IEEE 802.11 i Security and Vulnerabilities

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

RSSI Based Indoor Passive Localization for Intrusion Detection and Tracking

A real time system for intrusion detection and tracking based on wireless sensor network technology is designed by using the IITH mote which is de- veloped and designed in IIT Hyderabad as the communication module in the network.This paper describes the Device-Free Passive Localization system based on RSSI.The main objective of this paper is to design a DFP Local- ization system that is easily redeployable, recon�gurable, easy to use, and operates in real time. In addition the detection of humans is to be done.The em- bedded intrusion detection algorithm is designed so that it is able to cope with the limited resources, in terms of computational power and available memory space, of the microcontroller unit (MCU) found in the nodes. and various challenges and problem faced during the real test bed deployment and also proposed solutions to overcome them.We presented an alternative algo- rithm based on the minimum Euclidean distance classi�er.our result shows that the localization accuracy of this system is increased when using the proposed algorith

A Misuse-Based Intrusion Detection System for ITU-T G.9959 Wireless Networks

Wireless Sensor Networks (WSNs) provide low-cost, low-power, and low-complexity systems tightly integrating control and communication. Protocols based on the ITU-T G.9959 recommendation specifying narrow-band sub-GHz communications have significant growth potential. The Z-Wave protocol is the most common implementation. Z-Wave developers are required to sign nondisclosure and confidentiality agreements, limiting the availability of tools to perform open source research. This work discovers vulnerabilities allowing the injection of rogue devices or hiding information in Z-Wave packets as a type of covert channel attack. Given existing vulnerabilities and exploitations, defensive countermeasures are needed. A Misuse-Based Intrusion Detection System (MBIDS) is engineered, capable of monitoring Z-Wave networks. Experiments are designed to test the detection accuracy of the system against attacks. Results from the experiments demonstrate the MBIDS accurately detects intrusions in a Z-Wave network with a mean misuse detection rate of 99%. Overall, this research contributes new Z-Wave exploitations and an MBIDS to detect rogue devices and packet injection attacks, enabling a more secure Z-Wave network

Anti-Fall: A Non-intrusive and Real-time Fall Detector Leveraging CSI from Commodity WiFi Devices

Fall is one of the major health threats and obstacles to independent living for elders, timely and reliable fall detection is crucial for mitigating the effects of falls. In this paper, leveraging the fine-grained Channel State Information (CSI) and multi-antenna setting in commodity WiFi devices, we design and implement a real-time, non-intrusive, and low-cost indoor fall detector, called Anti-Fall. For the first time, the CSI phase difference over two antennas is identified as the salient feature to reliably segment the fall and fall-like activities, both phase and amplitude information of CSI is then exploited to accurately separate the fall from other fall-like activities. Experimental results in two indoor scenarios demonstrate that Anti-Fall consistently outperforms the state-of-the-art approach WiFall, with 10% higher detection rate and 10% less false alarm rate on average.Comment: 13 pages,8 figures,corrected version, ICOST conferenc

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Sensorless sensing with WiFi

Abstract: Can WiFi signals be used for sensing purpose? The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world’s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications. Key words: Channel State Information (CSI); sensorless sensing; WiFi; indoor localization; device-free human detection; activity recognition; wireless networks; ubiquitous computing

A novel cheater and jammer detection scheme for IEEE 802.11-based wireless LANs

The proliferation of IEEE 802.11 networks has made them an easy and attractive target for malicious devices/adversaries which intend to misuse the available network. In this paper, we introduce a novel malicious entity detection method for IEEE 802.11 networks. We propose a new metric, the Beacon Access Time (BAT), which is employed in the detection process and inherits its characteristics from the fact that beacon frames are always given preference in IEEE 802.11 networks. An analytical model to define the aforementioned metric is presented and evaluated with experiments and simulations. Furthermore, we evaluate the adversary detection capabilities of our scheme by means of simulations and experiments over a real testbed. The simulation and experimental results indicate consistency and both are found to follow the trends indicated in the analytical model. Measurement results indicate that our scheme is able to correctly detect a malicious entity at a distance of, at least, 120 m. Analytical, simulation and experimental results signify the validity of our scheme and highlight the fact that our scheme is both efficient and successful in detecting an adversary (either a jammer or a cheating device). As a proof of concept, we developed an application that when deployed at the IEEE 802.11 Access Point, is able to effectively detect an adversary. (C) 2015 Elsevier B.V. All rights reserved.Postprint (author's final draft

