Real Computer Networks Research Articles

Error Elimination From Bloom Filters in Computer Networks Represented by Graphs

An undirected mathematical graph, $G = (V, E)$ where $V$ is a set of vertices and $E = V \times V$ is the set of edges, can model a computer network. By this consideration we search for solutions to real computer network problems with a theoretical approach. This approach is based on labelling each edge by a subset of a universal set, and then encoding a path as the union of the labels of its edges. We label each vertex $v \in V$ by using a subset of universal set $U$, then we present a way to encode shortest paths in the graph $G$ by using a way optimizing the data. By mathematical approach, it is provable that the labelling method we introduced eliminates the errors from the shortest paths in the graph. We aim to obtain the results in a more efficient use of network resources and to reduce network traffic. This shows how our theoretical approach works in real world network systems.

Parameter Estimation of Binned Hawkes Processes

A key difficulty that arises from real event data is imprecision in the recording of event time-stamps. In many cases, retaining event times with a high precision is expensive due to the sheer volume of activity. Combined with practical limits on the accuracy of measurements, binned data is common. In order to use point processes to model such event data, tools for handling parameter estimation are essential. Here we consider parameter estimation of the Hawkes process, a type of self-exciting point process that has found application in the modeling of financial stock markets, earthquakes and social media cascades. We develop a novel optimization approach to parameter estimation of binned Hawkes processes using a modified Expectation-Maximization algorithm, referred to as Binned Hawkes Expectation Maximization (BH-EM). Through a detailed simulation study, we demonstrate that existing methods are capable of producing severely biased and highly variable parameter estimates and that our novel BH-EM method significantly outperforms them in all studied circumstances. We further illustrate the performance on network flow (NetFlow) data between devices in a real large-scale computer network, to characterize triggering behavior. These results highlight the importance of correct handling of binned data. Supplementary materials for this article are available online.

Multi-type relational clustering for enterprise cyber-security networks

Several cyber-security data sources are collected in enterprise networks providing relational information between different types of nodes in the network, namely computers, users and ports. This relational data can be expressed as adjacency matrices detailing inter-type relationships corresponding to relations between nodes of different types and intra-type relationships showing relationships between nodes of the same type. In this paper, we propose an extension of Non-Negative Matrix Tri-Factorisation (NMTF) to simultaneously cluster nodes based on their intra and inter-type relationships. Existing NMTF based clustering methods suffer from long computational times due to large matrix multiplications. In our approach, we enforce stricter cluster indicator constraints on the factor matrices to circumvent these issues. Additionally, to make our proposed approach less susceptible to variation in results due to random initialisation, we propose a novel initialisation procedure based on Non-Negative Double Singular Value Decomposition for multi-type relational clustering. Finally, a new performance measure suitable for assessing clustering performance on unlabelled multi-type relational data sets is presented. Our algorithm is assessed on both a simulated and real computer network against standard approaches showing its strong performance.

Minimum Label s-t Cut has large integrality gaps

The Min Labels-tCut problem is a fundamental problem in combinatorial optimization. This problem comes from many applications in real world, for example, information security and computer networks. We study two linear programs for Min Labels-tCut, proving that both of them have large integrality gaps, namely, Ω(m) and Ω(m1/3−ϵ) for the respective linear programs, where m is the number of edges in the input graph of the problem and ϵ>0 is any arbitrarily small constant. As Min Labels-tCut is NP-hard and the linear programming technique is a main approach to design approximation algorithms, our results give negative answer to the hope that designs can be found for better approximation algorithms for Min Labels-tCut that purely rely on linear programming.

On Bayesian new edge prediction and anomaly detection in computer networks

Monitoring computer network traffic for anomalous behaviour presents an important security challenge. Arrivals of new edges in a network graph represent connections between a client and server pair not previously observed, and in rare cases these might suggest the presence of intruders or malicious implants. We propose a Bayesian model and anomaly detection method for simultaneously characterising existing network structure and modelling likely new edge formation. The method is demonstrated on real computer network authentication data and successfully identifies some machines which are known to be compromised.

Propagation Effect of a Virus Outbreak on a Network with Limited Anti-Virus Ability.

This paper describes a new computer virus spreading model which takes into account the possibility of a virus outbreak on a network with limited anti-virus ability. Then, the model is investigated for the existence of equilibria and their stabilities are proved and illustrated. Moreover, it is found that these two factors are not only relative to the threshold value determining whether the virus becomes extinct or not, but that they are also relative to the virus epidemic levels. Theoretical and experimental results indicate that, in some ways, it would be practically possible to eradicate the virus or suppress its prevalence below a suitable level. Consequently, some suggestions are proposed that may help eradicate or suppress virus propagation over a real computer network.

