Spatiotemporal patterns and predictability of cyberattacks

TL;DR

This study uncovers intrinsic spatiotemporal patterns in cyberattacks using extensive data analysis, revealing high predictability that could enable proactive mitigation strategies in cybersecurity.

Contribution

The paper demonstrates the existence of intrinsic, predictable spatiotemporal attack patterns and identifies key attacker behaviors through quantitative analysis of attack data.

Findings

Few major attackers dominate attack traffic

Distinct spatiotemporal patterns characterize attack types

High attack predictability suggests potential for proactive defense

Abstract

A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term "spatio" refers to the IP address space. In particular, we focus on analyzing {\em macroscopic} properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack "fingerprints" and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches.