Local Area Network Environment Research Articles

Optimizing Internet-Wide Port Scanning for IoT Security and Network Resilience: A Reinforcement Learning-Based Approach in WLANs with IEEE 802.11ah

ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has spurred the need for robust security mechanisms within wireless local area network (WLAN) environments. Specifically, the IEEE 802.11ah (WiFi-HaLow) technology offers low-power, long-range communication capabilities ideally suited for IoT devices, but these devices face security threats due to constrained computational resources. This research addresses the challenge of optimizing Internet-wide port scanning to enhance IoT security while minimizing disruptions to network performance. In this paper, we propose a novel reinforcement learning-based approach to achieve this optimization. Our approach harnesses the power of the Proximal Policy Optimization (PPO) algorithm to guide decision-making for IoT security and network performance enhancement. The proposed solution entails training an agent to dynamically adjust the port scanning rate, ensuring the delicate balance between security improvement and network responsiveness. We establish a comprehensive system model, encompassing WiFi-HaLow infrastructure, IPv6-enabled IoT devices, and the integration of Internet-security (IPSec) protocols. Mathematical formulations encapsulate constraints such as resource limitations and security-performance trade-offs. Our experimental setup utilizes a carefully curated dataset and a simulation environment to rigorously evaluate the proposed solution’s effectiveness. The proposed approach demonstrates remarkable security enhancement through the prevention of unauthorized access attempts, data breaches, and improved intrusion detection. Moreover, network performance is optimized, with reductions in latency, improvements in throughput, and energy-efficient operation. By presenting a plethora of comparison scenarios, we validate the superiority of our solution against various benchmarks. This research contributes a comprehensive framework that not only advances IoT security within WLAN environments but also highlights the intricate relationship between security and network performance optimization.

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

Detection and Blocking of Replay, False Command, and False Access Injection Commands in SCADA Systems with Modbus Protocol

Industrial control systems (ICS) are being used for surveillance and controlling numerous industrial process plants in national critical infrastructures. Supervisory control and data acquisition (SCADA) system is a core component in ICS systems for continuous monitoring and controlling these process plants. Legacy SCADA systems are working in isolated networks and using proprietary communication protocols which made them less exposed to cyber threats. In recent times, these ICS systems have been connected to Internet and corporate networks for data sharing and remote monitoring. They are also using open protocols and operating systems. This leads to vulnerabilities of the system to cyberattacks. Cybersecurity threats are more prevalent than ever in ICS systems. These attacks may be external or internal. Modbus is a widely deployed communication protocol for SCADA communications. There is no security in design of Modbus protocol, and it is vulnerable to numerous cyberattacks. In this paper, we worked for False Command Injection attack, False Access Injection attack, and replay attacks on Modbus protocol. Initially, a real-time SCADA testbed was set up, and we envisaged the impact of these attacks on Modbus protocol data using the testbed. In this work, we used local area network (LAN) environment only for simulating the attacks. We assumed that the attacks penetrated the LAN network. We proposed and developed (a) a method to detect replay attacks by incorporating time stamp and sequence number in Modbus communications and (b) a frame filtering module which will block unauthorized attacks like False Command Injection and False Access Injection attacks to reach programmable logic controller (PLC). Numbers of attacks were simulated and the performance of the method was measured using attack block rate (ABR). It blocked 97% of malicious Modbus transactions or attacks to reach the PLC. It protects SCADA systems from attackers, which is a core component of industrial control systems. The solution enhanced the security of SCADA systems with Modbus protocol.

RETRACTED ARTICLE: Real-time voice system in local area network based on cloud computing background and service management of online education colleges

With the development of communication technology, many communication software have been developed, and these software have brought great convenience to our daily communication. We are currently studying the real-time voice system in the LAN. LAN communication can effectively eliminate external interference and ensure data security, so that more and more schools, companies, etc. choose LAN for internal communication. The multi-user real-time voice call system in the local area network environment has a wide range of applications, which greatly facilitates people’s work and life. In recent years, with the rapid development of the Internet, all walks of life are trying to find a method that can be combined with the mature “cloud computing” model. Generally, the cloud computing background is characterized by elastic expansion, self-service, and on-demand use. This paper proposes a university network service management plan based on the background of cloud computing, which is a research on university network service platforms with cloud computing experience. The plan considers the idea of creating a cloud platform for online education and university service management in universities, and the introduction of the cloud platform will have a positive impact on three aspects: teachers, scientists, and the media. This paper discusses the system design and deployment strategy in the “cloud” platform of network education in Colleges and universities, including the system network architecture, software architecture design, platform security strategy and the key technologies to be mastered when implementing the platform system.

