Browsing by Author "Baykas,T."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation Count: 0
    IEEE 802.11BB Reference Channel Models For Light Communications
    (Institute of Electrical and Electronics Engineers Inc., 2023) Baykaş, Tunçer; Baykas,T.; Elamassie,M.; Uysal,M.
    Increasing industrial attention to visible light communications (VLC) technology led the IEEE 802.11 to establish the task group 802.11bb 'Light Communications' (LC) for the development of a VLC standard. As a part of the standard development process, the development of realistic channel models according to possible use cases is of critical importance for physical layer system design. This article presents the reference channel models for the mandatory usage models adopted by IEEE 802.11bb for the evaluation of system proposals. The use cases include industrial, medical, enterprise, and residential scenarios. Channel impulse responses and corresponding frequency responses are obtained for each use case using a ray tracing approach based on realistic specifications for transmitters and receivers, and optical characterization of the environment. © 2023 IEEE.
  • Thumbnail Image
    Conference Object
    Citation Count: 2
    The Use of Statistical Features for Low-Rate Denial of Service Attack Detection
    (Institute of Electrical and Electronics Engineers Inc., 2023) Baykaş, Tunçer; Baykas,T.; Anarim,E.
    Low-rate Denial of Service (LDoS) attacks can significantly reduce the serving capabilities of networks. These attacks involve sending periodic high-intensity pulse data flows, and their harmful effects are like those of traditional DoS attacks. However, LDoS attacks have different attack modes, which make them particularly challenging to detect. The high level of concealment associated with LDoS attacks makes it extremely difficult for traditional DoS detection methods to identify them. This paper explores the potential of using statistical features for LDoS attack detection. The results demonstrate that statistical features can offer promising performance in detecting these types of attacks. Furthermore, through the application of RFE and SHAP analysis, we find that entropy and L-moment-based features play a crucial role in detection. These findings provide important insights into the use of statistical features for network security, which can help to enhance the overall resilience of networks against various types of attacks. © 2023 IEEE.