highly important role in different fields and brought great convenience to production and lifestyle. But we know all the data collected by sensors embedded in drones suffer from security challenges and privacy issues. There are a lot of authenticated key agreement protocols for the security of transmitted data through drones. We have analyzed a recent protocol, “A lightweight authentication and key agreement scheme for Internet of Drones” by Zhang et al. in the year 2020. We have found that this protocol is not secure against stolen smart card attacks, and the control server stores extra data. We have tried to address security issues of password guessing, anonymity, user/server impersonation, insider attack, and stolen smart card attacks, and man in the middle attack. We have proposed a modified lightweight authenticated key agreement protocol for Internet of Drones. We have proved its security in the random oracle model. We have done a performance analysis of the proposed protocol with relevant protocols that ensures its efficiency and security as well.

ENetRM: ElasticNet Regression Model based malicious cyber-attacks prediction in real-time server

Recently, cyber-attacks used unusual different malicious code or highly developed attack approaches like zero-day attacks which makes the process of detection more complex and difficult and thus traditional intrusion detection systems are unsuitable. For instance, 'botnet attacks are recent cyber-security threats. The endpoints are attacked either through malicious code or vulnerabilities. This affected system connects a communications channel to a command and control (C&C) server and issues comments to attack target servers.Malicious attacks are slow in carrying out denial of service (DoS) against webserver son protocols at application level like HTTP. Conventional signature-based intrusion detection systems are not appropriate to detect these attacks. To overcome this challenge, an artificial intelligence regression based malicious attack detection system is proposed for real-time attack detection of over encrypted traffic at application level. The process of malicious attack detection is done by the proposed ElasticNet Regression Model (ENetRM). The method has been validated in a real time application which proved its feasibility and ability to detect various slow DoS attacks over trafficin real-time. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Intrusion Detection System (NIDS), Novel Nested-Arc Hidden semi-Markov Model (NAHSMM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN).It is found that the proposed ENetRM method achieves 0.946 of precision. 0.9764 of recall, 0.9221 of F1-score, 97.04% of throughput and 53.48% of computation overhead.

An Efficient Authenticated Key Agreement Scheme Supporting Privacy-Preservation for Internet of Drones Communications.

In recent years, due to the rapid development of Internet of things (IoTs), various physical things (objects) in IoTs are smart enough to make their own decisions without the involvement of humans. The smart devices embedded in a drone can sense, collect, and transmit real-time data back to the controller from a designated environment via wireless communication technologies. The mobility, flexibility, reliability and energy efficiency of drones makes them more widely used in IoT environments such as commercial, military, entertainment applications, traffic surveillance and aerial photography. In a generalized IoD architecture, we have communications among the drones in a flying zone, among the drones and the control server, and also among the drones and authorized user. IoD still has many critical issues that need to be addressed, such as data access being carried out through a public channel and battery operated drones. To address these concerns in IoD communications, in this paper, an efficient authentication and secure communication scheme with privacy preservation is proposed and it only uses secure one-way hash function and bitwise XOR operations when control server, drone and user mutually authenticate each other. After the successful authentication, both IoD-based participants can agree on a common session key to secure the subsequent communication messages. The widely accepted ProVerif and BAN logic analysis have been used to assure that the proposed scheme is provably secure against existing well-known security attacks and ensures privacy. Finally, a comparative analysis is presented to demonstrate the proposed scheme preserves efficiency when compared to existing competitive schemes.

Design of intelligent operation inspection platform based on the multi-agent system for live line measurement of substation

At present, substations are developing vigorously in an intelligent direction. In this paper, an intelligent operation inspection platform based on a multi-agent system is designed. The platform’s design includes the design of its physical architecture and its operation architecture. The physical architecture of the platform includes a substation main control server, fixed live line measurement device, intelligent operation inspection software, intelligent data centralization device, intelligent interface conversion device, wireless communication channel, etc. In terms of platform operation architecture, an operation framework based on a multi-agent system is designed, including the “environment” layer, data sensing layer, data processing layer, and analysis and decision layer, and the whole platform is built based on the concept of edge computing. To solve the task allocation problem of edge computing, a Markov decision model is designed to minimize time consumption and maximize resource utilization, and the deep Q network (DQN) algorithm is used to solve the problem. The design aims at being effective, practical, safe, compatible, easy to upgrade, and extensible, and builds an intelligent operation inspection platform for live line measurement of substation equipment based on a multi-agent system, to improve the intelligent automation level of substation operation inspection.

