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Cyber situation awareness: modeling detection of cyber attacks with instance-based learning theory.
Varun Dutt1, Young-Suk Ahn, Cleotilde Gonzalez
1School of Computing and Electrical Engineering, School of Humanities and Social Sciences, Indian Institute of Technology, Mandi, PWD Rest House 2nd Floor, Mandi - 175 001, H.P., India. varun@iitmandi.ac.in
Human Factors
|July 9, 2013
Summary
Defender experience and threat tolerance impact network threat detection accuracy. Adversarial behavior strategy is also crucial for effective cyber defense. Understanding these factors improves overall threat identification.
Area of Science:
- Cybersecurity
- Cognitive Science
- Human-Computer Interaction
Background:
- Cyber attacks disrupt operations, necessitating a deeper understanding of threat detection.
- Defender behavior (experience, threat tolerance) and attacker strategy influence detection efficacy.
- Cognitive modeling is employed to predict these influencing factors.
Purpose of the Study:
- To assess how adversary behavior affects a defender's accurate and timely network threat detection.
- To investigate the interplay between defender characteristics and adversarial tactics in cybersecurity.
Main Methods:
- Utilized Instance-Based Learning Theory (IBLT) cognitive models for defenders.
- Simulated defender experience (threat-prone vs. nonthreat-prone) and threat tolerance (risk-averse vs. risk-seeking).
- Modeled adversarial behavior using patient (late threats) and impatient (early threats) attack strategies.
Main Results:
- Risk-averse, threat-prone defenders showed better detection against impatient adversaries.
- This advantage diminished when adversaries employed a patient strategy.
- Defender's experience and tolerance significantly influenced detection outcomes.
Conclusions:
- Defender's prior threat experience and threat tolerance are key predictors of detection accuracy.
- Adversarial behavior's nature is a critical, complementary factor in threat detection.
- Decision-support tools should integrate defender traits and adversary behavior for enhanced cybersecurity.
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