1,171 research outputs found
WiFi Epidemiology: Can Your Neighbors' Router Make Yours Sick?
In densely populated urban areas WiFi routers form a tightly interconnected
proximity network that can be exploited as a substrate for the spreading of
malware able to launch massive fraudulent attack and affect entire urban areas
WiFi networks. In this paper we consider several scenarios for the deployment
of malware that spreads solely over the wireless channel of major urban areas
in the US. We develop an epidemiological model that takes into consideration
prevalent security flaws on these routers. The spread of such a contagion is
simulated on real-world data for geo-referenced wireless routers. We uncover a
major weakness of WiFi networks in that most of the simulated scenarios show
tens of thousands of routers infected in as little time as two weeks, with the
majority of the infections occurring in the first 24 to 48 hours. We indicate
possible containment and prevention measure to limit the eventual harm of such
an attack.Comment: 22 pages, 1 table, 4 figure
Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats
Network steganography is the art of hiding secret information within innocent
network transmissions. Recent findings indicate that novel malware is
increasingly using network steganography. Similarly, other malicious activities
can profit from network steganography, such as data leakage or the exchange of
pedophile data. This paper provides an introduction to network steganography
and highlights its potential application for harmful purposes. We discuss the
issues related to countering network steganography in practice and provide an
outlook on further research directions and problems.Comment: 11 page
Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems
Many new devices, such as phones and tablets as well as traditional computer systems, rely on wireless connections to the Internet and are susceptible to attacks. Two important types of attacks are the use of malware and exploiting Internet protocol vulnerabilities in devices and network systems. These attacks form a threat on many levels and therefore any approach to dealing with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). This work also includes two prevention mechanisms for DoS attacks, namely a one- time password (OTP) and through the use of machine learning. Furthermore, we focus on an exploit of the widely used IEEE 802.11 protocol for wireless local area networks (WLANs). The work proposed here presents a threefold approach for intrusion detection to remedy the effects of malware and an Internet protocol exploit employing machine learning as a primary tool. We conclude with a comparison of dimensionality reduction methods to a deep learning classifier to demonstrate the effectiveness of these methods without compromising the accuracy of classification
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
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