Packet Data Networks Research Articles

Res-DFNN: An NN-Based Device Fingerprint Extraction Method Using Network Packet Data

The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. First, existing DF extraction methods need private information from devices to compute DFs, which puts the privacy of devices at stake. Second, the manually designing features-based methods suffer from poor performance. To tackle these limitations, we propose a Linear Residual Neural Network-based DF extraction method, Res-DFNN, which utilizes network packet data in the pcap file to generate DF. The key block is designed according to symmetry, and it is verified by simulation that our method achieves better performance in both non-private and privacy-preserving scenarios.

Comparison of Gain Ratio and Chi-Square Feature Selection Methods in Improving SVM Performance on IDS

An intrusion detection system (IDS) is a security technology designed to identify and monitor suspicious activity in a computer network or system and detect potential attacks or security breaches. The importance of accuracy in IDS must be addressed, given that the response to any alert or activity generated by the system must be precise and measurable. However, achieving high accuracy in IDS requires a process that takes work. The complex network environment and the diversity of attacks led to significant challenges in developing IDS. The application of algorithms and optimization techniques needs to be considered to improve the accuracy of IDS. Support vector machine (SVM) is one data mining method with a high accuracy level in classifying network data packet patterns. A feature selection stage is needed for an optimal classification process, which can also be applied to SVM. Feature selection is an essential step in the data preprocessing phase; optimization of data input can improve the performance of the SVM algorithm, so this study compares the performance between feature selection algorithms, namely Information Gain Ratio and Chi-Square, and then classifies IDS data using the SVM algorithm. This outcome implies the importance of selecting the right features to develop an effective IDS.

Virtualization of a 4G Evolved Packet Core Network Using Network Function Virtualization (NFV) Technology with NS3 for Enterprise and Educational Purpose

The current networks of many operators dispose an increasing variety of purpose built, vertically integrated, and vendor-locked hardware equipment. This makes it difficult to easily scale their radio access and core networks as it requires yet another variety of equipment, leading to increased Time-to-Market and inefficient resource utilization. Furthermore, network equipment are expensive to procure and upgrade (increased CAPEX), and are difficult to adapt and program for new services (increased OPEX). These trends have recently spurred several efforts to redesign various components of mobile networks, notably the 4G Evolved Packet Core (EPC). With regards to this, the present work proposes a solution based on Network Function Virtualization (NFV), which can be deployed for educational or enterprises purpose like in Data Centers, or network nodes with a fine-grained QoS, while maintaining the scalability of the virtualized network function entities, and optimum resource utilization. The actual work develops a mobile network simulation module centered on the 4G Core network comprising its major components; MME, SGW, and PGW (vEPC), as well as the networking amongst these entities, core network related aspects such as its interaction with the Enhanced Radio Access Network (E-UTRAN) and the Packet Data Network Services (Internet). This solution gives to core network engineers and EPC network agents the possibility to design, analyze and test variable types of network scenarios. The solution can be also integrated in engineering schools and college for labs’ practical work or to any e-laboratory initiative to allow students in virtual environment to analyze signaling, modify the network configuration and better understand some theoretical concepts taught during the courses. The results obtained from the present work can be of great help during the final deployment phase of the network for full production for engineers of carriers’ network or improve the engineering understanding of core network in academic domain. In this light, the network can easily be scaled, adjust virtually for the time to commercialize a service reduced for carrier or enterprises’ solutions or for testing in e-laboratories.

LocCams

Unlawful wireless cameras are often hidden to secretly monitor private activities. However, existing methods to detect and localize these cameras are interactively complex or require expensive specialized hardware. In this paper, we present LocCams, an efficient and robust approach for hidden camera detection and localization using only a commodity device (e.g., a smartphone). By analyzing data packets in the wireless local area network, LocCams passively detects hidden cameras based on the packet transmission rate. Camera localization is achieved by identifying whether the physical channel between our detector and the hidden camera is a Line-of-Sight (LOS) propagation path based on the distribution of channel state information subcarriers, and utilizing a feature extraction approach based on a Convolutional Neural Network (CNN) model for reliable localization. Our extensive experiments, involving various subjects, cameras, distances, user positions, and room configurations, demonstrate LocCams' effectiveness. Additionally, to evaluate the performance of the method in real life, we use subjects, cameras, and rooms that do not appear in the training set to evaluate the transferability of the model. With an overall accuracy of 95.12% within 30 seconds of detection, LocCams provides robust detection and localization of hidden cameras.

