Cybersecurity through Entropy Injection: A Paradigm Shift from Reactive Defense to Proactive Uncertainty
- URL: http://arxiv.org/abs/2504.11661v1
- Date: Tue, 15 Apr 2025 23:08:57 GMT
- Title: Cybersecurity through Entropy Injection: A Paradigm Shift from Reactive Defense to Proactive Uncertainty
- Authors: Kush Janani,
- Abstract summary: entropy injection is deliberately infusing randomness into security mechanisms to increase unpredictability and enhance system security.<n>We show that entropy injection can significantly reduce attack probability, with some implementations showing more than 90% reduction with minimal performance impact.<n>We conclude that entropy injection represents a paradigm shift from reactive defense to proactive uncertainty management.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cybersecurity often hinges on unpredictability, with a system's defenses being strongest when sensitive values and behaviors cannot be anticipated by attackers. This paper explores the concept of entropy injection-deliberately infusing randomness into security mechanisms to increase unpredictability and enhance system security. We examine the theoretical foundations of entropy-based security, analyze real-world implementations including Address Space Layout Randomization (ASLR) and Moving Target Defense (MTD) frameworks, evaluate practical challenges in implementation, and compare entropy-based approaches with traditional security methods. Our methodology includes a systematic analysis of entropy's role across various security domains, from cryptographic operations to system-level defenses. Results demonstrate that entropy injection can significantly reduce attack probability, with some implementations showing more than 90% reduction with minimal performance impact. The discussion highlights the trade-offs between security benefits and operational complexity, while identifying future directions for entropy-enhanced security, including integration with artificial intelligence and quantum randomness sources. We conclude that entropy injection represents a paradigm shift from reactive defense to proactive uncertainty management, offering a strategic approach that can fundamentally alter the balance between attackers and defenders in cybersecurity.
Related papers
- Concept Enhancement Engineering: A Lightweight and Efficient Robust Defense Against Jailbreak Attacks in Embodied AI [19.094809384824064]
Embodied Intelligence (EI) systems integrated with large language models (LLMs) face significant security risks.
Traditional defense strategies, such as input filtering and output monitoring, often introduce high computational overhead.
We propose Concept Enhancement Engineering (CEE) to enhance the safety of embodied LLMs by dynamically steering their internal activations.
arXiv Detail & Related papers (2025-04-15T03:50:04Z) - A Computational Model for Ransomware Detection Using Cross-Domain Entropy Signatures [0.0]
An entropy-based computational framework was introduced to analyze multi-domain system variations.<n>A detection methodology was developed to differentiate between benign and ransomware-induced entropy shifts.
arXiv Detail & Related papers (2025-02-15T07:50:55Z) - Hierarchical Entropy Disruption for Ransomware Detection: A Computationally-Driven Framework [0.0]
Monitoring entropy variations offers an alternative approach to identifying unauthorized data modifications.<n>A framework leveraging hierarchical entropy disruption was introduced to analyze deviations in entropy distributions.<n> evaluating the framework across multiple ransomware variants demonstrated its capability to achieve high detection accuracy.
arXiv Detail & Related papers (2025-02-12T23:29:06Z) - Discrete-modulated continuous-variable quantum key distribution secure against general attacks [0.0]
This work presents a security analysis of DM-CV-QKD against general sequential attacks, including finite-size effects.
Remarkably, our proof considers attacks that are neither independent nor identical, and makes no assumptions about the Hilbert space dimension of the receiver.
arXiv Detail & Related papers (2024-09-04T11:50:18Z) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers.
We then present the pointwise feasibility conditions of the resulting safety controller.
We use these conditions to devise an event-triggered online data collection strategy.
arXiv Detail & Related papers (2022-08-23T05:02:09Z) - CC-Cert: A Probabilistic Approach to Certify General Robustness of
Neural Networks [58.29502185344086]
In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks.
It is important to provide provable guarantees for deep learning models against semantically meaningful input transformations.
We propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds.
arXiv Detail & Related papers (2021-09-22T12:46:04Z) - Certifiers Make Neural Networks Vulnerable to Availability Attacks [70.69104148250614]
We show for the first time that fallback strategies can be deliberately triggered by an adversary.
In addition to naturally occurring abstains for some inputs and perturbations, the adversary can use training-time attacks to deliberately trigger the fallback.
We design two novel availability attacks, which show the practical relevance of these threats.
arXiv Detail & Related papers (2021-08-25T15:49:10Z) - Constraints Satisfiability Driven Reinforcement Learning for Autonomous
Cyber Defense [7.321728608775741]
We present a new hybrid autonomous agent architecture that aims to optimize and verify defense policies of reinforcement learning (RL)
We use constraints verification (using satisfiability modulo theory (SMT)) to steer the RL decision-making toward safe and effective actions.
Our evaluation of the presented approach in a simulated CPS environment shows that the agent learns the optimal policy fast and defeats diversified attack strategies in 99% cases.
arXiv Detail & Related papers (2021-04-19T01:08:30Z) - Measurement-driven Security Analysis of Imperceptible Impersonation
Attacks [54.727945432381716]
We study the exploitability of Deep Neural Network-based Face Recognition systems.
We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim.
We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
arXiv Detail & Related papers (2020-08-26T19:27:27Z) - A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems.
This paper proposes a self-supervised adversarial training mechanism in the input space.
It provides significant robustness against the textbfunseen adversarial attacks.
arXiv Detail & Related papers (2020-06-08T20:42:39Z) - Adversarial vs behavioural-based defensive AI with joint, continual and
active learning: automated evaluation of robustness to deception, poisoning
and concept drift [62.997667081978825]
Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security.
In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
arXiv Detail & Related papers (2020-01-13T13:54:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.