Received Data Packets Research Articles

Detecting selective forwarding attacks on cloud

—The Internet of Things (IoT) technology is now ubiquitous in both daily life and industrial production. Wireless Sensor Networks (WSNs) are a crucial part of the IoT infrastructure, providing the underlying network. However, WSNs are vulnerable to attacks due to challenging environmental conditions and their open communication nature. One particularly elusive threat is the selective forwarding attack, where malicious nodes intentionally drop parts or all of the received data packets, causing information loss and delays. This paper addresses the transition from the limited resources of central processing units (CPUs) to the expansive capabilities of cloud platforms within the IoT framework. By harnessing the robust computing power of cloud platforms, we investigate the effectiveness of the Time Convolution Network (TCN) time series prediction algorithm in detecting malicious nodes. Comparative results demonstrate that our proposed scheme achieves a Miss Detection Rate (MDR) of below 1% and a False Detection Rate (FDR) of less than 5%.

Encrypted Network Traffic Classification Using Deep and Parallel Network-In-Network Models

Network traffic classification analyzes received data packets to identify distinct application or traffic kinds. This research describes a neural network model that uses deep and parallel network-in-network (NIN) architectures to classify encrypted network data. In comparison to typical convolutional neural networks (CNN), NIN uses a micro network after each convolution layer to improve local modeling. Furthermore, NIN uses global average pooling instead of traditional fully connected layers before final classification, resulting in a considerable reduction in the number of model parameters. Our suggested solution uses deep NIN models with several MLP convolutional layers to map fixed-length packet vectors to application or traffic labels. Furthermore, a parallel decision method is created to build two sub-networks to process packet headers and packet bodies separately, taking into account that they may include different types of evidence for classification. Our investigations on the ''ISCX VPN-nonVPN'' encrypted traffic dataset demonstrate that NIN models can achieve a better balance between classification accuracy and model complexity than standard CNNs. The parallel decision technique can increase the accuracy of a single NIN model for classifying encrypted network data. Finally, the test set F1 scores of 0.983 and 0.985 are obtained for traffic characterisation and application identification, respectively

Prototipe Deteksi Letak Kebocoran Pipa dengan Optimalisasi Kinerja Penerimaan Paket LoRa menggunakan Pengkodean Parameter Fisik

The purpose of this research is to determine the effect of the physical coding of LoRa communications on monitoring water pipelines. Optimizing the performance of packet receivers in the LoRa communication system using coding on the physical parameters SF (spreading factor), BW (bandwidth), and CR (coding rate). The detection system consists of 3 sensor nodes, 3 intermediate nodes, and 1 receiver node. Data from these sensors is sent to a cloud database. The SX1278 LoRa communication module works using a 433 MHz frequency. During the transmission process on the LoRa communication system, optimization is carried out for receiving data packets using the parameter coding method of physical spread factors, bandwidth, and coding rate. As a result of the research, it is shown that the greater the value of the third parameter (SF, BW, and CR), such as improvement in packet reception performance, improvement in bit security, and increasing packet resistance to various disturbances in transmission, but the time required for sending data be longer. The optimal parameters for detecting pipe leak locations include SF 10, BW 500 KHz, and CR 4/8. The LoRa SX1278 scenario is optimal with a distance of 400 meters, where packet and byte reception are obtained 100%.

RANCANG BANGUN PENGELOLAAN BANDWIDTH INTERNET MENGGUNAKAN METODE QUEUE TREE DENGAN ROUTER MIKROTIK DI PT XYZ

Basically, the use of internet activities in companies is very dense, causing various problems, among the problems encountered related to internet connection is bandwidth usage that has not been optimally regulated, so that no matter how much bandwidth capacity is available, it will not meet the needs of internet bandwidth usage if it is not managed properly. . In order for internet user bandwidth allocation to be distributed evenly, bandwidth management is needed to overcome the existing bandwidth limitations. Bandwidth management aims to find a solution so that all user computer devices get internet bandwidth allocation and can find out how to manage good bandwidth so that user devices get a fair or even distribution of bandwidth. The methodology for implementing bandwidth management with the queue tree method is to observe a running network system, identify problems, collect data, then design a configuration system. After going through the design process then carrying out the configuration stage, and if the configuration is successfully applied then proceed to the testing phase using several user computers assisted by using the wireshark application to find out incoming or received data packets, the next stage is monitoring using monitoring applications such as Torch Winbox, speedtest or wireshark and the final stage is evaluation or management such as configuration backups and so on. This research and testing also takes into account Quality of Services (QoS) parameters such as throughput, delay, jitter, and packet loss to find out in more detail the calculation of packet traffic that has been captured by the wireshark application. So that with good design, proper configuration, careful monitoring, an even, optimal bandwidth usage and quality internet connection is produced, so that the implementation of the queue tree method of bandwidth management provides an effective solution in managing bandwidth allocation.

