Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 2, Pages 860-870Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2974520
Keywords
Computer crime; 5G mobile communication; Computer architecture; Cellular networks; Microprocessors; Performance evaluation; Unsolicited electronic mail; 5G; artificial intelligence (AI); call detail record (CDR); convolutional neural networks (CNNs); cyber-physical system (CPS); cybersecurity; DDoS attack; deep learning (DL)
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Funding
- National Natural Science Foundation of China [61941119]
- ZTE Industry-Academic-Research Cooperation Funds
- Fundamental Research Funds for the Central Universities, China
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The article proposes a comprehensive framework utilizing deep convolutional neural networks and real network data for early detection of DDoS attacks orchestrated by botnets, achieving over 91% accuracy in detecting normal and under attack cells.
With the advent of 5G, cyber-physical systems (CPSs) employed in the vertical industries and critical infrastructures will depend on the cellular network more than ever; making their attack surface wider. Hence, guarding the network against cyberattacks is critical not only for its primary subscribers but to prevent it from being exploited as a proxy to attack CPSs. In this article, we propose a consolidated framework, by utilizing deep convolutional neural networks (CNNs) and real network data, to provide early detection for distributed denial-of-service (DDoS) attacks orchestrated by a botnet that controls malicious devices. These puppet devices individually perform silent call, signaling, SMS spamming, or a blend of these attacks targeting call, Internet, SMS, or a blend of these services, respectively, to cause a collective DDoS attack in a cell that can disrupt CPSs' operations. Our results demonstrate that our framework can achieve higher than 91% normal and underattack cell detection accuracy.
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