Enterprise AI Analysis
Review of eXplainable artificial intelligence for cybersecurity systems
This article reviews approaches based on artificial intelligence (AI), which contributes to the security of cyber environments. We examine existing techniques using several indicators: explainability, performance and robustness. These indicators have been chosen based on their importance for user acceptance and interpretability of the approach. Indeed, the Al field is vast and is divided into several sub-domains. The two most well-known sub-domains are symbolic Al (representation of knowledge, rules and operations based on symbols) and numeric Al (calculations and algorithms using numeric information, focusing on the result, not the representation of knowledge). While most approaches investigated come from numeric Al, we conclude on the need for hybrid Al systems, combining the advantages of both Al sub-fields while maximising the protection provided against cyberattacks.
Executive Impact: Key Metrics
The review highlights that Numeric AI models offer high accuracy and precision (often above 97%) in detecting cyber threats across various categories like malware, botnets, and fraud. However, they generally suffer from a lack of transparency and explainability, often requiring post-hoc interpretations rather than being inherently understandable. Robustness is also an underdeveloped area, with few studies addressing adversarial inputs or distributional changes adequately. The conclusion strongly advocates for Hybrid AI systems that combine the strengths of Numeric AI (efficiency, scalability) and Symbolic AI (explainability, reasoning) to overcome these limitations and meet evolving regulatory demands.
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Average Detection Accuracy
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Average Detection Precision
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Faster Threat Response
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Breaches Involving Human Error
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Network Security
Endpoint Security
Application Security
User Security
Covers protection of network infrastructure from unauthorized access, intrusion, and attacks like DoS. Focuses on maintaining integrity, availability, and confidentiality of network data and resources.
Protects end-user devices (computers, IoT) from cyber threats, including antivirus, firewalls, and malware scanners. Ensures device reliability, usability, and protection against malicious alterations.
Secures all applications (web, mobile, cloud) from vulnerabilities, unauthorized access, and misuse. Involves code reviews, penetration testing, and runtime protection, ensuring data protection at rest and in transit.
Measures to protect users from cyber threats and ensure safe system usage. Includes education, awareness training, access controls, multi-factor authentication, and disaster recovery plans.
Numeric AI Accuracy in Cybersecurity
97.48% Average Accuracy (%)Numeric AI models demonstrate exceptional performance, with an average detection accuracy of 97.48% across various cybersecurity applications. This highlights their capability in identifying and responding to threats efficiently.
Enterprise Process Flow
Data Analysis & Preprocessing
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Model Training & Validation
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Performance Metrics Evaluation
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Explainability Assessment
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Robustness Testing
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Deployment & Monitoring
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Hybrid AI for Malware Detection: The Yan et al. Approach
Yan et al. [45] developed a hybrid system for mobile malware detection. They first trained a supervised Deep Neural Network (DNN) to process massive network traffic data, achieving high accuracy. To address the DNN's black-box nature, they extracted rules from the trained layers to build an interpretable decision tree. This decision tree, compact enough to be embedded on an FPGA, provides high-level interpretations for malware behavior. This approach successfully combines the efficiency of numeric AI with the explainability of symbolic AI, resulting in a robust and interpretable solution for endpoint security.
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Your AI Implementation Roadmap
A phased approach to integrate AI seamlessly into your enterprise, ensuring maximum impact and minimal disruption.
Phase 1: Discovery & Strategy Alignment
Assess current cybersecurity posture, identify key vulnerabilities, and define AI integration goals. Formulate a tailored strategy based on enterprise-specific needs and regulatory requirements.
Phase 2: Data & Model Development
Gather and preprocess relevant cybersecurity data. Develop and train initial AI models (hybrid approach recommended) focusing on specific threat detection or response capabilities.
Phase 3: Integration & Testing
Integrate AI systems into existing security infrastructure. Conduct rigorous testing, including adversarial testing and real-world simulations, to validate performance, explainability, and robustness.
Phase 4: Deployment & Continuous Optimization
Deploy AI systems in a production environment. Establish continuous monitoring, retraining mechanisms, and feedback loops to adapt to evolving threats and ensure long-term effectiveness and compliance.
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