Design and Implementation of In-Vehicle Alcohol Detection and Speed Control System

This study presented an effective method for reducing the rise in the number of road accidents instigated by drivers' excessive alcohol consumption on Nigerian roadways. Although speed restrictions and other preventative measures have been introduced, traffic accidents continue to occur daily. Over speeding, rash driving and drunk driving are all major contributors to motor vehicle accidents. To introduce this concept, this study has built an IOT-based in-vehicle alcohol detection and speed control system using the Arduino Nano microcontroller connected to the alcohol sensor, LCD display, and DC motor with the Bluetooth Low Energy (BLE) Application and Blynk Cloud Server for remote control and monitoring. This system uses the MQ-3 sensor to continuously monitor the blood alcohol concentration (BAC) and detect the presence of alcohol during breathing. This system can continuously monitor the alcohol content of the driver's breathing by installing a sensor in the steering wheel. This device was capable of detecting excessive speed and was programmed to alert with an SMS. After testing, it was determined that the suggested system met the standards for starting a car's engine. If the driver's breath alcohol level is discovered to be greater than the prescribed level (BAC) of 0.5 mg/mL, relevant individuals and authorities will be notified through GSM. The experimental results indicate that the alcohol sensor can respond rapidly when alcohol is detected and also operate for an extended period.

IoT based fault identification in solar photovoltaic systems using an extreme learning machine technique

Due to the shortage of fossil fuel usage, the solar Photovoltaic (PV) energy has increased grownup over the last decade. Most conventional applications of renewable energy are being phased out in order to reduce costs and save the environment. PV plants undergo numerous failures in faults detection and ultimate power developments. These consequences demonstrate in the environmental field and internal components. Even when internal standards are followed, the faults are unavoidable and undetectable. Due to this, the performance of manufacturing plants are not predictable. As a result, a proper fault detection mechanism is required for a PV system to detect faults and avoid energy losses. To address these issues, this research work proposed Internet of Things (IoT) sensor-based fault identification in a solar PV system. The PV panel status is monitored using pressure, light intensity, voltage, and current sensors. These sensor data’s are stored in the cloud for further analysis using a web-based control server. To classify the sensor data, models of Support Vector Machine (SVM), and Extreme Learning Machine (ELM) are utilized. The experimental results indicate that ELM achieves a classification accuracy of 96.32%. Which is higher than SVM and other optimization control techniques. The proposed model uses the IoT cloud to provide real-time monitoring and fault detection in plant environmental and electrical parameters.

A Study on Two-Phase Monitoring Server for Ransomware Evaluation and Detection in IoT Environment

Current trending- Internet of things (IoT) is internetworking of an assortment of hardware devices to offer a collection of applications and services. In the present-day world, ransomware cyber-attack has become one of the major attacks in IoT systems. Ransomware is a hazardous malware that targets the user’s computer inaccessible or inoperative, and then requesting the computer victim user to transfer a huge ransom to relapse the damage. At instance, the evolution rate outcomes illustrate that the level of attacks such as Locky and Cryptowall ransomware are conspicuously growing then other ransomware. Thus, these ransomware relations are the latent threat to IoT. To address the issue, this paper presents Two-phase ransomware prediction model based on the behavioral and communication study of Cryptowall ransomware for IoT networks. This proposed Two-phase model equipped with, Phase-1: observes the inward TCP/IP flowing traffic through a monitoring server to avert the ransomware attack The procedure of the monitoring server is to monitor the IoT's TCP/IP. The process of Monitoring TCP/IP is to extract TCP/IP header and routines command and control (C&C) server IP blacklisting to discover the ransomware attacks. In Phase-2: the proposed system will also analyze the application pattern for malicious behavior of the Web and URLs. Several societies have very affluent security tools in their milieu, but their events or logs are not monitored, which make affluent tools ineffective. The process of having efficient security based monitoring server is vital for detecting and controlling the ransomware attack.

