A Novel Predication Approach for Network Security Situation Inspired by Immunity

To effectively prevent large-scale network security attacks, a novel Predication Approach for Network Security Situation inspired by Immunity (PANSSI) is proposed. In this predication approach, the concepts and formal definitions of antigen and antibody in the network security situation predication domain are given; meanwhile, the mathematical models of some antibody evolution operators being related to PANSSI are exhibited. By analyzing time series and computing the affinity between antigen and antibody in artificial immune system, network security situation predication model is established, and then the future situation of network security attacks is predicted by it. Experimental results prove that PANSSI can forecast the future network security situation real-timely and correctly, and provides a novel approach for network security situation predication.

Download Full-text

Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

10.1101/2021.04.12.439440 ◽

2021 ◽

Author(s):

Florian Krause ◽

Nikolaos Kogias ◽

Martin Krentz ◽

Michael Luehrs ◽

Rainer Goebel ◽

...

Keyword(s):

Real Time ◽

Executive Control ◽

Large Scale ◽

Acute Stress ◽

Self Regulation ◽

Real Life ◽

Brain Network ◽

Large Scale Network ◽

Novel Approach ◽

Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

Download Full-text

A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents

The Scientific World JOURNAL ◽

10.1155/2013/827376 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7

Author(s):

Hui He ◽

Guotao Fan ◽

Jianwei Ye ◽

Weizhe Zhang

Keyword(s):

Network Security ◽

Network Topology ◽

Early Warning ◽

Early Warning System ◽

Large Scale ◽

Warning System ◽

Small Scale ◽

Large Scale Network ◽

Scale Network ◽

Security Incidents

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

Download Full-text