PaperlayerView exact revision on arXiv

Source-linked AI summary

Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detection

Marc Schmitt

arXiv:2401.01342v1cs.CRcs.LG

TL;DR

The paper addresses the need to protect increasingly connected digital ecosystems from cybercrime and shifting attack techniques. It evaluates AI/ML-based malware and intrusion detection across network, mobile, and IoT security, while examining integration challenges in complex IT infrastructures. The reported outcome is that these applications can enhance cybersecurity prediction accuracy and increase the resilience of AI-based defense systems.

  • Problem

    Rapid digitalization and a constantly shifting technological foundation create a complex environment for cybercriminals, increasing the need for intelligent threat detection.

  • Method

    The paper evaluates AI/ML-based anomaly detection, malware detection, and intrusion detection, and examines integrating these models into complex IT infrastructures.

  • Results

    AI/ML applications can enhance prediction accuracy in cybersecurity and increase the resilience of AI-based defense systems for network, mobile, and IoT security.

  • Takeaways & Limitations

    AI-based cyber threat and malware detection is presented as a means of protecting modern digital ecosystems across network, mobile, and IoT contexts.

  • Takeaways & Limitations

    AI algorithms are not guaranteed to remain effective against cybercriminals’ obfuscation and concealment tactics, motivating further work on robustness and response.

Abstract

from arXiv · show

The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to governments, businesses, and civil societies. The pervasiveness of digital technologies combined with a constantly shifting technological foundation has created a complex and powerful playground for cybercriminals, which triggered a surge in demand for intelligent threat detection systems based on machine and deep learning. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems. The primary focus is on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection and how to integrate those models in the context of network security, mobile security, and IoT security. The discussion highlights the challenges when deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures, including options to overcome those challenges. Finally, the paper provides future research directions to further increase the security and resilience of our modern digital industries, infrastructures, and ecosystems.

1 Introduction

Digitalization and hyperconnectivity expand both the value of modern infrastructures and the attack surface available to cybercriminals. The paper examines AI/ML-based anomaly and intrusion detection across network, mobile, and IoT security, including resilience and integration challenges.

  • Scope: The paper focuses on securing communication networks, mobile devices, IoT, and related cyber-physical digital ecosystems.These technologies underpin smart infrastructures, smart cities, smart manufacturing, and other digital-industry applications.
  • Security motivation: Cybercrime exploits the expanding attack surface of increasingly digital and interconnected infrastructures, threatening corporations, individuals, critical infrastructures, and supply chains.The paper characterizes cybersecurity as a continuing contest between evolving attacks and defenses.
  • Security properties: AI-based cybersecurity systems are discussed in terms of robustness, adaptive response, and resilience, with threat identification serving as a key element of resilient defense.The paper also notes that machine-learning classifiers can be vulnerable to adversarial attacks that mislead detection.
  • Research focus: Its research analyzes the resilience of AI-enabled anomaly-based intrusion detection systems and their integration into complex IT infrastructures.The stated objective includes understanding integration challenges and contributing insights for addressing them.
  • AI-enabled detection: AI/ML-based detection is studied as a way to identify anomalies, malicious behavior, and potentially novel or zero-day attacks beyond signature matching.Signature-based systems struggle with new and concealed attacks, whereas anomaly-based methods identify deviations from normal behavior.
  • Detection architectures: Network-based and host-based intrusion detection address complementary evidence: network traffic is monitored at the entrance, while host systems capture malicious activity that bypasses network defenses.The paper’s scenarios cover both system perspectives.

2 Methods and Materials

The paper combines literature review, quantitative experiments, and conceptual analysis to study AI/ML-based cyber threat detection and its integration into complex IT infrastructures. Experiments compare classifiers and a stacked ensemble across network, mobile, and IoT security datasets.

  • Research design: The study uses literature review, quantitative experiments, and conceptual analysis to examine AI/ML in cybersecurity and integration challenges.The literature review covers research papers, industry and white papers, and government sources; experimental findings inform the integration analysis.
  • Experimental study: Experiments cover network security, mobile security, and IoT security using three publicly available cybersecurity datasets.The datasets include varied attack types, and their public availability supports reproducibility.
  • Integration analysis: The integration discussion combines experimental, literature, and conceptual findings to suggest practical solutions and motivate future research.Industrial Information Integration Engineering provides the conceptual foundation for addressing information-integration challenges.
  • Models and ensemble: The evaluated models are Logistic Regression, Random Forest, Gradient Boosting Machine, and Deep Learning, combined through stacking into a super learner.The super learner feeds base-classifier predictions into a meta learner to produce the final ensemble prediction.
  • Models and ensemble: For the tabular datasets, the deep-learning model uses a multi-layer feedforward artificial neural network.The experiments use ReLU activation in hidden layers, with training based on stochastic gradient descent and backpropagation.

3.1 Technologies and Security

Modern networks, mobile devices, and IoT systems expand connectivity while increasing exposure to cyber threats. The section describes attack surfaces and attack types spanning network intrusion, malware, and IoT security.