Automatic Detection of DGA-Enabled Malware Using SDN and Traffic Behavioral Modeling

Enterprise networks are under enormous threats from sophisticated cyber-attacks. Various kinds of malware are installed by attackers on compromised hosts, acting as bots that typically use Domain Generation Algorithms (DGAs) to communicate with their Command and Control (C&C) servers. It is computationally expensive to inspect all network packets of every host connected to a large enterprise network in “real-time” at scale with hundreds of gigabits per second data rates. This paper combines Software Defined Networking (SDN) and machine learning to develop an accurate, cost-effective, and scalable system for detecting infected hosts communicating with external C&C servers, subsequent to the resolution of DGA query names. Our solution dynamically selects network flows for diagnosis by trained models in real-time, and relies more on the behavioral traffic profile, rather than packet content. Our first contribution highlights the prevalence and activity pattern of DGA-enabled malware across internal hosts. We draw insights into the behavioral profile of DGA-enabled malware flows when communicating with C&C servers. For our second contribution, we identify malware traffic attributes and train three specialized one-class classifier models using behavioral attributes of malware HTTP, HTTPS and UDP flows. We develop an SDN-based monitoring system to automatically mirror TCP/UDP flows pertinent to DGA queries for diagnosis by the trained models. Finally, we evaluate the efficacy of our approach by testing suspicious traffic flows (selectively recorded by SDN reactive rules), identifying infected hosts, and verifying our detection with an off-the-shelf Intrusion Detection System (IDS) software tool.

Understanding the Practices of Global Censorship through Accurate, End-to-End Measurements

It is challenging to conduct a large scale Internet censorship measurement, as it involves triggering censors through artificial requests and identifying abnormalities from corresponding responses. Due to the lack of ground truth on the expected responses from legitimate services, previous studies typically require a heavy, unscalable manual inspection to identify false positives while still leaving false negatives undetected. In this paper, we propose Disguiser, a novel framework that enables end-to-end measurement to accurately detect the censorship activities and reveal the censor deployment without manual efforts. The core of Disguiser is a control server that replies with a static payload to provide the ground truth of server responses. As such, we send requests from various types of vantage points across the world to our control server, and the censorship activities can be recognized if a vantage point receives a different response. In particular, we design and conduct a cache test to pre-exclude the vantage points that could be interfered by cache proxies along the network path. Then we perform application traceroute towards our control server to explore censors' behaviors and their deployment. With Disguiser, we conduct 58 million measurements from vantage points in 177 countries. We observe 292 thousand censorship activities that block DNS, HTTP, or HTTPS requests inside 122 countries, achieving a 106 false positive rate and zero false negative rate. Furthermore, Disguiser reveals the censor deployment in 13 countries.

HAGDetector: Heterogeneous DGA domain name detection model

The botnet relies on the Command and Control (C&C) channels to conduct its malicious activities remotely. The Domain Generation Algorithm (DGA) is often used by botnets to hide their Command and Control (C&C) server and evade take-down attempts, which allows the bot to generate a large number of domain names until it finds its C&C server. The lengths of domain names generated by DGAs are different. Our research finds that the length of the domain name has an impact on the performance of the DGA domain name detection model. In other words, the model is sensitive to the length of the domain name. In this case, attackers can evade detection simply by designing domain names of specific lengths. Moreover, the detection accuracy of DGA domain names still needs to be further improved. To solve these problems, three feature extraction methods adapted to the length of the domain name are proposed in this paper. For extra-short domain names, we use the attention-based method to extract features, which can make use of the character-level semantic feature. For moderate-length domain names, a two-dimensional structure, namely Right Shifted Tensor (RST), is constructed to make the domain name present apparent features similar to images. For the extra-long domain name, the effective classification of domain names can be achieved by manually crafted easy-to-calculate features. Then, different detection structures are designed based on these tree feature extraction methods to form a heterogeneous DGA detection model, namely HAGDetector. In addition, the public suffix is an important part of the domain name. We further analyze the public suffix to evaluate its impact on the detection of DGA domain names. Finally, the experiments are conducted to assess the validity of HAGDetector, as well as compare our approach with the current state-of-the-art and highlight the impact of the domain name length. The experimental results show that our method greatly improves the detection performance.