An efficient parallel computing strategy for the processing of large GNSS network datasets

The Global Navigation Satellite System (GNSS) has been an indispensable tool for geodetic surveying and geodynamics research and has rapidly developed over the past few years with abundant ground networks, modern constellations and multiple signal frequencies. However, due to increasing numbers of stations and satellites, the data processing burden has increased significantly. In this contribution, an improved parallel computing method is proposed for processing large GNSS network datasets. First, a parallelization strategy is introduced for the traditional GNSS processing model by analyzing the practicability of parallel integrated processing for GNSS network data. In addition, to maximize the advantages of modern microprocessors and local area network environments, we present the multi-core parallel computing of traditional GNSS data processing methods; then, the product is released as a service-oriented architecture that can be found and invoked through multiple nodes in the Internet. Obviously, this method combines the advantages of multi-core parallelism and network parallelism. Experiments show that the efficiency of the proposed method can be further increased with the accumulation of GNSS data and additional nodes. For example, in a network with 2000 stations, the efficiency of the parallel scheme with four quad-core nodes is at least 8 times faster than that of the traditional serial scheme. All the results demonstrate that the proposed strategy is an efficient and promising approach for processing large GNSS network datasets.

Implementing Practical DNN-Based Object Detection Offloading Decision for Maximizing Detection Performance of Mobile Edge Devices

In the last decade, deep neural network (DNN)-based object detection technologies have received significant attention as a promising solution to implement a variety of image understanding and video analysis applications on mobile edge devices. However, the execution of computationally intensive DNN-based object detection workloads in mobile edge devices is insufficient in fulfilling the object detection requirements with high accuracy and low latency, owing to the limited computation capacity. In this paper, we implement and evaluate a DNN-based object detection offloading framework to improve the object detection performance of mobile edge devices by offloading computation-intensive workloads to a remote edge server. However, preliminary experimental results have shown that offloading all object detection workloads of mobile edge devices may lead to worse performance than executing the workloads locally. This degradation is obtained from the inefficient resource utilization in the edge computing architectures, both for the edge server and mobile edge devices. To resolve the aforementioned problem with degradation, we devise a device-aware DNN offloading decision algorithm that is aimed to maximize resource utilization in the edge computing architecture. The proposed algorithm decides whether or not to offload the object detection workloads of edge devices by considering their computing power and network bandwidth, and therefore maximizing their average object detection processing frames per second. Through various experiments conducted in a real-life wireless local area network (WLAN) environment, we verified the effectiveness of the proposed DNN-based object detection offloading framework.

Fingerprinting Indoor Positioning Method Based on Kernel Ridge Regression with Feature Reduction

An important goal of indoor positioning systems is to improve positioning accuracy as well as reduce power consumption. In this paper, we propose an indoor positioning method based on the received signal strength (RSS) fingerprint. The proposed method used a certain criterion to select fixed access points (FPs) in an offline phase instead of an online phase for location estimation. Principal component analysis (PCA) was applied to reduce the features of the RSS measurements but retain the most information possible for establishing the positioning model. Then, a kernel‐based ridge regression method was used to obtain the nonlinear relationship between the principal components of the RSS measures and the position of the target. We thoroughly investigated the performance of the proposed method in realistic wireless local area network (WLAN) and wireless sensor network (WSN) indoor environments and made comparisons with recently developed methods. The experimental results indicated that the proposed method was less dependent on the density of the reference points and had higher positioning accuracy than the commonly used positioning methods, and it adapts to different application environments.

Real-time Remote Monitoring and Security System for a Local Area Network Environment

Real-time Remote Monitoring and Security System for a Local Area Network Environment

Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning

Fingerprint-based indoor positioning uses pattern recognition algorithms (PRAs) to estimate the users’ locations in wireless local area network environments, where satellite-based positioning methods cannot work properly. Traditionally, the training phase of PRA is separately conducted for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates. However, the received signal strength from access points is a unique fingerprint for each measured point, not for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates separately. In this letter, we propose a method to jointly employ the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates during the training phase using a novel PRA-based Gaussian process regression (GPR), named 2D-GPR. Experimental results show that the proposed 2D-GPR improves the accuracy of positioning more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40cm$ </tex-math></inline-formula> in limited data samples and has a lower calculation cost compared with conventional GPR.

An Efficient Fingerprint Database Construction Approach Based on Matrix Completion for Indoor Localization

The Received Signal Strength (RSS) fingerprint-based indoor localization method in Wireless Local Area Network (WLAN) environment has been widely used due to its low hardware requirements and high positioning accuracy. However, the positioning system requires enormous effort to collect fingerprint at each reference point in positioning area, which is time-consuming and laborious. For the practicability of the method, it is essential to reduce the workload of fingerprint collection in the offline phase and construct a fingerprint database efficiently and accurately. Utilizing the high spatial correlation of RSS, in this paper, an efficient fingerprint database construction method is proposed based on low-rank matrix completion. In the meanwhile, different from the traditional matrix completion model, we propose an improved optimization model by converting to the weighted nuclear norm and introducing l 1 -norm and Frobenius norm to recover unknown data accurately and reduce the influence of outliers and Gaussian noise simultaneously. Furthermore, we combine the K-Nearest Neighbor (KNN) method to solve the problem that matrix completion cannot deal with all unknown rows or columns in the matrix. The experiment results indicate that the proposed algorithm can recover the fingerprint database completely through a few RSS information and has higher accuracy than other algorithms in various scenarios.

