Biblio
Filters: Author is Acosta, Jaime C. [Clear All Filters]
Enabling Device Trustworthiness for SDN-Enabled Internet -of- Battlefield Things. 2022 IEEE Conference on Dependable and Secure Computing (DSC). :1—7.
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2022. Military networks consist of heterogeneous devices that provide soldiers with real-time terrain and mission intel-ligence. The development of next-generation Software Defined Networks (SDN)-enabled devices is enabling the modernization of traditional military networks. Commonly, traditional military networks take the trustworthiness of devices for granted. How-ever, the recent modernization of military networks introduces cyber attacks such as data and identity spoofing attacks. Hence, it is crucial to ensure the trustworthiness of network traffic to ensure the mission's outcome. This work proposes a Continuous Behavior-based Authentication (CBA) protocol that integrates network traffic analysis techniques to provide robust and efficient network management flow by separating data and control planes in SDN-enabled military networks. The evaluation of the CBA protocol aimed to measure the efficiency of the proposed protocol in realistic military networks. Furthermore, we analyze the overall network overhead of the CBA protocol and its accuracy to detect rogue network traffic data from field devices.
Deep Learning for Cyber Deception in Wireless Networks. 2021 17th International Conference on Mobility, Sensing and Networking (MSN). :551–558.
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2021. Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.