Potential Malicious Users Discrimination with Time Series Behavior Analysis
نویسندگان
چکیده
Discriminating the malicious users in a network is crucial in protecting the network entities and preventing any ongoing attacks. In an organized attack, a group users are supposed to behave synchronously in the same manner. In this study, we particularly focus on organized attacks where the attackers create a high volume of requests to overwhelm the server under heavy resource consumption. We propose a novel behavior analysis based on the time series alignment kernel and spectral clustering to determine the group of users that concurrently perform similar behaviors (or dissimilar behavior to that of innocent users). We experiment the proposed model on the simulated data.
منابع مشابه
DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks
The widespread location-based social networks (LBSNs) have immersed into our daily life. As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing the users’ decision in urban computing environments. To provide reliable information and improve the experience for...
متن کاملProviding a Method to Identify Malicious Users in Electronic Banking System Using Fuzzy Clustering Techniques
Money-Laundering causes a higher prevalence of crime and reduces the desire tending to invest in productive activities. Also, it leads to weaken the integrity of financial markets and decrease government control over economic policy. Banks are able to prevent theft, fraud, money laundering conducted by customers through identification of their clients’ behavioral characteristics. This leads to ...
متن کاملProviding a Method to Identify Malicious Users in Electronic Banking System Using Fuzzy Clustering Techniques
Money-Laundering causes a higher prevalence of crime and reduces the desire tending to invest in productive activities. Also, it leads to weaken the integrity of financial markets and decrease government control over economic policy. Banks are able to prevent theft, fraud, money laundering conducted by customers through identification of their clients’ behavioral characteristics. This leads to ...
متن کاملDiscrimination of time series based on kernel method
Classical methods in discrimination such as linear and quadratic do not have good efficiency in the case of nongaussian or nonlinear time series data. In nonparametric kernel discrimination in which the kernel estimators of likelihood functions are used instead of their real values has been shown to have good performance. The misclassification rate of kernel discrimination is usually less than ...
متن کاملDyVSoR: dynamic malware detection based on extracting patterns from value sets of registers
To control the exponential growth of malware files, security analysts pursue dynamic approaches that automatically identify and analyze malicious software samples. Obfuscation and polymorphism employed by malwares make it difficult for signature-based systems to detect sophisticated malware files. The dynamic analysis or run-time behavior provides a better technique to identify the threat. In t...
متن کامل