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Methods and Tools for Network Reconnaissance of IoT Devices = = Metode i alati za izvidanje mreze IoT uredaja.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Methods and Tools for Network Reconnaissance of IoT Devices =/
Reminder of title:	Metode i alati za izvidanje mreze IoT uredaja.
remainder title:	Metode i alati za izvidanje mreze IoT uredaja.
Author:	Gvozdenovic, Stefan.
Description:	1 online resource (145 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
Contained By:	Dissertations Abstracts International85-07A.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798381407426

Methods and Tools for Network Reconnaissance of IoT Devices = = Metode i alati za izvidanje mreze IoT uredaja.
Gvozdenovic, Stefan.

Methods and Tools for Network Reconnaissance of IoT Devices =Metode i alati za izvidanje mreze IoT uredaja.Metode i alati za izvidanje mreze IoT uredaja. - 1 online resource (145 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: A.

Thesis (Ph.D.)--Boston University, 2024.

Includes bibliographical references

The Internet of Things (IoT) impacts nearly all aspects surrounding our daily life, including housing, transportation, healthcare, and manufacturing. IoT devices communicate through a variety of communication protocols, such as Bluetooth Low Energy (BLE), Zigbee, Z-Wave, and LoRa. These protocols serve essential purposes in both commercial industrial and personal domains, encompassing wearables and intelligent buildings.The organic and decentralized development of IoT protocols under the auspices of different organizations has resulted in a fragmented and heterogeneous IoT ecosystem. In many cases, IoT devices do not have an IP address. Furthermore, some protocols, such as LoRa and Z-Wave, are proprietary in nature and incompatible with standard protocols.This heterogeneity and fragmentation of the IoT introduce challenges in assessing the security posture of IoT devices. To address this problem, this thesis proposes a novel methodology that transcends specific protocols and supports network and security monitoring of IoT devices at scale. This methodology leverages the capabilities of software-defined radio (SDR) technology to implement IoT protocols in software.We first investigate the problem of IoT network reconnaissance, that is the discovery and characterization of all the IoT devices in one's organization. We focus on four popular protocols, namely Zigbee, BLE, Z-Wave, and LoRa. We introduce and analyze new algorithms to improve the performance and speed-up the discovery of IoT devices. These algorithms leverage the ability of SDRs to transmit and receive signals across multiple channels in parallel.We implement these algorithms in the form of an SDR tool, called IoT-Scan, the first universal IoT scanner middleware. We thoroughly evaluate the delay and energy performance of IoT-Scan. Notably, using multi-channel scanning, we demonstrate a reduction of 70% in the discovery times of Bluetooth and Zigbee devices in the 2.4GHz band and of LoRa and Z-Wave devices in the 900MHz band, versus single-channel scanning.Second, we investigate a new type of denial-of-service attacks on IoT cards, called Truncate-after-Preamble (TaP) attacks. We employ SDRs to assess the security posture of off-the-shelf Zigbee and Wi-Fi cards to TaP attacks. We show that all the Zigbee devices are vulnerable to TaP attacks, while the Wi-Fi devices are vulnerable to the attack to a varying degree. Remarkably, TaP attacks demand energy consumption five orders of magnitude lower than what is required by a continuous jamming mechanism. We propose several countermeasures to mitigate the attacks.Third, we devise an innovative approach for the purpose of identifying and creating unique profiles for IoT devices. This approach leverages SDRs to create malformed packets at the physical layer (e.g., truncated or overlapping packets). Experiments demonstrate the ability of this approach to perform fine-grained timing experiments (at the microsecond level), craft multi-packet transmissions/collisions, and derive device-specific reception curves.In summary, the results of this thesis validate the feasibility of our proposed SDR-based methodology in addressing fundamental security challenges caused by the heterogeneity of the IoT. This methodology is future-proof and can accommodate new protocols and protocol upgrades.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381407426Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Denial of serviceIndex Terms--Genre/Form:

554714
Electronic books.

Methods and Tools for Network Reconnaissance of IoT Devices = = Metode i alati za izvidanje mreze IoT uredaja.
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502 $a Thesis (Ph.D.)--Boston University, 2024.
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520 $a The Internet of Things (IoT) impacts nearly all aspects surrounding our daily life, including housing, transportation, healthcare, and manufacturing. IoT devices communicate through a variety of communication protocols, such as Bluetooth Low Energy (BLE), Zigbee, Z-Wave, and LoRa. These protocols serve essential purposes in both commercial industrial and personal domains, encompassing wearables and intelligent buildings.The organic and decentralized development of IoT protocols under the auspices of different organizations has resulted in a fragmented and heterogeneous IoT ecosystem. In many cases, IoT devices do not have an IP address. Furthermore, some protocols, such as LoRa and Z-Wave, are proprietary in nature and incompatible with standard protocols.This heterogeneity and fragmentation of the IoT introduce challenges in assessing the security posture of IoT devices. To address this problem, this thesis proposes a novel methodology that transcends specific protocols and supports network and security monitoring of IoT devices at scale. This methodology leverages the capabilities of software-defined radio (SDR) technology to implement IoT protocols in software.We first investigate the problem of IoT network reconnaissance, that is the discovery and characterization of all the IoT devices in one's organization. We focus on four popular protocols, namely Zigbee, BLE, Z-Wave, and LoRa. We introduce and analyze new algorithms to improve the performance and speed-up the discovery of IoT devices. These algorithms leverage the ability of SDRs to transmit and receive signals across multiple channels in parallel.We implement these algorithms in the form of an SDR tool, called IoT-Scan, the first universal IoT scanner middleware. We thoroughly evaluate the delay and energy performance of IoT-Scan. Notably, using multi-channel scanning, we demonstrate a reduction of 70% in the discovery times of Bluetooth and Zigbee devices in the 2.4GHz band and of LoRa and Z-Wave devices in the 900MHz band, versus single-channel scanning.Second, we investigate a new type of denial-of-service attacks on IoT cards, called Truncate-after-Preamble (TaP) attacks. We employ SDRs to assess the security posture of off-the-shelf Zigbee and Wi-Fi cards to TaP attacks. We show that all the Zigbee devices are vulnerable to TaP attacks, while the Wi-Fi devices are vulnerable to the attack to a varying degree. Remarkably, TaP attacks demand energy consumption five orders of magnitude lower than what is required by a continuous jamming mechanism. We propose several countermeasures to mitigate the attacks.Third, we devise an innovative approach for the purpose of identifying and creating unique profiles for IoT devices. This approach leverages SDRs to create malformed packets at the physical layer (e.g., truncated or overlapping packets). Experiments demonstrate the ability of this approach to perform fine-grained timing experiments (at the microsecond level), craft multi-packet transmissions/collisions, and derive device-specific reception curves.In summary, the results of this thesis validate the feasibility of our proposed SDR-based methodology in addressing fundamental security challenges caused by the heterogeneity of the IoT. This methodology is future-proof and can accommodate new protocols and protocol upgrades.
520 $a Internet stvari (IoT) utice na gotovo sve aspekte naseg svakodnevnog zivota, ukljucujuci stanovanje, transport, zdravstvenu zastitu i proizvodnju. IoT uredaji komuniciraju putem razlicitih komunikacijskih protokola, kao sto su Bluetooth Low Energy (BLE), Zigbee, Z-Wave i LoRa. Ovi protokoli sluze sustinskim svrhama u komercijalnim, industrijskim i licnim domenima, ukljucujuci nosive uredaje i inteligentne zgrade.Organski i decentralizirani razvoj IoT protokola pod okriljem razlicitih organizacija rezultirao je fragmentiranim i heterogenim IoT ekosistemom. U mnogim slucajevima IoT uredaji nemaju IP (internet protokol) adresu. Stavise, neki protokoli, kao sto su LoRa i Z-Wave, su po prirodi vlasnicki i nekompatibilni sa standardnim protokolima.Ova heterogenost i fragmentacija IoT-a predstavljaju izazove u procjeni sigurnosnog polozaja IoT uredaja. Kako bi se rijesio ovaj problem, ovaj diplomski rad predlaze novu metodologiju koja prevazilazi pojedinacne protokole i podrzava mrezni i bezbjednosni nadzor IoT uredaja u velikom broju. Ova metodologija koristi mogucnosti tehnologije softverski definisanog radija (SDR) za implementaciju IoT protokola u softveru.Prvo istrazujemo problem izvidanja IoT mreze, odnosno otkrivanje i karakterizaciju svih IoT uredaja u organizaciji. Fokusiramo se na cetiri popularna protokola, naime Zigbee, BLE, Z-Wave i LoRa. Predstavljamo i analiziramo nove algoritme za poboljsanje performansi i ubrzavanje otkrivanja IoT uredaja. Ovi algoritmi iskoristavaju sposobnost SDR-a da paralelno salju i primaju signale preko vise kanala.Ove algoritme implementiramo u obliku SDR alata, nazvanog IoT-Skener, prvog univerzalnog IoT skenera. Temeljno procjenjujemo kasnjenje i energetske performanse IoT-Skenera. Primjetno, koristenjem visekanalnog skeniranja, pokazujemo smanjenje od 70% vremena otkrivanja Bluetooth i Zigbee uredaja u opsegu od 2,4 GHz i LoRa i Z-Wave uredaja u pojasu od 900 MHz, u odnosu na jednokanalno skeniranje.Drugo, istrazujemo novi tip napada uskracivanja usluge IoT kartice (tj. napad izgladnjivanja), koji nazivamo napadi skracivanja-nakon-zaglavlja (skr. TaP). Koristimo softverski radijo za procjenu sigurnosnog polozaja Zigbee i Wi-Fi kartica za TaP napade. Pokazujemo da su svi Zigbee uredaji ranjivi na TaP napade, dok su Wi-Fi uredaji ranjivi na napad u razlicitom stepenu. Zanimljivo je da TaP napadi zahtijevaju potrosnju energije pet redova velicine manju od one koja je potrebna za mehanizam kontinuiranog ometanja. Predlazemo nekoliko kontramjera za ublazavanje napada.Trece, osmisljavamo inovativan pristup u svrhu identifikacije i kreiranja jedinstvenih profila za IoT uredaje. Ovaj pristup koristi SDR za kreiranje deformisanih paketa na fizickom sloju (npr. skraceni paketi ili paketi koji se preklapaju). Eksperimenti pokazuju sposobnost ovog pristupa za izvodenje preciznih eksperimenata (na nivou mikrosekunde), kreiranje visepaketnih prenosa/sudara i izvodenje kriva prijema specificnih za uredaj.Ukratko, rezultati ove teze potvrduju izvodljivost nase predlozene metodologije zasnovane na SDR-u u rjesavanju fundamentalnih bezbjednosnih izazova uzrokovanih heterogenoscu IoT-а. Ova metodologija je skalabilna u buducnosti i moze prihvatiti nove protokole i nadogradnje protokola.
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