Siamese Neural Network and Machine Learning for DGA Classification

Domain Generation Algorithms (DGA) are systems used to create immediate multiple and varying domain names. Such “artificial” domains can be then used for siting command and control servers which in turn oversee recruiting/infecting devices, and finally turning them into new resources to be exploited. In this sense, identifying DGA domain names can be crucial, to avoid cyberattacks like Phishing, Spam sending, Bitcoin mining, and many other. Usually, domain names generated by DGAs, are comprised by illegible character strings, but new “intelligent” DGAs tend to generate names using combination of words in dictionaries making its detection a challenging task. For this reason, in this work, we propose to address this problem using a combination of Machine Learning algorithms for improving the classification of DGAs domains. In particular, we propose to combine Siamese Neural Networks and traditional supervised Machine Learning algorithms in order to expand the input domain into separable n-dimensional data points and then achieve the domain classification. The proposed approach can be separated into 3 phases. In a first phase, domain names are encoded, by a one-hot encoder and a variation of this, named probabilistic one-hot encoder, which are implemented separately. Then, in the second phase, Long Short-Term Memory and Convolutional Siamese embedders are tested and compared. In particular, the first one is combined with the one-hot, while the Convolution algorithm is applied with the probabilistic one-hot encoded data. In the final step, five Machine Learning algorithms are tested using the two ways embedded data. Both embedder approaches reach very high results in terms of F1-score and Accuracy (about 91%) depending on the implemented classifier. The promising results obtained by the application of the proposed method shows that it is possible to perform DGA domain classification uniquely over the domain names, without considering external information such as DNS packets features.

A Multi-Objective Meta-Heuristic Solution for Green Computing in Software-Defined Wireless Sensor Networks

Software-Defined Wireless Sensor Network (SDWSN) provides an architecture which enables WSN with self-configurable, scalable and programmable-control features. In SDWSN, network is decoupled into data plane and control plane, Normal Node (NN) at data plane is responsible for data collection and processing. Control Node (CN) in control plane routes the received data from the NNs and forwards it to its neighboring CNs and Control Server (CS) following the programmed instructions. The CNs are dynamically selected and placed wisely to activate SDWSN functions for achieving certain QoS parameters. The CNs require more energy and run with limited battery capacity that is not frequently rechargeable. Considering this scenario, this work proposes a green computing-aware multi-objective solution for the CNs placement problem by utilizing a meta-heuristic approach with energy, distance and relative-load imbalance performance metrics. In this work, a nature inspired Multi-Objective Harris Hawks optimization (MOHHO) meta-heuristic solution is proposed to offer an optimal multi-objective solution. Simulation results show the effectiveness of the proposed MOHHO solution in comparison to other state-of-the-art methods in terms of optimizing energy, load imbalance and delay performance metrics. This significantly enhances the lifetime of the SDWSN.

TriCTI: an actionable cyber threat intelligence discovery system via trigger-enhanced neural network

The cybersecurity report provides unstructured actionable cyber threat intelligence (CTI) with detailed threat attack procedures and indicators of compromise (IOCs), e.g., malware hash or URL (uniform resource locator) of command and control server. The actionable CTI, integrated into intrusion detection systems, can not only prioritize the most urgent threats based on the campaign stages of attack vectors (i.e., IOCs) but also take appropriate mitigation measures based on contextual information of the alerts. However, the dramatic growth in the number of cybersecurity reports makes it nearly impossible for security professionals to find an efficient way to use these massive amounts of threat intelligence. In this paper, we propose a trigger-enhanced actionable CTI discovery system (TriCTI) to portray a relationship between IOCs and campaign stages and generate actionable CTI from cybersecurity reports through natural language processing (NLP) technology. Specifically, we introduce the “campaign trigger” for an effective explanation of the campaign stages to improve the performance of the classification model. The campaign trigger phrases are the keywords in the sentence that imply the campaign stage. The trained final trigger vectors have similar space representations with the keywords in the unseen sentence and will help correct classification by increasing the weight of the keywords. We also meticulously devise a data augmentation specifically for cybersecurity training sets to cope with the challenge of the scarcity of annotation data sets. Compared with state-of-the-art text classification models, such as BERT, the trigger-enhanced classification model has better performance with accuracy (86.99%) and F1 score (87.02%). We run TriCTI on more than 29k cybersecurity reports, from which we automatically and efficiently collect 113,543 actionable CTI. In particular, we verify the actionability of discovered CTI by using large-scale field data from VirusTotal (VT). The results demonstrate that the threat intelligence provided by VT lacks a part of the threat context for IOCs, such as the Actions on Objectives campaign stage. As a comparison, our proposed method can completely identify the actionable CTI in all campaign stages. Accordingly, cyber threats can be identified and resisted at any campaign stage with the discovered actionable CTI.

