Detecting Spoofing Attacks in Mobile Wireless Environments

Yang, Jie; Chen, Yingying; Trappe, Wade

doi:10.1109/sahcn.2009.5168926

Cited by 44 publications

(20 citation statements)

References 20 publications

(34 reference statements)

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“…Data are gathered through a grid of multiple sensors and then processed to fingerprint stations and identify them. A similar approach is taken in [4,3,15,14], where the probability density fuction of the signal power is used to identify and locate single stations in a multiple sensors environment. The last two approaches are indeed innovative but heavier from a computational point of view.…”

Section: Wireless Signal Characterization and Related Workmentioning

confidence: 99%

“…Meanwhile WiFi technology has developed to include innovations, such as per-device multiple antennas, that make some of these methods obsolete and useless in practice [11]. The problem has been approached from two different angles, one based on mechanisms or techniques that operate at a logic level [12,9] and one relying on features related to the physical medium, like signal power or Round Trip Time (RTT) [7,8,6,14,4,3,15], which are unspoofable (or nearly impossible to spoof). Signal power analysis is therefore a natural choice for a method to detect MAC-spoof.…”

Section: Introductionmentioning

confidence: 99%

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Lightweight MAC-spoof detection exploiting received signal power and median filtering

Papini¹

2012

IJCCBS

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Abstract. IEEE 802.11 networks are subject to MAC-spoof attacks. An attacker can easily steal the identity of a legitimate station, even Access Points, thus enabling him to take full control over network basic mechanisms or even access restricted resources. In this paper we propose a method to detect this kind of attack based on signal power monitoring. The main contribution of our work is the introduction of a median filter that enables the detection of the attack by looking at the variance of the signal power. We take into account two types of references for the samples, time and number of frames, and compare their detection capabilities. Our experimental results show that the spoofing attack is successfully detected with both type of references. Moreover the median filter helps to reject false positives.

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Section: Wireless Signal Characterization and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lightweight MAC-spoof detection exploiting received signal power and median filtering

Papini¹

2012

IJCCBS

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“…However, in the mobile ad hoc network, there is no need for an adversary to gain the physical access to visit the network: once the adversary is in the radio range of any other nodes in the mobile ad hoc network, it can communicate with those nodes in its radio range and thus freedom to join, leave and move inside the network automatically. Thus, the mobile ad hoc network does not provide secure boundary to protect the network from some potentially dangerous network accesses [8], [9].…”

Section: Lack Of Secure Boundariesmentioning

confidence: 99%

“…And node should include the 2-hop neighbors in the hello message; this gives every node a 3-hop topology of the network. But it is defeated by spoofing outside of 3-hops [8].…”

Section: Spoofing Attackmentioning

confidence: 99%

An Assessment of Frequently Adopted Unsecure Patterns in Mobile Ad hoc Network: Requirement and Security Management Perspective

Singh¹,

Singh²,

Shree³

2011

IJCA

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A mobile Ad hoc network (MANET) has played an imperative role in making very fast connection of mobile devices without including any centralize equipment. It is the simplest way to transfer files between two or more devices that can also act as routers. Now a day, the appropriate uses of this modern wireless communication are in emergency rescue situation, military scenarios, sensor networks, conferences and many others. Mobile Ad hoc Networks are adopted when wired networks are malfunctioning or broken down due to some unavoidable situation. This rapidly deployed network collaborates without using any preexisting fixed network infrastructure. Due to rapid deployment of mobile nodes or frequent change in network topology, security is the most important concern in Mobile Ad hoc Network. Due to its limited physical security, energy constrained operations and lack of centralized administration; Ad hoc Networks are more vulnerable to attacks than a wired networks or traditional networks. With the proliferation of cheaper, small, and more powerful mobile devices, mobile ad hoc networks (MANETs) have become one of the fastest growing areas of research. In this paper we are attempting to analyze the security attacks in Ad-hoc environment and focusing on various areas of security requirement, different types of active and passive attacks in Ad-hoc networks. General TermsMobile Ad Hoc Network, Security, Secure Routing in MANETs.

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“…Choosing a group of algorithms employing RSS to perform the task of localizing multiple attackers and evaluate their performance in terms of localization accuracy. J.Yang et al, [13] proposed a technique DEtecting Mobile Spoofing aTtacks in wireless Environments (DEMOTE). They develop the DEMOTE system, which exploits Received Signal Strength (RSS) traces collected over time and achieves an optimal threshold to partition the RSS traces into classes for attack detection.…”

Section: Introductionmentioning

confidence: 99%

Spoofing Attacks Detection and Localizing Multiple Adversaries in Wireless Networks

D.Sontakke¹

2015

IJIRCCE

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ABSTRACT:Wireless networks provide various advantages in real world. This can help businesses to increase their productivity, lower cost and effectiveness, increase scalability and improve relationship with business partners and attract customers. Communication in wireless network is critical and challenging issue. Wireless spoofing attacks are occurs easily and reduce the networks performance. Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. The flexibility and openness of wireless networks enables an adversary to masquerade as other devices easily. The traditional approach to detect spoofing attacks is to apply cryptographic authentication. Here using spatial information, a physical property of each node, so hard to falsify and not depend on cryptographic security, on the beginning for (1) detecting spoofing attacks; (2) determining the number of attackers when multiple node pretend as a same node identity, and (3) localizing multiple adversaries. Here using the correlation between a signal's spatial direction and the average received signal gain of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. Then the problem of determining the number of attackers as multiclass detection problem is formulated. Cluster-based mechanisms are developed to determine the number of attackers. When the training data is available, Support Vector Machines (SVM) method is used to further improve the accuracy of determining the number of attackers. The approach can both detects the presence of attacks as well as determine the number of adversaries, spoofing the same node identity, also that can localize any number of attackers and eliminate them.

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Detecting Spoofing Attacks in Mobile Wireless Environments

Cited by 44 publications

References 20 publications

Lightweight MAC-spoof detection exploiting received signal power and median filtering

Lightweight MAC-spoof detection exploiting received signal power and median filtering

An Assessment of Frequently Adopted Unsecure Patterns in Mobile Ad hoc Network: Requirement and Security Management Perspective

Spoofing Attacks Detection and Localizing Multiple Adversaries in Wireless Networks

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