Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

IoT/Embedded Security

// // //
4/27/2018
08:05 AM
Larry Loeb
Larry Loeb
Larry Loeb

Researchers Detail Self-Learning System That Secures IoT Devices

Researchers from several universities have published a new paper describing what they believe is a better way to protect and secure IoT devices and sensors.

The proliferation of Internet of Things devices seems to be unstoppable. However, along with the increase in the number of devices out there comes the security risk that these sensors and connected things can pose when they are compromised by an attacker.

In addition, many security tools don't adapt well to IoT. (See Increased IoT Use Causing Added Enterprise Security Concerns Report.)

These tools have historically been based on assumptions about the protocols that are used in the network connections and the device states that exist when they attach to the network. For instance, IoT devices are too variable in these areas to fit into neat categories.

Now, however, researchers have come up with a self-learning system aimed at detecting compromised IoT devices, which does not require any prior knowledge about the device types or require pre-programmed training data to operate.

(Source: iStock)
(Source: iStock)

Here's how they describe it:

We propose a novel approach that combines automated device-type identification and subsequent device-type-specific anomaly detection by making use of machine learning techniques. Using this approach, we demonstrate that we can effectively and quickly detect compromised IoT devices with little false alarms. [It] is completely autonomous and can be trained in a distributed crowdsourced manner without requiring human intervention or labeled training data.

Sounds too good to be true, but the researchers say that they have it.

Giving it the name of DÏOT, the system has two main components.

The first is the "Security Gateway" and the second is the "IoT Security Service." Together, these two components detect compromised IoT devices by monitoring their communication as observed by the Security Gateway, which acts as a network gateway for the local network.

The security service also has cloud-based functionality, which has two main components: Device-Type Identification and Anomaly Detection Model.

The security service trains the gateway by using fingerprints that are generated at several Security Gateways to learn the specific device-type identification models that are attached to the network. The aggregating maximizes the usage of limited information obtained from scarce communications at each gateway.

The Anomaly Detection Model maintains a repository of device-type-specific anomaly detection models which are matched to the signatures gathered.

Once a model is chosen, the system looks at the current traffic pattern to see if it matches the normal pattern expected. This is done through the use of neural network techniques. If it doesn’t matchup, an anomaly alert is generated.


The fundamentals of network security are being redefined -- don't get left in the dark by a DDoS attack! Join us in Austin from May 14-16 at the fifth-annual Big Communications Event. There's still time to register and communications service providers get in free!

The system was tested on a lab network that had several devices, such as appliances, smart lamps, cameras, and routers connected to it by WiFi. The network was allowed to connect for 24 hours before it was tested. It turned out that effective device identification for 33 different IoT devices required only a few hours of traffic monitoring.

Various attacks were then implemented, most based on the Mirai botnet. (See IoT Malware-on-the-Fly Expected to Rise .)

Researchers found that most aggressive distributed denial of service (DDoS) attacks were detected in one millisecond with 100% accuracy. Overall, it detected 96% of attacks in less than 0.03 seconds with a low false alarm rate of 1%.

This kind of system has some potential huge benefits.

Mostly, it is automated and effective and so can serve as a protective barrier that can be widely implemented. Further development with this technique may help to rid the Internet of the dangers of malicious IoT devices and the bots that go with them.

Related posts:

— Larry Loeb has written for many of the last century's major "dead tree" computer magazines, having been, among other things, a consulting editor for BYTE magazine and senior editor for the launch of WebWeek.

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
Edge-DRsplash-10-edge-articles
I Smell a RAT! New Cybersecurity Threats for the Crypto Industry
David Trepp, Partner, IT Assurance with accounting and advisory firm BPM LLP,  7/9/2021
News
Attacks on Kaseya Servers Led to Ransomware in Less Than 2 Hours
Robert Lemos, Contributing Writer,  7/7/2021
Commentary
It's in the Game (but It Shouldn't Be)
Tal Memran, Cybersecurity Expert, CYE,  7/9/2021
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
Developing and Testing an Effective Breach Response Plan
Whether or not a data breach is a disaster for the organization depends on the security team's response and that is based on how the team developed a breach response plan beforehand and if it was thoroughly tested. Inside this report, experts share how to: -understand the technical environment, -determine what types of incidents would trigger the plan, -know which stakeholders need to be notified and how to do so, -develop steps to contain the breach, collect evidence, and initiate recovery.
Flash Poll
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2022-23476
PUBLISHED: 2022-12-08
Nokogiri is an open source XML and HTML library for the Ruby programming language. Nokogiri `1.13.8` and `1.13.9` fail to check the return value from `xmlTextReaderExpand` in the method `Nokogiri::XML::Reader#attribute_hash`. This can lead to a null pointer exception when invalid markup is being par...
CVE-2022-23492
PUBLISHED: 2022-12-08
go-libp2p is the offical libp2p implementation in the Go programming language. Version `0.18.0` and older of go-libp2p are vulnerable to targeted resource exhaustion attacks. These attacks target libp2p’s connection, stream, peer, and memory management. An attacker can cause the a...
CVE-2022-3092
PUBLISHED: 2022-12-08
GE CIMPICITY versions 2022 and prior is vulnerable to an out-of-bounds write, which could allow an attacker to execute arbitrary code.
CVE-2022-4261
PUBLISHED: 2022-12-08
Rapid7 Nexpose versions prior to 6.6.172 failed to reliably validate the authenticity of update contents. This failure could allow an attacker to provide a malicious update and alter the functionality of Rapid7 Nexpose. The attacker would need some pre-existing mechanism to provide a malicious updat...
CVE-2022-4291
PUBLISHED: 2022-12-08
The aswjsflt.dll library from Avast Antivirus windows contained a potentially exploitable heap corruption vulnerability that could enable an attacker to bypass the sandbox of the application it was loaded into, if applicable. This issue was fixed in version 18.0.1478 of the Script Shield Component.