A Privacy-Preserved Variational-Autoencoder for DGA Identification in the Education Industry and Distance Learning.

One of the most insidious methods of bypassing security mechanisms in a modern information system is the domain generation algorithms (DGAs), which are used to disguise the identity of malware by periodically switching the domain name assigned to a command and control (C&C) server. Combating advanced techniques, such as DGAs, is an ongoing challenge that security organizations often need to work with and possibly share private data to train better and more up-to-date machine learning models. This logic raises serious concerns about data integrity, trade-related issues, and strict privacy protocols that must be adhered to. To address the concerns regarding the privacy and security of private data, we propose in this work a privacy-preserved variational-autoencoder to DGA combined with case studies from the education industry and distance learning, specifically because the recent pandemic has brought an explosive increase to remote learning. This is a system that, using the secured multi-party computation (SMPC) methodology, can successfully apply machine learning techniques, specifically the Siamese variational-autoencoder algorithm, on encrypted data and metadata. The method proposed for the first time in the literature facilitates learning specialized extraction functions of useful intermediate representations in complex deep learning architectures, producing improved training stability, high generalization performance, and remarkable categorization accuracy.

DGA-based botnets detection using DNS traffic mining

Botnet is a network of infected workstations that are remotely managed by BotMaster via the command and control (C&C) server. Botnets pose a serious threat to network security since they are the source of a variety of malicious behaviors such as information theft, phishing, and Distributed Denial of Service (DDoS) assaults. Using a Domain Generation Algorithm (DGA) to produce a vast set of domain names is one of the most prevalent ways for hiding the identity of the C&C server.As a result, existing defensive methods have a limited chance of detecting and defeating such infrastructure. In this study, a system is suggested that employs machine learning techniques to categorize domain names intomalicious orlegitimate domain names. The suggested method is based on assessing the linguistic qualities of domain names requested from various hosts. Fifteen associated linguistic features were collected from the domain wordings to determine the degree of randomization, rarity, typing difficulty, and other related factors. The proposed system is tested with DNS requests gathered from various sources and seven distinct DGA botnet families. The findings reveal that the suggested technique can detect DGA domains with a 99.1% and a 0.6% false-positive rate.

(Retracted) Secure framework against cyber attacks on cyber-physical robotic systems

Robot-based platforms and processes have integrated the security and efficiency of data into a comprehensive range for domains, such as manufacturing, industrial, logistical, agricultural, healthcare, and internet services. Smart cyberattacks have been on the rise, specifically targeting corporate, industrial robotic systems. These attacks execute once the internet of things, internet, and organization integration is implemented with the industrial units. We implemented security criteria-based indices for cyber-physical systems (CPS) with industrial components and embedded sensors that process the information logs and processes. We proposed an attack tree-based secure framework that does not include every CPS device but takes into consideration the critical exploitable vulnerabilities to execute the attacks. We categorized each physical device and integrated sensors based on logs and information in a sensor indices device library. This research simulated real-time exploitation of vulnerabilities on CPS robotic systems using the proposed framework in form of a two-phased process. This validates the enhanced data security output of the integrated sensor and physical nodes with the intelligent monitor and controller system health monitor during real-time cyberattacks This research simulated common cyberattacks on cyber-physical controller servers based on cross-site scripting and telnet pivoting. The authors gathered known and unknown vulnerabilities and exploited them with a tree-based attack algorithm. The authors calculated the average time for cyberattackers with different skills when trying to compromise CPS devices and systems.

Machine learning-based algorithmically generated domain detection

Malware like botnets typically uses domain generation algorithms (DGAs) to dynamically produce a large number of random algorithmically generated domains (AGDs) and use a few of them to communicate with the command and control servers. AGD detection provides a lightweight yet effective solution to the threats imposed by DGA-based malware. For example, the linguistic distance between domain names was found as the promising metric to identify AGDs from benign domains. However, the distance metrics are not informatively enough used by the conventional approach. We propose to use machine learning algorithms on the distance metrics. Feature engineering techniques are proposed to boost detection performance. The results show that our proposal can outperform the existing algorithms, with a detection accuracy of over 99% for the tested DGAs. The permutation feature importance analysis is presented for explainability. The deployment locations of the AGD detectors are discussed.

