Complex Network Attack Research Articles

RETRACTED ARTICLE: Network security situation analysis based on a dynamic Bayesian network and phase space reconstruction

When establishing a network attack strategy, target network information is not certain, and the attacker lacks comprehensive, reliable and real-time attack information, making it difficult to perform an attack. To address this issue, a complex scientific network attack method is proposed. The attacker’s income, losses, costs and encountered risks related to a cyberattack are analysed, an index system is established, and a dynamic Bayesian network is used to comprehensively assess the attack effects on network nodes to overcome drawbacks of the traditional node importance assessment method, which relies on a single network topological index or makes static assessments of the target node. A simulation experiment shows that the proposed method synthesizes more node information and observed data for the attack, thereby avoiding the discrepancy between actual attack effects and theoretical expectations of attacks from static assessment and delivering higher levels of attack accuracy and efficiency than previous methods.

A Universal High-Performance Correlation Analysis Detection Model and Algorithm for Network Intrusion Detection System

In big data era, the single detection techniques have already not met the demand of complex network attacks and advanced persistent threats, but there is no uniform standard to make different correlation analysis detection be performed efficiently and accurately. In this paper, we put forward a universal correlation analysis detection model and algorithm by introducing state transition diagram. Based on analyzing and comparing the current correlation detection modes, we formalize the correlation patterns and propose a framework according to data packet timing and behavior qualities and then design a new universal algorithm to implement the method. Finally, experiment, which sets up a lightweight intrusion detection system using KDD1999 dataset, shows that the correlation detection model and algorithm can improve the performance and guarantee high detection rates.

Sequential defense against random and intentional attacks in complex networks.

Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is first proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.

Analysis Method based on Rough Attack-defense Bayes Game Model

In order to solve the strategic interdependence question during the attack and defense process in a complex network, the dissertation proposes an analysis method based on rough attack-defense Bayes game model. After defining the inner structure of attacking agent and defense agent, the dissertation extends traditional object Petri Net and introduces rough set theory into node domain and transition domain, then propose the network attack-defense confrontation model. By dividing domain attack strategy set into equivalence classes, the extraction method of characteristic attack strategy set is given. Then the rough game model and utility function of attack and defense agents are defined, accordingly, the solution of Bayes equilibrium strategy and maximal attack and defense strategy set are proposed. The analysis method can reduce the scale of strategic space of the game model, and suitable for researching on complex network attack and defense action.

Impact of Structural Centrality Based Attacks in Complex Networks

Impact of Structural Centrality Based Attacks in Complex Networks

A Synergetic Pattern Matching Method Based-on DHT Structure for Intrusion Detection in Large-scale Network

Research in network security, with the attacks becoming more frequent, increasing complexity means, for the large-scale network intrusion detection, this paper presents a warning by analyzing the behavior of the log, the contents of the relevant association, through the DHT(Distributed Hash Table) distributed architecture, the Collabarative matching, fusion, and ultimately determine the method of attack paths. First, by improving the classical Apriori algorithm, greatly improving the efficiency of the association. At the same time, through the behavior pattern matching algorithms to extract information about the behavior of the alert and the behavior sequence elements to match the template, and through the right path to finally determine the value of the threat of the network path. After the design of a DHT network, the distributed collaborative match the path used to find complex network attacks. Finally, the overall algorithm flow, proposed a complete threat detection system architecture.

Tolerance of local information-based intentional attacks in complex networks

In this paper, we study the tolerance of complex networks against an intentional attack which takes down network nodes in a decreasing order of their degrees. Specifically, we evaluate an effect which has been largely ignored in the existing studies: in many real-life systems such as communication networks, attacks typically propagate throughout the networks from compromised nodes to their adjacent nodes, utilizing at most local network-topology information. By theoretical analysis and numerical simulations, we show that though different local information-based attacks have different performances, generally speaking, they can be highly efficient. Such insight shall be helpful for the future developments of efficient network attack and protection schemes.

Tolerance of intentional attacks in complex communication networks

Motivated by recent developments in the theory of complex networks, we examine the tolerance of communication networks for intentional attacks that aim to crash the network by taking down network hubs. In addition to providing a brief survey of key existing results, we investigate two different effects that largely have been ignored in past studies. Many communication networks, such as the Internet, are too large for anyone to have global information of their topologies, which makes accurate, intentional attacks virtually impossible; most attacks in communication networks must propagate from nodes to adjacent nodes, utilizing local-network topology information only. We show that incomplete global information has a different impact on intentional attacks in different circumstances, and local information-based attacks can actually be highly efficient. Such insights will be helpful for the future development of efficient protection schemes against network attacks.