Measuring Network Security Research Articles

Measuring network security in the cloud : A roadmap for proactive defense

An exhaustive examination and evaluation of hierarchy network security measuring and optimal active defense in the context of cloud computing is provided in this paper. After collecting all the cloud platform’s security-related data obtained via evaluation, the pertinent security data is summarized and analyzed to create the cloud platform’s safety record index. This index serves as a benchmark for the cloud platform administrators to evaluate the security vulnerabilities associated with their cloud platform. It offers cloud platform administrators a standard against which to assess the security hazards of cloud platforms. We primarily investigate the building of the cloud platform, the building process of the security situational awareness system, and the estimation of the safety circumstance value and use via the cloud platform’s security circumstance understanding system, thereby significantly enhancing the cloud platform’s stability, security, and dependability. By implementing the approach, the limitations of conventional network security management are circumvented, as the latter is predicated solely on historical data and is incapable of perceiving real-time alterations in the security condition of the system. Concurrently, the anticipated outcomes are incorporated into the imprecise decision-making system’s input, enhancing the evaluation’s precision. The approach improves the efficiency and efficacy of predicting the network safety situation in real time, augments the algorithm’s rate of convergence and prediction precision, and prevents overfitting from occurring. Simulation experiments conducted using the internet network safety posture dataset demonstrate that this research approach exhibits superior learning efficiency and lower prediction error compared to conventional machine learning and other deep learning techniques.

Multi-layer Network Attack and Defense Model Based on Entropy Method to Measure Network Security

Network security is also facing major challenges. On the one hand, network security assessment can dynamically reflect the state of network security and reveal potential threats. On the other hand, network security assessment is the basis of network security management, it is also the prerequisite for building a reliable and secure network. This paper proposes a multi-level network security measurement model, which divides the network into a network environment layer and a network attack and defense layer. The network environment layer considers the network hardware security and the reliability of a network topology. The network attack and defense layer mainly examines the security status of the network from the perspective of network attack and defense. The model comprehensively evaluates and measures the network from different layers of the network, and conducts experiments. The result of experiment proves that the model can effectively assess the security of the network.

Multi-Layer, Multi-Dimensional and Multi-Granularity Network Model to Measure Network Security

Recently, network security is more and more important and the network security measure is the premise to build a robust and secure network. However, the methods of measuring the network security are limited. For example, the most methods are not comprehensive, which only consider a part of the network ignoring the overall network. Therefore, this paper proposes a new multi-layer, multi-dimensional and multi-granularity network model based on the attack graph and CVSS. The model divides the network into four layers. The four layers can measure the network security completely and effectively. For each layer, two dimensions are quantified. The measure value of each layer is rated score 0-10. In addition, this model takes the different network granularities into account, making the network security model more comprehensive. In order to examine the validity of the network model, this paper carries out two experiments by configuring five networks with different security configurations. It is found that this model not only can identify the network security level effectively but also can quickly locate the security problems.

Multi-Layer, Multi-Dimensional and Multi-Granularity Network Model to Measure Network Security

Multi-Layer, Multi-Dimensional and Multi-Granularity Network Model to Measure Network Security

Analytical framework for measuring network security using exploit dependency graph

Attack graph is a popular tool for modelling multi-staged, correlated attacks on computer networks. Attack graphs have been widely used for measuring network security risks. Majority of the works on attack graph use host-based or state-based approaches. These attack graph models are either too restrictive or too resource consuming. Also, a significant portion of these works have used ‘probability of successfully exploiting a network’ as the metric. This approach requires that the ‘probability of successfully exploiting individual vulnerabilities’ be known a priori. Finding such probabilities is inherently difficult. This present study uses exploit dependency graph, which is a space efficient and expressive attack graph model. It also associates an additive cost with executing individual exploits, and defines a security metric in terms of the ‘minimum cost required to successfully exploit the network’. The problem of calculating the said metric is proved to be NP-complete. A modified depth first branch and bound algorithm has been described for calculating it. This study also formulates, a linear-time computable, security metric in terms of the ‘expected cost required to successfully exploit the network’ assuming a random attacker model and an uncorrelated attack graph.

Large-Scale Integration of Wind Generation Including Network Temporal Security Analysis

This paper presents a methodology to assess large-scale wind generation projects that considers their effect on network security. The proposed method is based on contingency analysis, including temporal study. Inputs to the simulation are grid model, forecasted load, conventional generation profiles, and wind variability of proposed projects. A time-step simulation is run for the time horizon to produce benefit indices for every location (bus) in the system. The congested transmission elements that require expansion are identified and ranked as part of the simulation. Each wind project in the proposed portfolio can result in benefits or costs for grid security. Policy makers can then use the method to design policies that ensure preservation of long-term system security. Developers could use the tool to identify security effects and assess their wind portfolios. Measuring network security and determining benefits of large-scale wind projects is a complex planning task that involves several aspects: temporal wind variability, spatial distribution of flows, multiple load and generation profiles, and numerous possible contingencies. All these wind project development aspects must be isolated to identify and correctly assign security costs and benefits