Sensor Scheduling for Remote State Estimation with Limited Communication Resources: A Time- and Event-Triggered Hybrid Approach.

This paper proposes a time- and event-triggered hybrid scheduling for remote state estimation with limited communication resources. A smart sensor observes a physical process and decides whether to send the local state estimate to a remote estimator via a wireless communication channel; the estimator computes the state estimate of the process according to the received data packets and the known scheduling mechanism. Based on the existing optimal time-triggered scheduling, we employ a stochastic event trigger to save precious communication chances and further improve the estimation performance. The minimum mean-squared error (MMSE) state estimate is derived since the Gaussian property is preserved. The estimation performance upper bound and communication rate are analyzed. The main results are illustrated by numerical examples.

C-FD MAC: Constellation based Full-Duplex MAC protocol

Simultaneous transmission and reception of data packets in Full-Duplex (FD) communication can theoretically double the system throughput. However, efficient self-interference mitigation and symbol detection at the receiver is required to realize the full potential of FD communication in practical scenarios. In this paper, we propose a constellation-based FD medium access control (MAC) protocol (C-FD) for wireless local area networks (WLAN). In the proposed C-FD MAC protocol, the node and Access Point (AP) can exchange packets simultaneously by transmitting the symbols and using different modulation schemes. The superimposed constellation diagram has been used to decode the symbols efficiently at both ends. The novel feature of the C-FD MAC protocol is that it does not require any complicated interference cancellation techniques. The analytical results for Symbol Error Rate (SER), Packet Error Rate (PER), and throughput have been derived for the symmetrical and asymmetrical FD link considering the additive white Gaussian noise (AWGN) with Rayleigh fading. The proposed C-FD MAC protocol achieves nearly twice the throughput compared to Half duplex (HD) communication without compromising the SER. The analytical results have been validated with extensive simulation.

IMPROVEMENT OF THE METHODOLOGY FOR EVALUATING THE CARRIER FREQUENCY OF THE SIGNAL DURING DATA TRANSMISSION IN TELECOMMUNICATION CONTROL AND DISTANCE LEARNING SYSTEMS

Effectiveness of the application of telecommunications systems is directly influenced by the effectiveness of the functioning of each component and device of its design. The article directly examines the issue of improving the carrier frequency estimation methodology for data transmission in telecommunication management and distance learning systems. The essence of the solution lies in proposals for improving the efficiency of frequency estimation at the input signal synchronization stage, which is presented on the example of a satellite telecommunication control system and distance learning. Namely, the analysis of modern satellite telecommunications systems was carried out and the features that affect the effectiveness of carrier frequency estimation and input signal synchronization were determined. The main inconsistencies are the low energy of the input signal receiving channel, significant signal frequency uncertainty, and the presence of "neighboring channels" receiving the input signal. It was determined that satellite communication systems during data transmission work in the mode with multi-station access in the variant of operation with frequency division of channels, which can receive data packets in the mode of multiple access or when providing a data reception channel on demand. At the same time, the operation of telecommunications systems can proceed both in continuous and in batch modes of receiving the input signal. It is considered that coherent processing of the input signal is carried out in demodulators of satellite telecommunications and interference-resistant coding is used. To solve the task of estimating the carrier frequency of the input signal in coherent demodulators of modern satellite telecommunications, considering the above-defined inconsistencies and features, the article proposes a methodical approach. The essence of this approach is to determine the potential boundary of variances of the obtained estimates of the carrier frequency and to develop algorithms for estimating the carrier frequency of the input signal separately for continuous and batch modes of input data transmission based on the fast Fourier transform. Keywords: Frequency, signal, uncertainty, carrier, estimation.

