A Routing Algorithm for Distributed Optimal Double Loop Computer Networks

A routing algorithm for distributed optimal double loop computer networks is proposed and analyzed. In this paper, the routing algorithm rule is described, and the procedures realizing the algorithm are given. The proposed algorithm is shown to be optimal and robust for optimal double loop. In the absence of failures,the algorithm can send a packet along the shortest path to destination; when there are failures,the packet can bypasss failed nodes and links.

Design and Implementation of Online Experimental Platform for Computer Networks Course

Practice training is very important for students learning Computer networks.But building a real laboratory is constrained and expensive.In this paper,we present an online experimental platform for computer networks course based on Dynamips simulator.Instructors and students can access the platform by IE Browser to manage and take router experiments.On the basis of deployment and testing,the platform is effective and flexible.

Multi-agent cooperative intrusion response in mobile adhoc networks

The nature of adhoc networks makes them vulnerable to security attacks. Many security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermea- sures are only to protect the networks, and there is no automated network-wide counteraction against detected intrusions, the architecture of cooperation intrusion response based multi-agent is propose. The architecture is composed of mobile agents. Monitor agent resides on every node and monitors its neighbor nodes. Decision agent collects information from monitor nodes and detects an intrusion by security policies. When an intruder is found in the architecture, the block agents will get to the neighbor nodes of the intruder and form the mobile firewall to isolate the intruder. In the end, we evaluate it by simulation.

Flooding attack and defence in Ad hoc networks

Mobile ad hoc networks are particularly vulnerable to denial of service （DOS） attacks launched through compromised nodes or intruders. In this paper, we present a new DOS attack and its defense in ad hoc networks. The new DOS attack, called AA hoc Flooding Attack（AHFA）, is that intruder broadcasts mass Route Request packets to exhaust the communication bandwidth and node resource so that the valid communication can not be kept. After analyzed AM hoc Flooding Attack, we develop Flooding Attack Prevention （FAP）, a genetic defense against the AM hoc Flooding Attack. When the intruder broadcasts exceeding packets of Route Request, the immediate neighbors of the intruder record the rate of Route Request. Once the threshold is exceeded, nodes deny any future request packets from the intruder. The results of our implementation show FAP can prevent the AM hoe Flooding attack efficiently.

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and cybercrime.Such malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,etc.Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of money.To prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)algorithm.This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in Computer Networks.The presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the network.To accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality problems.Besides,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is applied.The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results.The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct measures.The comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

Wireless Sensor Security Issues on Data Link Layer:A Survey

A computer network can be defined as many computing devices connected via a communication medium like the internet.Computer network development has proposed how humans and devices communicate today.These networks have improved,facilitated,and made conventional forms of communication easier.However,it has also led to uptick in-network threats and assaults.In 2022,the global market for information technology is expected to reach$170.4 billion.However,in contrast,95%of cyber security threats globally are caused by human action.These networks may be utilized in several control systems,such as home-automation,chemical and physical assault detection,intrusion detection,and environmental monitoring.The proposed literature review presents a wide range of information on Wireless Social Networks(WSNs)and Internet of Things(IoT)frameworks.The aim is first to be aware of the existing issues(issues with traditional methods)and network attacks on WSN and IoT systems and how to defend them.The second is to review the novel work in the domain and find its limitations.The goal is to identify the area’s primary gray field or current research divide to enable others to address the range.Finally,we concluded that configuration.Message Rapid Spanning Tree Protocol(RSTP)messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol(BPDU)forms.The research divides our future research into solutions and newly developed techniques that can assist in completing the lacking component.In this research,we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview.

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to data.But the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so on.Presently,malware attacks and software piracy pose serious risks in compromising the security of IoT.They can steal confidential data which results infinancial and reputational losses.The advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud environment.This article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud Net-works.The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment.The proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification problem.The EAG-TODL-CTD model pre-processes the input data to transform to a compatible for-mat.For threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class labels.Finally,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our work.The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign.The experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

A Recovery Algorithm for Self-Stabilizing Communication Protocols

This paper presents a recovery algorithm for self-stabilizing communication protocols. It first describes some concepts and a formal description method for the algorithm. Then it proposes the algorithm procedures, proves its correctness and analyses its complexity. Finally, it also verifies the availability and efficiency of the algorithm by illustrating an example protocol with multi-processes.