Deep Reinforcement Learning-Based Traffic Light Scheduling Framework for SDN-Enabled Smart Transportation System

This work proposes a traffic-light scheduling framework using the deep reinforcement learning technique to balance the traffic flow and to prevent congestion in the dense regions of the city via a software-defined control interface. A software-defined control enabled architecture is proposed to monitor the traffic conditions and it generates the traffic light control signal (Red/Yellow/Green) accordingly. For an intelligent traffic light control signal, a Deep Reinforcement Learning (DRL) model is proposed which takes vehicular dynamics as inputs from the real-time traffic environment such as heterogeneous vehicles count, speed, traffic density etc. To determine the congestion, a threshold policy is proposed and deployed on control server which generates the congestion prevention signal. A DRL agent operates in the coordination of congestion prevention signal and generates an effective traffic light control signal. The proposed model is evaluated through a realistic simulation on Indian city OpenStreetMap by using a well-known open-source simulator (SUMO). The comparative results show that the proposed solution improves several performance metrics such as average waiting time, throughput, average queue length, and average speed in the interval of 28.34% – 66.62%, 24.76% – 66.60%, 30.89% – 69.80%, and 16.62% – 43.67% respectively over other states of the art approaches.

An evolutionary computing‐based energy‐efficient solution for IoT‐enabled software‐defined sensor network architecture

SummarySoftware‐defined networking (SDN) has emerged as an evolving technique in wireless sensor networks (WSNs). SDN enables WSNs with programmable control to manage network functions dynamically and efficiently. In Internet of Things (IoT) applications, smart sensors suffer from the low battery issue, generally deployed in harsh network environments where regular recharge is not feasible. Moreover, integrating SDN with IoT‐enabled sensor network puts forward several challenges, for example, control nodes' selection, load balancing, and energy cost optimization while aggregating the collected data, focusing on heterogeneous traffic data. Thus, an energy‐efficient data collection technique via definite sensing control in two‐level IoT‐enabled software‐defined heterogeneous WSN (2SD‐HWSN) is formulated as an optimization problem, with transmission distance from smart sensors, residual energy of sensors, and load based on node density. The proposed algorithm is divided into two: set‐up and transmission phases. In the set‐up phase, the control server (CS) elects the best‐suited control nodes (CNs) and sets up a schedule for coordinating data transmission. Further, normal nodes join appropriate CNs based on distance and residual energy. This way, CNs form clusters and route sensed data during the transmission phase. Therefore, an alternative nature‐inspired algorithm, that is, grey wolf optimization (GWO), is hybridized with particle swarm optimization using a low‐level co‐evolutionary technique to improve its overall performance. This hybrid variant of GWO, known as HGWO‐BC, offers balanced clustering (BC) via novel fitness function design. An exhaustive simulation study is performed in different scenarios considering homogeneous and heterogeneous sensors. Comparative results show that the HGWO‐BC outperforms state‐of‐the‐arts concerning network lifetime, instability period, residual energy, throughput, and computational efforts.

Smart Fire Detector utilizing IoT-based Zigbee Sensor

<pre>There are several differences between the two types of alarm systems, conventional systems and addressable systems. It is important to carefully determine the introduction of a fire alarm system according to the installation environment. Talking about the main difference relates to how the connected device communicates with the main control panel by sending a signal. Cost is another factor that can be a determinant of your chosen fire alarm system. In this paper, we proposed smart addressable fire detection system. In the proposed system, <span>IoT</span> was used and the network was constructed using <span>ZigBee</span> module. In the configured network, it consists of a local server and a control server. The local server controls the addressing sensor and sends the information obtained from the sensor to the control server. The control server receives data transmitted from the local server and enables quick fire action. In the actual implementation, the local server used the Lycra controller and <span>ZigBee</span> module. In addition, the control server used the Raspberry Pi and <span>ZigBee</span> modules and connected to the Ethernet so that the administrator could monitor or control the local server.</pre>