Mitigation of Black Hole and Selective Forwarding Attacks through Active Trust Mechanisms in Wireless Sensor Networks

In modern times, security remains a significant concern in wireless networks. Wireless sensor networks are widely employed in different practical situations. WSNs struggle with numerous internal and external threats, and detecting and defending against insider attacks is difficult. Insider attacks, in which intruders discard received data packets selectively, pose a significant risk to WSNs. The presence of black hole nodes within the network causes this issue. This research paper presents a secure and reliable route management methodology for WSNs that selects nodes and routing paths based on trust metrics. The proposed mechanism employs intelligent routing and node selection to defend against various types of assaults encountered during the routing process, including selective forwarding and black hole attacks. As a result, the paper identifies secure routes and establishes secure network routing. This scheme uses throughput, latency, and packet drop ratio as performance metrics. Experimentation demonstrates that the proposed system effectively increases the probability of secure network routing paths while extending the network lifecycle.

Certificateless multi-source elliptic curve ring signcryption for cloud computing

Network coding can encode and forward the received data packets to save the network resources and improve the bandwidth utilization in cloud computing environment. To resist the pollution attacks and improve the computational efficiency in network coding, we construct certificateless multi-source elliptic curve ring signcryption (CL-MSECRSC) scheme suitable for cloud computing, its security relies on the hardness of elliptic curve discrete logarithm (ECDL) and elliptic curve computation Diffie-Hellman (ECCDH) problems. CL-MSECRSC has the properties of anti-forgery, anti-pollution, confidentiality and unconditional anonymity; also, scalar multiplication operations greatly save its computation overhead.

Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods

Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.

A Water Pollution Monitoring System Based on the Internet of Things

The author aims to propose an implementation scheme for the overall design of a water environment online monitoring system based on the Internet of Things. The design of the system is mainly divided into three main parts: The sensor node acquisition part, the sink node transceiver part, and the upper computer monitoring and control part. The sensor node collection section consists of electrochemical probes (temperature, pH, conductivity, dissolved oxygen, turbidity), hardware conditioning circuits, and Zigbee sending nodes. The measured parameters are identified and converted into electrical signals through chemical probes, amplified and processed by conditioning circuits, and finally converted into digital signals and encoded into data packets through the sending node, which are uploaded to the aggregation node. The receiving and transmitting part of the aggregation node consists of a ZigBee receiving node and a serial/USB communication module. The receiving node is responsible for receiving data packets sent through the sending node, and achieving communication and transmission with the upper computer through a serial P/USB communication module. The upper computer monitoring and control part processes the received data packets in real-time through the upper computer interface software, and displays them in an orderly and purposeful manner through chart analysis.

Event-Triggered Finite-Time Formation Control for Networked Nonholonomic Mobile Robots Under Denial-of-Service Attacks

This paper studies the finite-time formation control problem for networked nonholonomic mobile robots (NMRs) under denial-of-service (DoS) attacks. The communication signals are transmitted among topology-connected NMRs by threatened networks, which might be disconnected or recovered at any time due to DoS attacks. Under DoS attacks, the received discontinuous date packets resulting from the directed graph might make the formation error escape to an unknown boundary. In addition, it is demonstrated that the frequency of DoS attacks directly affects the convergence time of controllers. A novel distributed observer is developed for each follower to narrow the unknown boundary, in which a projection operation is used to restrain the estimation of state with a known bounded set. An event-triggered mechanism is developed for each follower to identify the intermittent DoS attacks by verifying the time stamps of the received data packets. To reduce the effects of the attacker on the closed-loop system, a finite-time formation controller is proposed to guarantee formation convergence to the desired position in every duration of DoS attack-shut, preventing formation error accumulation. The Lyapunov tools are utilized to derive the conditions for guaranteeing the finite-time formation convergence under DoS attacks subject to a certain frequency. It is proved that the state estimation errors and formation errors can be regulated to the neighborhoods of origin in every duration of DoS attacks shut. The DoS attack and distributed DoS attack examples are provided to illustrate the correctness and performance of the proposed method for networked NMRs through numerical simulation comparisons.

Secure Distributed Adaptive Platooning Control of Automated Vehicles Over Vehicular Ad-Hoc Networks Under Denial-of-Service Attacks.