Adaptive Predictive Functional Control for Networked Control Systems with Random Delays

Nowadays, more and more field devices are connected to the central controller through a serial communication network such as fieldbus or industrial Ethernet. Some of these serial communication networks like controller area network （CAN） or industrial Ethernet will introduce random transfer delays into the networked control systems （NCS）, which causes control performance degradation and even system instability. To address this problem, the adaptive predictive functional control algorithm is derived by applying the concept of predictive functional control to a discrete state space model with variable delay. The method of estimating the networkinduced delay is also proposed to facilitate the control algorithm implementing. Then, an NCS simulation research based on TrueTime simulator is carried out to validate the proposed control algorithm. The numerical simulations show that the proposed adaptive predictive functional control algorithm is effective for NCS with random delays.

Network Fault Diagnosis Using DSM

Difference similitude matrix (DSM) is effective in reducing information system with its higher reduction rate and higher validity. We use DSM method to analyze the fault data of computer networks and obtain the fault diagnosis rules. Through discretizing the relative value of fault data, we get the information system of the fault data. DSM method reduces the information system and gets the diagnosis rules. The simulation with the actual scenario shows that the fault diagnosis based on DSM can obtain few and effective rules. Key words computer networks - data reduction - fault management - difference-similitude matrix CLC number TP 393 Foundation item: Supported by the National Natural Science Foundation of China (90204008)Biography: Jiang Hao (1976-), male, Ph. D candidate, research direction: computer network, data mine.

An Optimal Framework for SDN Based on Deep Neural Network

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network programming.However,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in SDN.To address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is proposed.This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module.The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on DNN.DDoS assaults were found when suspected irregular traffic was validated.Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy rate.The false alarm rate(FAR)is much lower than that of the information entropy-based detection method.Simultaneously,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

An Optimal Scheme for WSN Based on Compressed Sensing

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks,bio-medical engineering,agriculture,industry and many more.It has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load.Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head node.Then,each ordinary node joins the cluster that is nearest to it.The greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head nodes.The simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of ten.When compared to clustering and SPT without CS,it is reduced by 75%and 65%,respectively.When compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,respectively.Clustering and SPT without CS are compared in terms of node data transfer volume standard deviation.SPT with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,respectively.When compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

The Algorithms for Achieving Global States and Self-Stabilization for Communication Protocols

This paper discusses the algorithms for achieving global states and self-stabilizationfor communication protocols. It first describes a primary algorithm including its suitability forachieving global states and limitation of self-stabilization for communication protocols, and thenpresents an improved algorithm that can be suitable to achieve global states and can be also usedto self-stabilizing communication protocols. Filially, it gives the proof of correctness and analysis ofcomplexity of the improved algorithm, and verifies its availability and efficiency by illustrating anexample protocol.

ON THE ANALYSIS OF PARAMETER γ IN FAST TCP

The limits of parameter γ in FAST TCP are studied in this paper. A continuous time fluid flow model of the link buffer is considered to create a linear control system related to FAST TCP. Linearing the fluid flow model and window control model, the Laplace transform version of congestion control system are presented. It results in a negative feedback system with open loop transfer function. With the analysis of Nyquist curve of the system, a sufficient condition on asymptotical stability of FAST TCP congestion window related to the parameter γ is obtained. Packet level ns-2 simulations are used to verify the theoretical claims.

The Network Architecture and Protocols for Distributed Virtual Environment

In this paper, the properties of distributed virtual environment (DVE) and the requirements on computer networks is briefly reviewed. A multicast protocol, called sender initiated grouping multicast protocol for DVE (SIGMP), is proposed. This new multicast protocol is based on a novel concept, multicast group (MG), which divides all participants in a DVE system into groups, among which there is a multicast group trustee (MGT) node to manage the group. The protocol provides unreliable/reliable, totally ordered and multiple to multiple multicast transmission service for DVE systems without sacrificing the communication efficiency heavily. At the same time, reliable unicast and one to multiple multicast transmission services are also supported. The performance analysis of the new protocols is also presented. Based on SIGMP, a simple demonstration of DVE system is designed and implemented. This demo system is running on several SGI workstations connected by a FDDI and Ethernet network.