Fragmentation of data packets in wireless sensor network with variable temperature and channel conditions

This study formalizes and solves the problem of minimizing the energy dissipated by a node in order to successfully transmit the initial data amount during one round of communication over a wireless channel affected by Rice fading under varying temperature conditions. In this work, the models for the temperature dependence of the parameters in an incoherent frequency shift key (FSK) demodulator are presented and investigated for the first time, taking into account both the frequency separation and the synchronization type of the clock generator. The studied orthogonality coefficients of FSK signals represent internal parameters in the proposed model of the incoherent Rice wireless channel. This model allows the analytical relationship between the bit error probability (BEP) and node heating temperature, signal fading depth in the channel (Rician K-factor), and signal-to-noise ratio (SNR) to be established.Since it is necessary to retransmit the entire data packet when an erroneous bit is detected in a communication system that uses an automatic repeat request, at high BEP values, it is recommended to fragment packets in order to improve the energy efficiency of the nodes. By using the developed models for calculating energy losses during the communication round, it was determined that optimal fragmentation options (OFO) for data packets can be identified within specific ranges of temperature and Rician K-factor, which includes the overhead costs for packeting and retransmission. As a result, OFO matrices of packets in the "temperature – K-factor" (t0–K) coordinate system were obtained for minimizing the energy loss of nodes at different SNR values. In our example of using TI CC2500 node transceivers for 90-day monitoring of corn vegetation, the t0K-Frag algorithm reduced node energy losses by a factor of K = 3.3 compared to the case of transmitting an unfragmented packet of initial length.

Efficient Data Gathering in Cloud Computing Using Clustering Schemes, Cloud Agent-Based Data Schemes and Efficient Path Planning Techniques

Cloud computing is a rapidly evolving field with a wide range of applications, including data storage, processing, and analysis. Clustering schemes, cloud agent-based data schemes, and efficient path planning techniques are all important aspects of cloud computing. Clustering schemes are used to group similar data items together, which can improve the efficiency of data processing and analysis. Cloud agent-based data schemes allow for the collection and management of data from a variety of sources, including cloud and edge devices. Efficient path planning techniques are used to optimize the routing of data packets in cloud networks. This research paper provides a comprehensive overview of clustering schemes, cloud agent-based data schemes, and efficient path planning techniques for cloud computing. The paper discusses the different types of clustering schemes and cloud agent-based data schemes, as well as their advantages and disadvantages. The paper also presents an overview of efficient path planning techniques for cloud computing. The paper concludes by discussing the challenges and future research directions in cloud computing. The authors believe that cloud computing is a rapidly evolving field with a bright future, and they encourage researchers to continue to develop new and innovative solutions for cloud computing.

Construction of a simulated dynamic model of data packet routing on a telecommunication network fragment

The object of the current research is the process of routing data packets in a telecommunications network. It was established that the state and parameters of data transmission channels and routers could have a negative impact and need to be taken into account in the process of modeling the routing of data packets in telecommunication networks. A model of simulated dynamic modeling of data packet routing in telecommunication networks has been devised and proposed. The suggested model makes it possible to establish quantitative values of the delay time of processing data packets during their routing along a separate segment of the telecommunications network, taking into account the state and parameters of the data transmission channel and routers. It has been established that the achievement of the minimum delay time of data packets during routing is achieved by choosing the path of their transmission under the condition of minimum transmission time by communication lines and the capabilities of routers in terms of their accumulation and service speed. At the same time, the reduction of packet transmission delay time relative to the average for the time network segment can reach from 21 to 38 percent. It is shown that the main factor affecting the value of the delay time is the speed of data processing along the selected path of packet transmission. The number of routing nodes in a separate data transmission path can affect the delay time only if the parameters of the routers are equal compared to others that are included in alternative routes. The model of simulated dynamic modeling of data packet routing reported in this work, unlike the existing ones, takes into account the state and parameters of data transmission channels and routers. It can be used in practical improvement of existing and development of new multi-service telecommunication communication networks

