2,526 research outputs found
IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
With the rapid growth of the Internet-of-Things (IoT), concerns about the
security of IoT devices have become prominent. Several vendors are producing
IP-connected devices for home and small office networks that often suffer from
flawed security designs and implementations. They also tend to lack mechanisms
for firmware updates or patches that can help eliminate security
vulnerabilities. Securing networks where the presence of such vulnerable
devices is given, requires a brownfield approach: applying necessary protection
measures within the network so that potentially vulnerable devices can coexist
without endangering the security of other devices in the same network. In this
paper, we present IOT SENTINEL, a system capable of automatically identifying
the types of devices being connected to an IoT network and enabling enforcement
of rules for constraining the communications of vulnerable devices so as to
minimize damage resulting from their compromise. We show that IOT SENTINEL is
effective in identifying device types and has minimal performance overhead
SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
This paper presents the development of a Supervisory Control and Data
Acquisition (SCADA) system testbed used for cybersecurity research. The testbed
consists of a water storage tank's control system, which is a stage in the
process of water treatment and distribution. Sophisticated cyber-attacks were
conducted against the testbed. During the attacks, the network traffic was
captured, and features were extracted from the traffic to build a dataset for
training and testing different machine learning algorithms. Five traditional
machine learning algorithms were trained to detect the attacks: Random Forest,
Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained
machine learning models were built and deployed in the network, where new tests
were made using online network traffic. The performance obtained during the
training and testing of the machine learning models was compared to the
performance obtained during the online deployment of these models in the
network. The results show the efficiency of the machine learning models in
detecting the attacks in real time. The testbed provides a good understanding
of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin
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