Defending Against Intelligent Attackers at Large Scales
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913808052649984 |
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| author | Lohn, Andrew J. |
| author_facet | Lohn, Andrew J. |
| contents | We investigate the scale of attack and defense mathematically in the context of AI's possible effect on cybersecurity. For a given target today, highly scaled cyber attacks such as from worms or botnets typically all fail or all succeed. Here, we consider the effect of scale if those attack agents were intelligent and creative enough to act independently such that each attack attempt was different from the others or such that attackers could learn from their successes and failures. We find that small increases in the number or quality of defenses can compensate for exponential increases in the number of independent attacks and for exponential speedups. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Defending Against Intelligent Attackers at Large Scales Lohn, Andrew J. Cryptography and Security Computers and Society We investigate the scale of attack and defense mathematically in the context of AI's possible effect on cybersecurity. For a given target today, highly scaled cyber attacks such as from worms or botnets typically all fail or all succeed. Here, we consider the effect of scale if those attack agents were intelligent and creative enough to act independently such that each attack attempt was different from the others or such that attackers could learn from their successes and failures. We find that small increases in the number or quality of defenses can compensate for exponential increases in the number of independent attacks and for exponential speedups. |
| title | Defending Against Intelligent Attackers at Large Scales |
| topic | Cryptography and Security Computers and Society |
| url | https://arxiv.org/abs/2504.18577 |