On Unique Ergodicity Of Coupled AIMD Flows

The AIMD algorithm, which underpins the Transmission Control Protocol (TCP) for transporting data packets in communication networks, is perhaps the most successful control algorithm ever deployed. Recently, its use has been extended beyond communication networks, and successful applications of the AIMD algorithm have been reported in transportation, energy, and mathematical biology. A very recent development in the use of AIMD is its application in solving large-scale optimization and distributed control problems without the need for inter-agent communication. In this context, an interesting problem arises when multiple AIMD networks are coupled in some sense (usually through a nonlinearity). The purpose of this note is to prove that such systems in certain settings inherit the ergodic properties of individual AIMD networks. This result has important consequences for the convergence of the aforementioned optimization algorithms. blackThe arguments in the paper also correct conceptual and technical errors in (1).

Digital Forensics for E-IoT Devices in Smart Cities

With the global expansion of urban infrastructure and development of 5G communication technology, advanced information and communications technology has been applied to power systems and the use of smart grids has increased. Smart grid systems collect energy data using Internet-of-Things (IoT) devices, such as data concentrator units (DCUs) and smart meters, to effectively manage energy. Services and functions for energy management are being incorporated into home IoT devices. In this paper, the IoT for energy management in smart cities and smart homes is referred to as the E-IoT. Systems that use the E-IoT can efficiently manage data, but they present many potential security threats, because the E-IoT devices in such homes and enterprises are networked for energy management. Therefore, in this study, to identify vulnerabilities in the E-IoT device systems, digital forensics is applied to the E-IoT device systems. E-IoT devices supplied to Korean power systems were used to build a digital forensic test bed similar to actual E-IoT environments. For digital forensics application, E-IoT data acquisition and analysis methodology was proposed. The proposed methodology consisted of three methods—network packet data analysis, hardware interface analysis, and mobile device paired with E-IoT—which were applied to a DCU, smart meter, smart plug, smart heat controller, smart microwave, and smart monitoring system. On analyzing the user and system data acquired, artifacts such as the device name and energy consumption were derived. User accounts and passwords and energy-usage logs were obtained, indicating the possibility of leakage of personal information and the vulnerabilities of E-IoT devices.

Adaptive global coordination of local routing policies for communication networks

We consider optimal routing of data packets in communication networks featuring time-variable flow rates and bandwidth limitations. Taking into account recent programmability developments in communication systems, we propose a two-level control scheme: routers with a programmable data plane implement local proportional control policies that forward the incoming data to different available output interfaces at line rate. The local controllers’ parameters are adapted periodically on a slower time scale by a logically centralized (software-defined) network controller running a global coordination algorithm that keeps the routing feasible and optimal with respect to a network metric, such as the average packet delay. A robust optimization approach is selected to handle traffic variations in-between global adaptation steps. The outcome is a non-convex Quadratically Constrained Quadratic Program (QCQP), for which we present an iterative solution approach that is computationally suitable for realistically-sized backbone communication networks. With simulation experiments, we demonstrate the advantages of adaptive, global routing coordination compared to fixed, globally or locally-determined policies, especially concerning packet loss.

Development and Simulation of a Fast Delay Cancellation Algorithm for Digital Multimedia Network Communication

The digital multimedia network data traffic is huge, and many end devices are applied to the digital multimedia network; therefore, the digital media network communication delay transmission must be analysed in order to improve the performance of the digital multimedia network communication transmission. Due to the presence of a large number of data packets in the communication network used, some random delays in data can occur during use. Conventional data transmission methods rely solely on extended data storage capacity to achieve this, making it difficult to achieve intelligent scheduling, resulting in delays and optimized performance degradation during communication data transmission. The aim of this paper is to enhance the communication quality of digital multimedia networks by developing an algorithmic model that rapidly mitigates communication delays. The proposed approach employs a delay cancellation algorithm in a network predictive control system. Simulation techniques are utilized to simulate and compensate for delays in multimedia communication networks. The efficacy of the network control strategy is demonstrated through simulation tests on a large-scale multimedia network communication system. The research findings indicate that the network control prediction model and elimination algorithm can substantially reduce the packet loss rate and quickly eliminate communication delays in digital multimedia networks.

Malicious Data Classification in Packet Data Network Through Hybrid Meta Deep Learning

Malicious Data Classification in Packet Data Network Through Hybrid Meta Deep Learning

Unsupervised Deep Learning Approach for In-Vehicle Intrusion Detection System

The controller area network (CAN) is a standard communication protocol used for sending messages between electronic control unit of a modern automotive system. CAN protocol does not have any in-built security mechanisms and, hence, various attacks can affect the vehicle and cause life threats to the passengers. This article presents an unsupervised deep learning architecture for detecting intrusions on a CAN bus. The CAN intrusion detection system (IDS) architecture has an autoencoder that helps to learn the optimal features from CAN packets to differentiate between the normal and attacks. The optimal features are passed as input to the Gaussian mixture model, which helps us to cluster the CAN network packet data samples into normal and attacks. A detailed analysis of the proposed architecture is done on the CAN IDS dataset. To develop a robust CAN IDS system and achieve generalization, the proposed method is evaluated on the other two computer network intrusion datasets and a wireless sensor network dataset. In all the experiments, the proposed method has performed better than the existing unsupervised method and mainly showed a performance gain of 6.4% on the CAN IDS dataset. This indicates that the proposed method is robust and generalizable across detecting various attacks in a CAN bus and most importantly, the method can be used in real time to effectively monitor the CAN network traffic to proactively alert possible attacks.

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model.

At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system's performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity υ of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (−2, 2). Its angular velocity ω initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle θ coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.

Naive Bayes Algorithm Mining Mobile Phone Trojan Crime Clues

After the mobile phone virus infects the mobile phone, it can transmit the real-time information of the user to the designated place set by the virus through the built-in recorder and camera on the mobile phone, thereby causing information leakage. With the rapid development of the Internet, the penetration rate of mobile terminals is also increasing day by day. As an emerging mobile terminal, smart phones have now fully occupied the market. With this trend, the importance of mobile phone information security is also increasing day by day. How to prevent mobile phone virus has gradually become an important issue. Trojan horse crime cases have different manifestations and behavioral characteristics from traditional cases. They have the characteristics of low crime cost, high income, high concealment, novel criminal methods, and great difficulty in detection, which brings greater difficulties to the public security organs in their investigation and detection. And the current research on mobile phone virus behavior is still in the preliminary stage, and some existing detection models can only target random networks. Trojan horses, viruses, and malicious software for smartphones have sprung up like mushrooms after rain, seriously infringing on the data security of mobile communication terminals, such as mobile phones and causing incalculable losses to users. This paper proposes a naive Bayesian algorithm to mine the clues of the criminal cases of mobile phone Trojans. It helps detect and discover new viruses at the beginning of an attack, allowing them to be more effectively defended and contained. And based on the feature set data extracted from the network data packets, it conducts an in-depth analysis of the current business behaviors of mobile phone Trojans, such as propagation and implantation, remote control, leakage of user privacy information, and malicious ordering, and extracts its behavior characteristics. Thus, unknown mobile Trojan horses that are taking place can be detected. The experimental results of the naive Bayesian classification algorithm proposed in this paper show that the algorithm improves the accuracy of mobile phone Trojan virus mining by 28%, which plays a significant role.