Effect of Maintenance on Computer Network Reliability

At the time of the new information technologies, computer networks are inescapable in any large organization, where they are organized so as to form powerful internal means of communication. In a context of dependability, the reliability parameter proves to be fundamental to evaluate the performances of such systems. In this paper, we study the reliability evaluation of a real computer network, through three reliability models. The computer network considered (set of PCs and server interconnected) is localized in a company established in the west of Algeria and dedicated to the production of ammonia and fertilizers. The result permits to compare between the three models to determine the most appropriate reliability model to the studied network, and thus, contribute to improving the quality of the network. In order to anticipate system failures as well as improve the reliability and availability of the latter, we must put in place a policy of adequate and effective maintenance based on a new model of the most common competing risks in maintenance, Alert-Delay model. At the end, dependability measures such as MTBF and reliability are calculated to assess the effectiveness of maintenance strategies and thus, validate the alert delay model.

Computer Network Behavior Research Based on the Theory of Complexity

Guided by the thought of system integrity, complex behavior of computer network is thoroughly study based on complexity theory. Based on chaos theory complex behavior property of computer networks traffic with different time-scale is deeply researched, the result indicates the universe of the chaos and scale-free behavior in real computer networks. The network behavior evolvement model that correctly describes function mechanism of general real networks is presented, and formal description of the evolvement rules is implemented, the fluctuations formula of input traffic is also deducted. Based on the mean field model of network traffic, the dynamical properties of network traffic system is analyzed, and the condition of chaotic dynamical behavior is achieved under mean field hypothesis.

New Intelligent Computer Intrusion Detection Method Using Hessian Local Linear Embedding and Multi-Kernel Support Vector Machine

Computer networks frequently collapse under the destructive intrusions. It is crucial to detection hidden intrusions to protect the computer networks. However, a computer intrusion often distributes high dimensional characteristic signals, which increases the difficulty of intrusion detection. Literature review indicates that limited work has been done to address the nonlinear dimension reduction problem in computer intrusion detection. Hence, this study has proposed a new intrusion detection method based on the Hessian Local Linear Embedding (HLLE) and multi-kernel Support Vector Machine (SVM). The HLLE was firstly used to reduce the dimension of the original intrusion date in a nonlinear manner. Then the SVM with multiply kernels was employed to detect the intrusions. A real computer network experimental system has been established to evaluate the proposed method. Four typical intrusions have been tested. The test results show high effectiveness of the new detection method. In addition, the new method has been compared with the single-kernel SVM with Local Linear Embedding (LLE) or Principal Component Analysis (PCA). The comparison results demonstrate that the proposed HLLE plus multi-kernel SVM can provide the best computer intrusion detection rate of 97.1%.

Multi-objective optimization for stochastic computer networks using NSGA-II and TOPSIS

Network reliability is a performance indicator of computer/communication networks to measure the quality level. However, it is costly to improve or maximize network reliability. This study attempts to maximize network reliability with minimal cost by finding the optimal transmission line assignment. These two conflicting objectives frustrate decision makers. In this study, a set of transmission lines is ready to be assigned to the computer network, and the computer network associated with any transmission line assignment is regarded as a stochastic computer network (SCN) because of the multistate transmission lines. Therefore, network reliability means the probability to transmit a specified amount of data successfully through the SCN. To solve this multiple objectives programming problem, this study proposes an approach integrating Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). NSGA-II searches for the Pareto set where network reliability is evaluated in terms of minimal paths and Recursive Sum of Disjoint Products (RSDP). Subsequently, TOPSIS determines the best compromise solution. Several real computer networks serve to demonstrate the proposed approach.

A probability approach to anomaly detection with twin support vector machines

Classification of intrusion attacks and normal network flow is a critical and challenging issue in network security study. Many intelligent intrusion detection models are proposed, but their performances and efficiencies are not satisfied to real computer networks. This paper presents a novel effective intrusion detection system based on statistic reference model and twin support vector machines (TWSVMs). Moreover, a network flow feature selection procedure has been studied and implemented with TWSVMs. The performances of proposed system are evaluated through using the fifth international conference on knowledge discovery and data mining in 1999 (KDD’99) data set collected at MIT’s Lincoln Labs and the results indicate that the proposed system is more efficient and effective than conventional support vector machines (SVMs) and TWSVMs.