Managing congested local area network (LAN) environment with intelligent switch using semi-supervised learning (SSL) method

In artificial intelligence (AI), most of the application domain have the deficiency of not having their data labeled. Even some that are defined are not suitable for semi-supervised learning method. Although, unlabeled data are cheaply available in the public domain, it is not often used in artificial intelligence applications. Labeled data ensures that the AI support-application have clear algorithms for implementation. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning method addresses this problem and act as a middle-man between supervised and unsupervised learning. It is therefore an improved self-learning algorithm for AI implementations. This work addresses few techniques of enhanced semi-supervised learning (SSL) such as self-training and co-training. A new algorithm was developed that uses two approaches in a congested network environment that is being managed with an intelligent switch. The trained switch can control number of users on the network for effective data communication without interference and packet loss. This work could be beneficial to network managers, schools with large network infrastructures, to banks, to governments and to any other organization that deals or handles any topology of networks.

Remotely View User Activities and Impose Rules and Penalties in a Local Area Network Environment

Monitoring whether a student is opening other files other than the allowed file/s during an examination period is really a challenging task. The art of cheating in a written examination is also pertinent to a computer-based examination. Since the proctor is not at all time looking at the student’s computer activities, students still have the chance to open restricted applications. In this hurdle, developing a system that will send to the proctor’s machine the list and screenshots of the classified computer programs accessed in every student machine in real-time is beneficial. This system will detect the deceitful act through a warning message. If the act is not discontinued, the student machine will be penalized by either be locked, restarted, shutdown, sleep, or hibernate depending on the penalty set by the proctor. Furthermore, this system also records the student attendance during digital exams or as the students log-on to their machine.

Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network.

This paper investigated the throughput performance of a secondary user (SU) for a random primary user (PU) activity in a realistic experimental model. This paper proposed a sensing and frame duration of the SU to maximize the SU throughput under the collision probability constraint. The throughput of the SU and the probability of collisions depend on the pattern of PU activities. The pattern of PU activity was obtained and modelled from the experimental data that measure the wireless local area network (WLAN) environment. The WLAN signal has detected the transmission opportunity length (TOL) which was analyzed and clustered into large and small durations in the CTOL model. The performance of the SU is then analyzed and compared with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the probability of collisions in the network and the SU throughput were influenced by the value of the minimum contention window and the maximum back-off stage. The simulation results revealed that the higher contention window had worsened the SU throughput even though the channel has a higher number of TOLs.

Performance measurement using dpdk in software-defined local and wide area networks

Background/Objectives: For data center-based enterprises, the use of data traffic continues to increase. As a result, the increasingly efficient processing of increasing data traffic is becoming a major issue. Effective processing of traffic can be performed through the addition of physical network equipment, but this requires considerable costs.Methods/Statistical analysis: To solve this problem, researches on server virtualization and network virtualization technology are being actively conducted. However, while network virtualization is a cost-effective solution, open-source virtual switches used in network virtualization technologies have the performance penalty because they handle packets at the kernel of server's operating system. As a result, the Intel has developed the Data Plane Development Kit (DPDK) technology to address the performance degradation of the operating system.Findings: In this paper, we propose the DPDK performance test environment based on Open Virtual Switch (OVS) to verify the stability and performance of the DPDK-oriented virtualization technology, in association with a new KREONET-S project which aims tothe softwarization of the Korea Research Environment Open Network (KREONET) infrastructure.Improvements/Applications: We perform conducted experiments on Software-Defined Local Area Network (LAN) and Wide Area Network (WAN) environment using KREONET-S show the result.

A Heterogeneous Distributed Virtual Geographic Environment—Potential Application in Spatiotemporal Behavior Experiments

Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC)-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN) environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research.

Cloud Based Drive Forensic and DDoS Analysis on Seafile as Case Study

The rapid development of Internet due to increasing data rates through both broadband cable networks and 4G wireless mobile, make everyone easily connected to the internet. Storages as Services (StaaS) is more popular and many users want to store their data in one place so that whenever they need they can easily access anywhere, any place and anytime in the cloud. The use of the service makes it vulnerable to use by someone to commit a crime or can do Denial of Service (DoS) on cloud storage services. The criminals can use the cloud storage services to store, upload and download illegal file or document to the cloud storage. In this study, we try to implement a private cloud storage using Seafile on Raspberry Pi and perform simulations in Local Area Network and Wi-Fi environment to analyze forensically to discover or open a criminal act can be traced and proved forensically. Also, we can identify, collect and analyze the artifact of server and client, such as a registry of the desktop client, the file system, the log of seafile, the cache of the browser, and database forensic.