AdaBoost Algorithm in Trustworthy Network for Anomaly Intrusion Detection

Abstract Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. AdaBoost algorithm, as a typical boosting algorithm, transforms weak learners or predictors to strong predictors in order to solve problems of classification. With remarkable usability and effectiveness, AdaBoost algorithm has been widely used in many fields, such as face recognition, speech enhancement, natural language processing, and network intrusion detection. In the large-scale enterprise network environment, more and more companies have begun to build trustworthy networks to effectively defend against hacker attacks. However, since trustworthy networks use trusted flags to verify the legitimacy of network requests, it cannot effectively identify abnormal behaviors in network data packets. This paper applies Adaboost algorithm in trustworthy network for anomaly intrusion detection to improve the defense capability against network attacks. This method uses a simple decision tree as the base weak learner, and uses AdaBoost algorithm to combine multiple weak learners into a strong learner by re-weighting the samples. This paper uses the real data of trustworthy network for experimental verification. The experimental results show that the average precision of network anomaly detection method based on AdaBoost algorithm is more than 0.999, indicating that it has a significant detection effect on abnormal network attacks and normal network access. Therefore, the proposed method can effectively improve the security of trustworthy networks.

100 Gbps Dynamic Extensible Protocol Parser Based on an FPGA

In order to facilitate the transition between networks and the integration of heterogeneous networks, the underlying link design of the current mainstream Information-Centric Networking (ICN) still considers the characteristics of the general network and extends the customized ICN protocol on this basis. This requires that the network transmission equipment can not only distinguish general network packets but also support the identification of ICN-specific protocols. However, traditional network protocol parsers are designed for specific network application scenarios, and it is difficult to flexibly expand new protocol parsing rules for different ICN network architectures. For this reason, we propose a general dynamic extensible protocol parser deployed on FPGA, which supports the real-time update of network protocol parsing rules by configuring extended protocol descriptors. At the same time, the multi-queue protocol management mechanism is adopted to realize the grouping management and rapid parsing of the extended protocol. The results demonstrate that the method can effectively support the protocol parsing of 100 Gbps high-speed network data packets and can dynamically update the protocol parsing rules under ultra-low latency. Compared with the current commercial programmable network equipment, this solution improves the protocol update efficiency by several orders of magnitude and better supports the online updating of network equipment.

Aeronautical Communications

Aeronautical communications is a discipline covering the highly specialized and mission critical communications such as aircraft-to-ground, aircraft-to-aircraft, and aircraft-to-satellite. Recent changes in this field include: advanced satellite communications installations in aircraft, and very high frequency digital radio, and data link applications reporting aircraft position in transoceanic operations, as well as airline, air traffic, and passenger services. Because of such rapid changes in this field, it is critical to aviation safety that we have an expertise in satellite and data link communications, data link and aircraft surveillance applications, aeronautical communications network, HF and VHF data link, circuit mode and packet mode communications.

Relay Selection for Capacity Increase in Underwater Acoustic Sensor Network.

In long distance sensor nodes, propagation delay is the most crucial factor for the successful transmission of data packets in underwater acoustic sensors networks (UWAs). Therefore, to cope with the problem of propagation delay, we propose examining and selecting the best relay node (EBRN) technique based on checking the eligibility and compatibility of RN and selecting the best RN for UWAs. In the EBRN technique, the source node (S) creates a list of the best RNs, based on the minimum propagation delay to the midpoint of a direct link between S and the destination node (D). After that, the S attaches the list of selected RNs and transmit to the D along with data packets. Finally, from the list of selected RNs, the process of retransmission is performed. To avoid collision among control packets, we use a backoff timer that is calculated from the received signal strength indicator (RSSI), propagation delay and transmission time, whereas the collision among data packets is avoided by involving single RN in a particular time. The performance of the proposed EBRN technique is analyzed and evaluated based on throughput, packet loss rate (LR), packet delivery ratio (PDR), energy efficiency, and latency. The simulation results validate the effectiveness of the proposed EBRN technique. Compared with the existing schemes such as underwater cooperative medium access control (UCMAC) and shortest path first (SPF), the proposed EBRN technique performs remarkably well by increasing the throughput, PDR, and energy efficiency while decreasing the latency and LR in UWAs.