  • Network security: Networks enable extensive device and service connectivity, but exchanged packets create targets for sniffing, man-in-the-middle, and distributed denial-of-service attacks.Firewalls restrict access through policies, while VPNs establish encrypted tunnels over the internet.
  • Mobile security: Mobile-device use for corporate access is growing while the attack surface expands to malware, phishing, network, supply-chain, and password attacks.The section links increased mobile connectivity with greater exposure to cyber threats.
  • IoT security: IoT security combines network, wireless, and mobile-system security, while weak device security can create additional attack vectors.Compromised smartphones may control authenticated IoT devices, and IoT devices may provide entry points for lateral attacks.
  • IoT security: IoT interconnectivity increases the attack surface and can connect cyber compromise to harmful actions in the physical world.The section cites attacks on critical infrastructure and cyber-physical systems as examples of this risk.
  • Attack types and malware: The IoT attack categories described include DDoS, information gathering, man-in-the-middle, injection, and malware attacks.Malware is presented as a major component of cyberattacks that can violate confidentiality, integrity, and availability.

3.2 Digital Ecosystems

Digital ecosystems integrate technologies across cyber-physical, enterprise, and industrial contexts. The section presents Industrial Information Integration Engineering as a foundation for interoperability, information integration, and robust industrial information systems.

  • Industrial transformation: Industry 4.0 and 5.0 integrate technologies including IoT, cyber-physical systems, AI, cloud or edge computing, modern mobile networks, blockchain, and quantum computing.Industry 5.0 is described as an extension that includes Industry 4.0 concepts.
  • Digital ecosystems: Digital transformation depends on integrating technologies and enabling interoperability across complex digital ecosystems.The section connects this integration with Industry 4.0 and Industry 5.0.
  • Industrial Information Integration Engineering: Industrial Information Integration Engineering combines systems engineering, data science, computer science, and industrial engineering to develop robust industrial information systems.It focuses on integrating and managing systems and data in industrial contexts.
  • Integration foundations: Its foundational building blocks include enterprise architecture, enterprise application integration, service-oriented architecture, business process management, and information integration and interoperability.These approaches align IT with business objectives, connect applications, integrate platforms and legacy systems, and streamline workflows.
  • Integration foundations: Vertical, horizontal, and end-to-end digital integration aim to reduce data silos and enable information sharing across systems.The section presents information integration as a core competence of digital transformation.

4 Numerical Results

The experiments compare base classifiers and two super learners across network intrusion, Android malware, and IoT threat detection. Gradient boosting generally delivers the strongest results, while deep learning is strongest among super learners for Android malware detection.

  • Evaluation setup: The experiments compare two super learners against logistic regression, random forest, gradient boosting, and deep learning using AUC, accuracy, and F-score.Accuracy and F-scores are reported at a 0.5 threshold.
  • Network intrusion detection: Gradient boosting achieves the best network intrusion performance, with AUC, accuracy, and F-score values of 1.000, 0.9966, and 0.9966.Random forest follows with an AUC of 0.9999, while the ensemble models outperform deep learning on this dataset.
  • Scenario comparisons: The figures present ROC curves and evaluation metrics for deep-learning and gradient-boosting super learners in the three detection scenarios.The tasks are to classify network traffic as usual or attack, Android applications as malicious or benign, and IoT actions as malicious or benign.
  • Android malware detection: Gradient boosting also leads Android malware detection, with AUC, accuracy, and F-score values of 1.000, 0.9966, and 0.9966.Both super learners outperform the base classifiers across all three measures, and the deep-learning super learner is strongest across those measures in this scenario.
  • IoT cyber threat detection: Gradient boosting leads IoT threat detection with AUC, accuracy, and F-score values of 0.9990, 0.9856, and 0.9859.Random forest ranks second with AUC, accuracy, and F-score values of 0.9975, 0.9876, and 0.9878.

5 Discussion

The discussion frames AI/ML threat detection as necessary for protecting digital industries and smart infrastructures. It emphasizes that selecting resilient models is important, but integrating them into complex enterprise ecosystems is a larger challenge.

  • Scope: The paper analyzes AI/ML-based intrusion detection across network, mobile, and IoT/CPS security scenarios.These scenarios cover major building blocks of contemporary digital ecosystems.
  • Integration challenge: Model resilience and robustness matter, but deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems poses a bigger challenge.The integration problem concerns complex digital ecosystems and existing IT infrastructures.

5.1 Model choice, prediction accuracy, and resilience

Across the tested scenarios, the results do not identify a single universally best AI/ML model. Gradient boosting generally performs strongest, while deep learning is the strongest super learner for Android malware detection.

  • Model choice: The experiments ask which AI/ML model best detects anomaly-based intrusions and cyber-attacks across network, mobile, and IoT settings.The discussion concludes that model performance depends on the tested scenario rather than yielding one clear go-to model.
  • Prediction accuracy: Gradient boosting achieves the highest tested accuracy across network security, Android malware, and IoT security scenarios.Random forest and deep learning also provide strong performance on the tested metrics.
  • Resilience: The study reports that accuracy differences between deep learning and ensemble models are not necessarily significant, and prior literature similarly finds no clear go-to model.Attack detection effectiveness also depends on the assumed context and attack models.
  • Super learners: Gradient boosting is the strongest super-learner base choice except for Android malware, where deep learning is the strongest meta-learner.The experiments cover network intrusion, Android malware, and IoT cyber threat detection.
  • Classifier composition: Adding weaker classifiers can dilute super-learner prediction accuracy, so logistic regression was dropped because it had the weakest performance and added no value.The super learners used random forest, gradient boosting, and deep learning as base classifiers.

