06-19, 11:45–12:45 (America/New_York), Great Falls
The Internet of Things (IoT) has revolutionized the way we live and work, but it has also created significant challenges for network security and asset management. Most businesses have a blind spot when it comes to IoT devices, which creates an opportunity for attackers. Lacking sufficient visibility and control, these devices provide an easy and inconspicuous way for attackers to infiltrate a network.
With a vast array of devices, identifying what devices are running in the network has become a critical issue for organizations. Software agents have been the standard way to collect this information, but for embedded and IoT devices, it’s not always possible to install them. An effective solution to this problem lies in passive fingerprinting, which involves matching uniquely identifying patterns in the host’s network traffic and classifying it accordingly.
The Internet of Things (IoT) has revolutionized the way we live and work, but it has also created significant challenges for network security and asset management. Most businesses have a blind spot when it comes to IoT devices, which creates an opportunity for attackers. Lacking sufficient visibility and control, these devices provide an easy and inconspicuous way for attackers to infiltrate a network.
With a vast array of devices, identifying what devices are running in the network has become a critical issue for organizations. Software agents have been the standard way to collect this information, but for embedded and IoT devices, it’s not always possible to install them. An effective solution to this problem lies in passive fingerprinting, which involves matching uniquely identifying patterns in the host’s network traffic and classifying it accordingly.
In this talk, we will explore how various protocols from different layers of the OSI model can be used to provide information about the device and its operating system. Attendees can expect to gain valuable insights into the practical application of this technique and how it can enhance their organization’s security posture.
Asaf Fried is a data scientist for Cato Networks and previously held the same role at Check Point Software. He earned a MS degree from Ben-Gurion University of the Negev with his thesis “Facing Airborne Attacks on ADS-B Data with Autoencoders” and received a Bachelors degree in computer science from Reichman University.