Space capsule recovery—Evaluation of risk factors, safety plans and procedures and design of experiments for systems qualification

The Indian Space capsule (SRE-1) launched aboard PSLV-C7 rocket, was recovered successfully in the Bay of Bengal on January 22, 2007 after its orbital sojourn of 12 days. Apart from serving as a platform for micro-gravity experiments, SRE-1 demonstrated ISRO's capability in the field of orbital reentry and recovery technologies. Satish Dhawan Space Centre (SDSC SHAR), the Spaceport of India was given the prime responsibility of assessment of mission risk, formulation and execution of safety plans and procedures, design and conduct of trials for validating the mission-critical sub-systems as well as the physical recovery of the capsule. To achieve these objectives, a number of drop tests were designed and conducted by SDSC SHAR involving real time computer network, ground-based tracking and telemetry stations, communication systems, safety and material handling systems, target identification and recovery systems. Dissemination of relevant information and coordination with multiple external organizations such as Indian Coast Guard, Indian Air Force and Indian Navy is an important aspect of these experiments. This paper delineates the methodologies designed and implemented at SDSC SHAR for validating those critical systems whose functionality finally culminated in the success of the mission, enabling India to join the elite group of nations with reentry module recovery capability.

Adaptive sampling in measuring traffic parameters in a computer network using a fuzzy regulator and a neural network

Adaptive sampling using a neural network and a fuzzy regulator is described as applied to computer network traffics. The objective of this approach is to maximally reduce the amount of data to be processed with preservation of acceptable measurement accuracy. The results of experimental verification of sampling efficiency are also presented that are based on the traffic data archive of a real computer network.

A Design of Gain-scheduled Congestion Controllers using State Predictive Observers

Abstract In this paper, a design problem of congestion controllers is discussed for TCP/AQM(Transmission Control Protocol/Active Queue Management) networks. The proposed method consists of two control techniques. First one is based on a gain-scheduling technique considering nonlinearities of TCP/AQM networks. But the designed congestion controller is a state feedback controller and it is impossible to embed the congestion controller in real computer networks. To avoid this problem, second one is based on a design technique of state predictive observer for linear time-delay systems. Thus it can be possible to embed the congestion controller in real networks by combining these methods. Firstly dynamical models of TCP/AQM are described as linear systems with self-scheduling parameters, which also depend on information delay. Here it is distinguishing to focus on constraints on the maximum queue length and TCP window-size, which are the network resources in TCP/AQM networks. And a design method of memoryless state feedback controllers is shown for linear system with a self-scheduling parameter and an information delay. But the designed gain- scheduled congestion controller is a state feedback controller and it is impossible to apply this controller to computer networks directly. Thus observers are also designed and the observer-based congestion controllers are derived.

Analysis of power spectrum and [formula omitted] type power law in a complex computer network model

The computer network, as a complex nonlinear system, exhibits plenty of nonlinear complexities phenomena. More and more researchers have been attracted to these exciting dynamic behaviors. In general, computer simulation studies are widely used in the researches on network dynamics. Unfortunately, too simple and regular topologies are adopted in these network models. It inevitably results in the fact that some important complexities behaviors in a real computer network may not emerge in most of computer experimental studies. In this paper, the phase transition phenomena between the packet creation rate and the lifetime of packet, on basis of more realistic computer network models composed of the Transit–Stub topology and the proposed mechanism of packet traffic, are explored. To illustrate the fluctuation of packets more clearly, a power spectrum is made and the 1/f type power law is found. Finally, simulation results and some research emphases in the future are discussed.

The Use of Synchronous Computer Networks in Second Language Instruction: A Preliminary Report

ABSTRACT This paper reports some personal observations regarding a second language teaching situation where non‐native speakers of Portuguese participated in class discussions via real time computer networks. Synchronous computer networks have been utilized in university courses to improve group participation in writing and composition in L1 situations. This same process offers L2 an opportunity to participate in interlanguage discussions via computer. Preliminary observations from this experience suggest that computer assisted class discussions may promote increased participation from all members of a work group, allow students to speak without interruption, reduce anxiety which is frequently present in oral conversations, render honest and candid expression of emotion, provide personalized identification of target language errors and create substantial interlanguage communication among L2 learners.

Optimal program and data locations in computer networks

An optimization procedure for the allocation of program and data files in a computer network is presented. This algorithm takes into account the dependencies between files and programs such as occur in real heterogeneous computer networks. Insights into whether or not to convert programs from one computer to another can also be gained from the model. A search procedure for the file location problem is described, along with an example and a possible application of the model.