Therefore, this paper showcases how different autoencoder-based architectures can spot the presence of malicious communications hidden in conversations, especially in the TTL of IPv4 traffic. To conduct tests, this work considers IoT traffic traces gathered in a real setting and the presence of an ...
All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (...
All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (...
The first challenge relates to the analysis of the network traffic. An increasing amount of traffic is encrypted, which is a beneficial development for the security of the users, but limits the collection of interesting traces to those transmitted by the less secure IoT devices. Additionally, IoT...
For example, here is a long string that we can say to Alexa Echo Dot/Google Home while sniffing their traffic. Pay attention if the device is transmitting data before the wake word. It is a dark and stormy night. My friends and I just came back from the Yosemite National Park, where ...
Besides, more and more IoT devices currently use encrypted traffic, which also makes traffic-based security detection more difficult. The firmware virtualization technology simulates the operating environment of the embedded system based on the firmware image, realizes large-scale and automated dynamic ...
and other rapid detection analysis algorithms like device behavior traces, traffic anomalies, and packet analysis. The IoT platform also needs to be able to quickly diagnose and respond to device behavior according to the application scenario and specific situation based on device behavior detection res...
We then analyze the traffic traces to characterize statistical attributes such as data rates and burstiness, activity cycles, and signalling patterns, for over 20 IoT devices deployed in our environment. Finally, using these attributes, we develop a classification method that can not only distinguish...
14 Botnet datasetFor IoT (BoT-IoT) [56] Dos, DDoS, key logging,Reconnaissance Network-based, real traffic labeled data with IoT traces, zero-day attacks 15 UNIBS [57] DoS Contain labels for application protocol only, attack scenarios focus on DoS 16 Aegean Wi-Fi Intrusion Dataset (AWID) ...
Processes IPv4 traffic between MAP nodes that are in two different MAP domains. Each FMR rule has IPv4 Prefix, IPv4 Prefix Length and EA Bits Length. You can define up to 10 FMR Profiles. FMR settings are pushed to the device as a part of MAP-T Settin...