438 research outputs found

    IEEE 802.11 user fingerprinting and its applications for intrusion detection

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    AbstractEasy associations with wireless access points (APs) give users temporal and quick access to the Internet. It needs only a few seconds to take their machines to hotspots and do a little configuration in order to have Internet access. However, this portability becomes a double-edged sword for ignorant network users. Network protocol analyzers are typically developed for network performance analysis. Nonetheless, they can also be used to reveal user’s privacy by classifying network traffic. Some characteristics in IEEE 802.11 traffic particularly help identify users. Like actual human fingerprints, there are also unique traffic characteristics for each network user. They are called network user fingerprints, by tracking which more than half of network users can be connected to their traffic even with medium access control (MAC) layer pseudonyms. On the other hand, the concept of network user fingerprint is likely to be a powerful tool for intrusion detection and computer/digital forensics. As with actual criminal investigations, comparison of sampling data to training data may increase confidence in criminal specification. This article focuses on a survey on a user fingerprinting technique of IEEE 802.11 wireless LAN traffic. We also summarize some of the researches on IEEE 802.11 network characteristic analysis to figure out rogue APs and MAC protocol misbehaviors

    IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT

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    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

    A New MAC Address Spoofing Detection Technique Based on Random Forests

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    Media access control (MAC) addresses in wireless networks can be trivially spoofed using off-the-shelf devices. The aim of this research is to detect MAC address spoofing in wireless networks using a hard-to-spoof measurement that is correlated to the location of the wireless device, namely the received signal strength (RSS). We developed a passive solution that does not require modification for standards or protocols. The solution was tested in a live test-bed (i.e., a wireless local area network with the aid of two air monitors acting as sensors) and achieved 99.77%, 93.16% and 88.38% accuracy when the attacker is 8–13 m, 4–8 m and less than 4 m away from the victim device, respectively. We implemented three previous methods on the same test-bed and found that our solution outperforms existing solutions. Our solution is based on an ensemble method known as random forests.https://doi.org/10.3390/s1603028

    IEEE 802.11 i Security and Vulnerabilities

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    Despite using a variety of comprehensive preventive security measures, the Robust Secure Networks (RSNs) remain vulnerable to a number of attacks. Failure of preventive measures to address all RSN vulnerabilities dictates the need for enhancing the performance of Wireless Intrusion Detection Systems (WIDSs) to detect all attacks on RSNs with less false positive and false negative rates

    Speaking the Local Dialect: Exploiting differences between IEEE 802.15.4 Receivers with Commodity Radios for fingerprinting, targeted attacks, and WIDS evasion

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    Producing IEEE 802.15.4 PHY-frames reliably accepted by some digital radio receivers, but rejected by others---depending on the receiver chip\u27s make and model---has strong implications for wireless security. Attackers could target specific receivers by crafting shaped charges, attack frames that appear valid to the intended target and are ignored by all other recipients. By transmitting in the unique, slightly non-compliant dialect of the intended receivers, attackers would be able to create entire communication streams invisible to others, including wireless intrusion detection and prevention systems (WIDS/WIPS). These scenarios are no longer theoretic. We present methods of producing such IEEE 802.15.4 frames with commodity digital radio chips widely used in building inexpensive 802.15.4-conformant devices. Typically, PHY-layer fingerprinting requires software-defined radios that cost orders of magnitude more than the chips they fingerprint; however, our methods do not require a software-defined radio and use the same inexpensive chips. Knowledge of such differences, and the ability to fingerprint them is crucial for defenders. We investigate new methods of fingerprinting IEEE 802.15.4 devices by exploring techniques to differentiate between multiple 802.15.4-conformant radio-hardware manufacturers and firmware distributions. Further, we point out the implications of these results for WIDS, both with respect to WIDS evasion techniques and countering such evasion

    Intrusion detection and monitoring for wireless networks.

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