Network entity characterization and attack prediction

by   Vaclav Bartos, et al.

The devastating effects of cyber-attacks, highlight the need for novel attack detection and prevention techniques. Over the last years, considerable work has been done in the areas of attack detection as well as in collaborative defense. However, an analysis of the state of the art suggests that many challenges exist in prioritizing alert data and in studying the relation between a recently discovered attack and the probability of it occurring again. In this article, we propose a system that is intended for characterizing network entities and the likelihood that they will behave maliciously in the future. Our system, namely Network Entity Reputation Database System (NERDS), takes into account all the available information regarding a network entity (e. g. IP address) to calculate the probability that it will act maliciously. The latter part is achieved via the utilization of machine learning. Our experimental results show that it is indeed possible to precisely estimate the probability of future attacks from each entity using information about its previous malicious behavior and other characteristics. Ranking the entities by this probability has practical applications in alert prioritization, assembly of highly effective blacklists of a limited length and other use cases.



page 1

page 2

page 3

page 4


A Heterogeneous Graph Learning Model for Cyber-Attack Detection

A cyber-attack is a malicious attempt by experienced hackers to breach t...

Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing

Entity disambiguation (ED) is the last step of entity linking (EL), when...

DDoSDet: An approach to Detect DDoS attacks using Neural Networks

Cyber-attacks have been one of the deadliest attacks in today's world. O...

From product recommendation to cyber-attack prediction: Generating attack graphs and predicting future attacks

Modern information society depends on reliable functionality of informat...

Semisupervised Adversarial Neural Networks for Cyber Security Transfer Learning

On the path to establishing a global cybersecurity framework where each ...

A Novel Approach to Detect Phishing Attacks using Binary Visualisation and Machine Learning

Protecting and preventing sensitive data from being used inappropriately...

A Large Scale Study and Classification of VirusTotal Reports on Phishing and Malware URLs

VirusTotal (VT) provides aggregated threat intelligence on various entit...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.