Pre-scan based fast handoff scheme for enterprise IEEE 802.11 networks

In IEEE 802.11 networks, many access points (APs) are required to cover a large area due to the limited coverage range of APs, and frequent handoffs may occur while a station (STA) is moving in an area covered by several APs. However, traditional handoff mechanisms employed at STAs introduce a few hundred milliseconds delay, which is far longer than what can be tolerated by some multimedia streams such as voice over Internet protocol (VoIP), it is a challenging issue for supporting seamless handoff service in IEEE 802.11 networks. In this paper, we propose a pre-scan based fast handoff scheme within an IEEE 802.11 enterprise wireless local area network (EWLAN) environment. The proposed scheme can help STA obtain the best alternative AP in advance after the pre-scan process, and when the handoff is actually triggered, STA can perform the authentication and reassociation process toward the alternative AP directly. Furthermore, we adopt Kalman filter to minimize the fluctuation of received signal strength (RSS), thus reducing the unnecessary pre-scan process and handoffs. We performed simulations to evaluate performance, and the simulation results show that the proposed scheme can effectively reduce the handoff delay.

Sounding Node Set and Sounding Interval Determination for IEEE 802.11ac MU-MIMO

In this paper, we propose a sounding control scheme for IEEE 802.11ac multiuser multiple-input multiple-output (MU-MIMO). The proposed scheme comprehensively considers the long-term characteristics of a network environment, including the downlink traffic loads and channel coherence times of wireless links, and jointly determines the sounding node set and sounding interval to maximize the long-term expected MU-MIMO throughput gain in consideration of sounding overhead. To this end, we analytically formulate an MU-MIMO throughput gain maximization problem, considering the network environment and sounding overhead. We conduct MIMO channel measurement in practical wireless local area network (WLAN) environments and evaluate the performance of the proposed scheme by employing the real channel data traces. Simulation results verify that the proposed scheme adaptively determines the sounding node set and sounding interval, according to the network environment, and outperforms the existing scheme, which considers the channel coherence times only.

Blind symbol timing synchronization scheme for real-time industrial wireless control network using high-speed preambleless OFDM systems over multipath fading channels

An accurate estimation of symbol timing is crucial for orthogonal frequency division multiplexing (OFDM) demodulation, since synchronization errors might destroy the orthogonality among all subcarriers and have greater impact on the system performance than single carrier systems. The symbol timing offset (STO) that leads to inter-symbol interference (ISI) and inter-channel interference (ICI) must be avoided as much as possible. In this paper, a novel symbol timing synchronization algorithm is proposed for OFDM systems on the basis of cyclic prefix (CP). A metric function and a positioning function have been designed by taking advantage of the correlation properties of OFDM signals. Computer simulations demonstrate that the schemes are superior because they achieve accurate timing offset estimation with smaller standard deviation (SD) and relatively lower computational complexity compared with the existing methods based on CP under high signal–noise ratio (SNR) assumption over frequency-selective channels, which can meet the real-time requirements in wireless local area network (WLAN) environments for factory automation (FA).

Efficient message delivery models for XML-based publish/subscribe systems

XML-based publish/subscribe (pub/sub) systems have been receiving a great deal of attention from the academic community and the industry. This research focuses on efficient pub/sub systems and considers the devising of XML-based pub/sub systems from the perspective of subscription/query and publication message delivery. Existing research mainly focuses on the efficiency of XML publication message filtering algorithms. Not much research, however, has considered using the system or the communication model in the context of XML publication messages delivery. This paper presents innovative XML delivery techniques, the cross-layer model and the peer model; both techniques make use of publisher and customer edge brokers for efficient XML subscription aggregation and publication message delivery. The primary contribution of the proposed models is the reduction of the number of XML publication message filtering and XPath query aggregation operations performed in the conventional filter-based XML multicast model, which has a high computational overhead. The main idea is to store user subscriptions at customer and publisher edge brokers which are either directly connected or close to the subscribers and the publisher, respectively. We have performed a number of experiments within a controlled local area network (LAN) environment for demonstration of the basic concepts underlying the techniques and in the Amazon cloud environment that emulates the wide area network (WAN). Both the cross-layer and the peer models can reduce the end-to-end (E2E) delay in message delivery. For example, the results obtained from experiments in a LAN demonstrate several-fold performance improvement in E2E delay for both the cross-layer and the peer models compared to the conventional filter-based XML multicast model, and the results using the cloud show an improvement as high as 64% in E2E delay for the peer model over the multicast model.