Statistical Analysis of Network Based Issues and Their Impact on Social Computing Practices in Pakistan

Social computing and online groups have accompanied in a new age of the network, where information, networking and communication technologies are enabling systematized human efforts in primarily innovative ways. The social network communities working on various social network domains face different hurdles, including various new research studies and challenges in social computing. The researcher should try to expand the scope and establish new ideas and methods even from other disciplines to address the various challenges. This idea has diverse academic association, social links and technical characteristics. Thus it offers an ultimate opportunity for researchers to find out the issues in social computing and provide innovative solutions for conveying the information between social online groups on network computing. In this research paper we investigate the different issues in social media like users’ privacy and security, network reliabilities, and desire data availability on these social media, users’ awareness about the social networks and problems faced by academic domains. A huge number of users operated the social networks for retrieving and disseminating their real time and offline information to various places. The information may be transmitted on local networks or may be on global networks. The main concerns of users on social media are secure and fast communication channels. Facebook and YouTube both claimed for efficient security mechanism and fast communication channels for multimedia data. In this research a survey has been conducted in the most populated cities where a large number of Facebook and YouTube users have been found. During the survey several regular users indicate the certain potential issues continuously occurred on these social web sites interfaces, for example unwanted advertisement, fake IDS, uncensored videos and unknown friend request which cause the poor speed of channel communication, poor uploading and downloading data speed, channel interferences, security of data, privacy of users, integrity and reliability of user communication on these social sites. The major issues faced by active users of Facebook and YouTube have been highlighted in this research.

Quantum efficiency calibration of opto-electronic detector by means of correlated photons method

A new calibration method of detectors can be realized by using correlated photons generated in spontaneous parametric down-conversion （SPDC） effect of nonlinear crystal. An absolute calibration system of detector quantum efficiency is performed. And its principle and experimental setup are introduced. A continuouswave （CW） ultraviolet （351 nm）, diode-pumped, frequency-doubled, and solid-state laser is used to pump BBO crystal. The quantum efficiencies of the photomultiplier at 633, 702, and 789 nm are measured respectively. The coincidence peaks are observed using coincidence circuit. Some measurement factors including the filter bandwidth of trigger channel, the detector position alignment and polarization of the pump light are analyzed. The uncertainties of this calibration method are also analyzed, and the relative uncertainties of total calibration are less than 5.8%. The accuracy of this method could be improved in the future.

An approach of open-path gas sensor based on tunable diode laser absorption spectroscopy

Tunable diode laser absorption spectroscopy （TDLAS） is a new method to detect trace-gas qualitatively or quantificationally based on the scan characteristic of the diode laser to obtain the absorption spectroscopy in the characteristic absorption region. A time-sharing scanning open-path TDLAS system using two near infrared distributed feedback （DFB） tunable diode lasers is designed to detect CH4 and H2S in leakage of natural gas. A low-cost Fresnel lens is used in this system as receiving optics which receives the laser beam reflected by a solid corner cube reflector with a distance of up to about 60 m. High sensitivity is achieved by means of wavelength-modulation spectroscopy with second-harmonic detection. The minimum detection limits of 1.1 ppm-m for CH4 and 15 ppm-m for H2S are demonstrated with a total optical path of 120 m. The simulation monitoring experiment of nature gas leakage was carried out with this system. According to the receiving light efficiency of optical system and detectable minimum light intensity of detection, the detectable optical path of the system can achieve 1 - 2 km. The sensor is suitable for natural gas leakage monitoring application.

Measurement of spatial object's exterior attitude based on linear CCD

It is difficult to realize real-time measurement of exterior attitude by the traditional systems based on the area image sensor which have conflict between speed and accuracy. The subsystem for three-dimensional （3D） coordinate reconstruction of point target （S3DCRPT） which is composed of three one-dimensional （1D） cameras based on linear charge-coupled device （CCD） can determine the distant light spots＇ spatial position. The attitude angle of the measured object is determined by the spatial solution while the coordinate reconstruction is separately carried on by the S3DCRPT with some point cooperation targets （PCTs） on the measured object. A new optical system is designed to solve the interference problem with one-to-one relationship between the PCTs and the S3DCRPT optical subsystems, which improves the measurement accuracy and saves space. The mathematical model of the attitude measurement is established, and partial and global calibrations are realized for the multi-camera attitude measurement system. The test results show the feasibility of the exterior attitude measurement based on linear CCD.

Seasonal variability of cloud optical depth over northwestern China derived from CERES/MODIS satellite measurements

The seasonal variability of cloud optical depth over northwestern China derived from Clouds and the Earth＇s Radiant Energy System （CERES） Single Scanner Footprint （SSF） Aqua Moderate Resolution Imaging Spectroradiometer （MODIS） Edition IB data from July 2002 to June 2004 is presented. The regions of interest are those with Asia monsoon influence, the Tianshan and Qilian Mountains, and the Taklimakan Desert. The results show that the instantaneous measurements presented here are much higher than the previous results derived from International Satellite Cloud Climatology Project （ISCCP） D2 monthly mean data. Generally the measurements of cloud optical depth are the highest in summer and the lowest in winter, however, Taklimakan Desert has the lowest measurements in autumn. The regional variation is quite significant over northwestern China.