This article deals with the problem of secure distributed adaptive platooning control of automated vehicles over vehicular ad-hoc networks (VANETs) in the presence of intermittent denial-of-service (DoS) attacks. The platoon, which is wirelessly connected via directed vehicle-to-vehicle (V2V) communication, is composed of a group of following vehicles subject to unknown heterogeneous nonlinearities and external disturbance inputs, and a leading vehicle subject to unknown nonlinearity and external disturbance as well as an unknown control input. Under such a platoon setting, this article aims to accomplish secure distributed platoon formation tracking with the desired longitudinal spacing and the same velocities and accelerations guided by the leader regardless of the simultaneous presence of nonlinearities, uncertainties, and DoS attacks. First, a new logical data packet processor is developed on each vehicle to identify the intermittent DoS attacks via verifying the time-stamps of the received data packets. Then, a scalable distributed neural-network-based adaptive control design approach is proposed to achieve secure platooning control. It is proved that under the established design procedure, the vehicle state estimation errors and platoon tracking errors can be regulated to reside in small neighborhoods around zero. Finally, comparative simulation studies are provided to substantiate the effectiveness and merits of the proposed control design approach on maintaining the desired platooning performance and attack tolerance.

Using Software-Defined Networking for Data Traffic Control in Smart Cities with WiFi Coverage

The growth of smart cities is fueled by the vast rise in wireless smart gadgets and uninterrupted connectivity. WiFi is the dominant wireless technology, enabling Internet-of-Things (IoT) connectivity in smart cities due to its ubiquitous access points and low deployment cost. However, smart city applications offer a wide range of services with different quality-of-service (QoS) demands. This paper addresses packet delivery latency as one of the QoS metrics affecting many time-sensitive smart city services. Thus, the paper proposes employing software-defined networking (SDN) to control the traffic load of WiFi access points (APs), preserving its symmetry in a city-wide coverage of WiFi-connected IoT gateways or fog nodes. These gateways receive data packets from smart city/IoT devices via wireless links and forward them over a city-deployed WiFi network to their management entities or servers. Three SDN-based algorithms are devised to reduce the gateways’ packet-forwarding delay and keep a symmetric traffic load at the WiFi network APs. The algorithms are developed and tested using a real hardware setup constituting WiFi devices without additional requirements on the IoT gateways (WiFi clients) or the APs, such as support for a specific roaming protocol or bandwidth-consuming signaling such as sending probe packets. Extensive hardware experimentation shows that the SDN controller, via the proposed algorithms, can effectively reduce the packet forwarding latency of IoT gateways by carefully selecting the IoT gateway with the highest packet latency and seamlessly handing it over to the least-loaded covering AP.

Distributed Localization of Multiagent Systems With Imperfect Channels Based on Iterative Learning

The real-time localization problem for a class of multiagent systems with imperfect channels under directed graphs is investigated in this article. A successive distributed localization estimation algorithm based on iterative learning is proposed to solve the localization problem of the agent with bounded communication delays. The barycentric coordinates are used to represent the relative position between agents. An estimator is proposed to inspect whether the received data packet is the latest through the iteration stamp on the packet. Meanwhile, an intermittent distributed localization algorithm is devised to handle with the random data loss of agents in real-time localization problem. The relationship between the convergence rate and the probability of the data loss is given in the sense of mathematical expectations. Both numerical simulation and Qbot-2e robot experiment are provided to verify the effectiveness and the feasibility of the aforementioned methods.

Effect of NTT on Performance of AODV in a Grid Topology-Based Wireless Ad Hoc Network

Routing in wireless ad hoc networks that enable nodes, acting as routers also, to find the best path between source and destination nodes, taking into account cost, is a very challenging task. In the present work, an investigation of performance of AODV routing protocol in a grid topology based ad hoc network by varying the value of node traversal time (NTT) and taking into account absence and presence of Hello messages is reported. A set of metrics, including Average End-to-End Delay, Packet Delivery Ratio, Throughput, Routing Overhead, Route Error Overhead, Normalized Routing Load, Average Hop Count, and Total Number of Received Data Packets, has been used to assess the performance of AODV in the grid network. Performance of AODV routing protocol varies in the value of NTT. Throughput in grid topology, by and large, is observed to decrease with an increase in NTT. However, explicit relations between certain metrics with NTT as well as simulation time could not be traced due to intricacies involved in the combination of states of various links and flows in the grid topology. To have better insights, grid topologies of two, three, and four rows are planned to be investigated in the future.