5.2 Integration challenges of AI-enabled security solutions

Integrating AI-enabled cybersecurity into complex digital ecosystems requires addressing process, data, application, and infrastructure challenges. The discussion highlights changing network conditions, device heterogeneity, privacy, resource constraints, and the need for integration principles such as IIIE and zero trust.

  • Enterprise integration: Enterprise integration challenges span process, data, application, and infrastructure dimensions, with Industrial Information Integration Engineering proposed as a way to address them.The paper links overcoming these challenges with more seamless adoption of cyber threat detection solutions.
  • Network security: Network-security integration must handle evolving traffic patterns, blurred internal and external boundaries, large real-time data volumes, and rapidly changing threats.Cloud adoption and remote work contribute to the changing network environment.
  • Zero trust: Zero-trust security supports decentralized ecosystems through granular access control and network segmentation rather than automatic trust inside or outside a perimeter.The paper connects this approach with cloud-based and remote-work environments.
  • Mobile security: Mobile-security integration is complicated by diverse devices, operating systems, and applications, intermittent connectivity, privacy concerns, and the need to balance security with user experience.The distributed nature of mobile devices increases the need for host-based security solutions.
  • IoT/CPS security: IoT/CPS solutions must be lightweight enough for resource-constrained devices while remaining capable of detecting complex threats and protecting data and physical safety.Data privacy and reliability are also significant concerns because breaches can affect real-world systems such as healthcare.
  • Model deployment: The experiments indicate that deep learning is not necessarily best for IoT, because faster models requiring less CPU power can match or outperform it.The discussion also reports adequate attack-prediction accuracy from simpler models, including single-layer neural networks.

5.3 Towards integrated, secure, and resilient digital ecosystems

The section presents Industrial Information Integration Engineering (IIIE) and structured ML pipelines as approaches for integrating AI-enabled cybersecurity into complex enterprise environments. It also identifies adversarial attacks, dataset scope, and deployment challenges that motivate future research.

  • Integration architecture: Data quality and availability across the enterprise are important for model performance and can be supported through consistent ML-pipeline processes.Validation processes may also help reduce false positives and negatives and address bias through representative data and correction mechanisms.
  • Deployment: Cloud-based solutions, distributed processing, and enterprise-architecture integration can enable high-volume data handling and real-time analysis for prompt threat response.The section also associates modular designs, containerization, and regular system checks with maintenance and updates.
  • Limitations and future research: Capable adversaries may use zero-day attacks, obfuscation, concealment, and adversarial techniques to circumvent AI-enabled security solutions.The text notes that AI algorithms are not guaranteed to capture threats and may misclassify normal behavior as malicious.
  • Integration architecture: IIIE and a well-structured ML pipeline can support integrating AI-enabled cybersecurity models into complex enterprise systems and IT infrastructures.The pipeline covers data preprocessing, training, evaluation, and deployment, while IIIE improves interoperability and interaction with enterprise components.
  • Limitations and future research: Future work should strengthen robustness, response, resilience, classification power, and reliable deployment and integration of AI-based cybersecurity systems.The section connects these directions with protecting increasingly connected digital ecosystems and human-centered AI research.
  • Limitations and future research: The study’s focus on network, mobile, and industrial-IoT security leaves additional datasets, endpoint protection, home networks, and CPS or IoT devices as future research directions.The section also proposes broader model families, fusion methods, and more comprehensive security solutions for real-world enterprise and government use.

6 Conclusion

The conclusion frames increasing interconnectedness as expanding the cyberattack surface and emphasizes AI-based intrusion and malware detection for protecting digital ecosystems. It reports applicability across network, mobile, and IoT security, while positioning IIIE as helpful for enterprise integration.

  • Conclusion: Increasing interconnectedness and the convergence of digital and physical systems have expanded the cybersecurity attack surface.The conclusion links this environment to distributed smart devices and growing demand for intelligent threat-detection systems.
  • Conclusion: The paper explores AI-based intrusion and malware detection to protect modern digital ecosystems from cyberattacks.Its reported security contexts are network security, mobile security, and IoT security.
  • Conclusion: The paper reports that machine-learning applications can enhance prediction accuracy and increase the resilience of AI-based defense systems across network, mobile, and IoT security.This is stated as the paper’s conclusion rather than as a single dataset-specific numerical result.
  • Conclusion: Industrial Information Integration Engineering could help deploy and integrate AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures.The conclusion relates this application to business digitalization, critical-infrastructure protection, and government security.
  • Conclusion: The conclusion identifies cybersecurity as an ongoing contest with organized, adaptive adversaries and calls for continued preparation and intelligent protection.It presents automated intelligent systems as a means of shielding connected environments against complex cyberattacks.
Loading 2401.01342v1…