Intrusion detection based on embedded processing of received signal strength indicator

Langattomien anturiverkkojen yhteydessä, vastaanotetun signaalin voimakkuuden indikaattoria (RSSI, received signal strength indicator) on perinteisesti käytetty langattomien anturien paikallistamiseen, etäisyyden estimointiin ja radiolinkin hyvyyden arviointiin. Viimeaikainen tutkimus on osoittanut, että RSSI:n vaiheluita voidaan käyttää myös havaitsemaan ihmisten läsnäolo langattoman anturiverkon läheisyydessä. Sen lisäksi ihmisen kulkema reitti valvotulla alueella voidaan uudelleen rakentaa antureiden keräämistä RSSI mittauksista. Tämä menettely on toimiva, mutta se vaatii kaikkien RSSI mittausten lähettämistä keskussolmulle erillistä prosessointia varten ja täten se kasvattaa anturiverkon latenssia ja energian kulutusta. Diplomityön tavoitteena on käyttää RSSI mittauksia sisätilojen valvontaa varten prosessoimalla mittaukset hajautetusti anturitasolla. Hajautetulla prosessoinnilla anturiverkon solmut kykenevät itsenäisesti havaitsemaan henkilön ja seuraamaan hänen liikkeitään. Lähettämällä keskussolmulle vain hälytykset jotka liittyvät merkittäviin tapahtumiin, järjestelmän latenssi sekä energiankulutus pystytyään minimoimaan. Lisäksi järjestelmän käyttämä tarkka aikasynkronointiprotokolla mahdollistaa solmujen pitämään radionsa suljettuna yli puolet ajasta kasvattaen järjestelmän elinikää entisestään. Kokeiden aikana, esitetty järjestelmä kykeni havaitsemaan valvotulle alueelle tunkeutuneen ihmisen ja seuraamaan hänen liikkeitään reaaliajassa. Järjestelmän mahdollisia sovelluskohteita ovat kriittisen rakennusten valvonta, työntekijöiden turvallisuuden lisääminen teollisuudessa, edesauttaa pelastustyöntekijöitä löytämään ihmiset esimerkiksi tulipaloissa ja maanjäristyksissä, sekä avustamaan poliiseja panttivankitilanteissa tai terroristihyökkäyksissä.In the context of wireless sensor networks (WSNs), the received signal strength indicator (RSSI) has been traditionally exploited for nodes localization, distance estimation, and link quality assessment. Recent research has shown that variations of the RSSI in indoor environments where nodes have been deployed can be exploited to detect movements of people. Moreover, the time-histories of the RSSI of multiple links allow reconstructing the path followed by the person inside the monitored area. This approach, though effective, requires the transmission of multiple raw RSSI time-histories to a central sink node for off-line analysis, consistently increasing latency and power consumption of the system. This thesis aims at applying distributed processing of the RSSI measurements for indoor surveillance purposes. Through distributed processing, the nodes are able to autonomous-ly detect and track a moving person, minimizing latency and power consumption of the system by transmitting to the sink node only the alerts raised by significant events. More-over, a high accuracy time synchronization protocol allows the nodes to keep the radio off for over half of the time, increasing the life time of the system. During the tests, the proposed system was able to detect the intrusion of a person walking inside the monitored area, and to correctly keep track in real-time of the path he had followed. Possible applications of such a system include surveillance of buildings, enhancement of workers safety in factories, support to emergency workers in locating people e.g. during fires and earthquakes, and to police in hostage situations or terrorist attacks