Load Based Carrier Aggregation Algorithm for LTE Advanced

Long Term Evolution (LTE) supports transmission bandwidth upto 20MHz. Nowadays the multimedia rich applications connects billions of users worldwide. These services demands more number of Resource Blocks (RBs) to satisfy the Quality of Service (QoS) requirements and that can increase the data traffic in multifold. This crunch has led the Mobile Network Operators (MNOs) to increase the transmission bandwidth in order to fulfil the QoS requirements of the different services. In Release10, Third Generation Partnership Project (3GPP) provides a provision for MNOs to use Carrier Aggregation (CA) scheme in LTE-Advanced to aggregate upto five Components Carriers (CCs) of existing LTE carriers at the eNB to increase the transmission bandwidth upto 100MHz. Further, this scheme allows a User Equipment (UE) to transmit and receive data packets simultaneously on multiple CCs based on UE capability to meet the service requirements. However, the CC selection and efficient RBs utilization plays a vital role to optimize the system performance. Hence, in this paper using QualNet 8.2 network simulator the Load Based Carrier Aggregation Algorithm (LBCAA) is proposed for LTE-Advanced to aggregate the CCs of the existing LTE carriers at the eNB based on cell load in order to increase the transmission bandwidth to handle the data traffic growth. The simulations results illustrated that the proposed LBCAA for LTE-Advanced outperforms in the achieved average unicast received throughput, average unicast end-to-end delay and jitter as compared to LTE.

Age of Information Modeling and Optimization for Fast Information Dissemination in Vehicular Social Networks

Autonomous vehicles (AVs) with advanced communication, computing, and control capabilities will provide not only a convenient means of transportation but also an emerging platform for real-time social communications and networking. Thereby, it is crucial to enable timely exchange of information over the dynamic cyber-physical-social system enabled by the AVs. In this paper, we consider age of information (AoI)-centric real-time information dissemination over vehicular social networks (VSNs), where the AoI captures the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">freshness</i> of received data packets. We first propose a mathematical framework based on the mean-field theory (MFT) to analyze the network AoI (NAoI) of VSNs, namely AoI for the AV that lastly receives the information update in the network. The proposed analytical framework considers both the social features of vehicular networks, which are characterized using the scale-free network theory, and the underlying wireless communication process to evaluate the NAoI. Then, we further consider joint optimization of the information update rate at the source node and the transmit probabilities at the AVs for minimization of the average peak NAoI (PNAoI), i.e., the time average of peak values occurred in the evolution of the NAoI, which is solved via a novel parametric optimization scheme. Both analytical and simulation results show that compared with several baseline schemes, the proposed scheme can exploit the scale-free feature of the VSNs to significantly lower the average PNAoI by up to 96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

A Novel Energy-Aware Routing in Wireless Sensor Network Using Clustering Based on Combination of Multiobjective Genetic and Cuckoo Search Algorithm

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self-organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near-optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.

A Generic Secure Transmission Scheme Based on Random Linear Network Coding

Unlike general routing strategies, network coding (NC) can combine encoding functions with multi-path propagation over a network. This allows network capacity to be achieved to support complex security solutions. Moreover, NC has intrinsic security advantages against passive attacks over traditional routing techniques. However, due to the transmission of the global encoding kernels, the system is fragile to eavesdropping attacks with multiple probes. This paper proposes a generic unicast secure transmission scheme based on random linear network coding (RLNC). Specifically, the intended receiver generates a random matrix upon receiving the request from the source node, and then transmits each row vector of this matrix over a link reversely to the source node. Each intermediate node rearranges all received vectors to form a matrix by row, and then post-multiplies its local encoding kernel by this matrix to obtain a new matrix. Similarly, each row vector of the new matrix is reversely transmitted over a link to the source node. This procedure is performed until we have the source node, where the generalized inverse of the received matrix (or part of it) can be used as its local encoding kernel. Hence, the intended receiver can use the generated matrix (or the corresponding part) to decode the received data packets directly. We also analyze the security to demonstrate that the proposed scheme is at least as secure as other methods against wiretapping attacks. We also evaluate the performance of the proposed scheme to demonstrate its utility.