Nonreciprocity Compensation Combined With Turbo Codes for Secret Key Generation in Vehicular Ad Hoc Social IoT Networks

Epiphaniou, Gregory; Karadimas, Petros; Ismail, Dhouha Kbaier Ben; Al-Khateeb, Haider; Dehghantanha, Ali; Choo, Kim‐Kwang Raymond

doi:10.1109/jiot.2017.2764384

Cited by 43 publications

(20 citation statements)

References 58 publications

(91 reference statements)

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“…(2) when the cluster has alert_1 and alert_2 and the other alerts "alert_3 and alert_4 " are missed; (3) when the 510 cluster has alert_1 and alert_3 and the other alerts "alert_2 and alert_4 " are missed; (4) when the cluster has alert_2 and alert_3 and the other alerts "alert_1 and alert_4 " are missed; (5) when the cluster has alert_1 and the other alerts "alert_2, alert_3, and alert_4 " are missed; (6) when the cluster has alert_2 and the other alerts "alert_1, alert_3, and alert_4 " are missed; 515 (7) and when the cluster has alert_3 and the other alerts "alert_1, alert_2, and alert_4 " are missed.…”

Section: Implementation Of the Alerts Clustering (Ac) Modulementioning

confidence: 99%

“…This trend is currently being driven by cyber warfare and the emergence of the Internet of Things [1][2][3]. The annual cost of cyber attacks was $3 trillion in 2015 and it is expected to increase to more than $6 trillion per annum by 5 2021 [4]. This high cost has brought much interest in research and investment towards developing new cyber attacks defence methods and techniques [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of advanced persistent threat using machine-learning correlation analysis

Ghafir

Hammoudeh

Přenosil

et al. 2018

Future Generation Computer Systems

231

107

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Item Type Article Authors Ghafir, Ibrahim; Hammoudeh, M.; Prenosil, V.; Han, L.; Hegarty, R.; Rabie, K.; Aparicio-Navarro, F.J. Citation Ghafir I, Hammoudeh M, Prenosil V (et al) Detection of advanced persistent threat using machine-learning correlation analysis. Future Generation Computer Systems. 89: 349-359. Rights Citation: Ghafir I, Hammoudeh M, Prenosil V (et al) Detection of advanced persistent threat using machine-learning correlation analysis. Future Generation Computer Systems. 89: 349-359. AbstractAs one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented system is able to predict APT in its early steps with a prediction accuracy of 84.8%.to a technical report by Trend Micro [11], this assumption is no longer valid with the rise of targeted attacks, Advanced Persistent Threats (APTs), in which both cyber-criminals and hackers are targeting selected organizations and persisting until they achieve their goals.The APT attack is a persistent, targeted attack on a specific organisation 20 and is performed through several steps [12]. The main aim of APT is espionage and then data exfiltration. Therefore, APT is considered as a new and more complex version of multi-step attack. These APTs present a challenge for current detection methods as they use advanced techniques and make use of unknown vulnerabilities. Moreover, the economic damage due to a successful 25 APT attack significant. The potential cost of attacks is the major motivation for the investments in intrusion detection and prevention systems [13]. APTs are currently one of the most serious threats to companies and governments [14].Most of the research in the area of APT detection, has focused on analysing already identified APTs [15][16][17][...

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Section: Implementation Of the Alerts Clustering (Ac) Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of advanced persistent threat using machine-learning correlation analysis

Ghafir

Hammoudeh

Přenosil

et al. 2018

Future Generation Computer Systems

231

107

View full text Add to dashboard Cite

show abstract

“…A channel gain complement method is developed to reduce the non-reciprocity of RSS in the key generation. In [31], the vehicle-to-vehicle communication characteristics, e.g., multipath propagation and surrounding scatterers' mobility, are incorporated in the key generation process. The non-reciprocity compensation method in [27] is used by the transmitter to generate the secret key according to designated RSS, while Turbo codes are used for channel information reconciliation of multiple links.…”

Section: Link-based Secret Key Generationmentioning

confidence: 99%

Design and Implementation of Secret Key Agreement for Platoon-based Vehicular Cyber-physical Systems

Li¹,

Emami³

et al. 2019

ACM Trans. Cyber-Phys. Syst.

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In platoon-based vehicular cyber-physical system (PVCPS), a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control messages to the vehicles that follow. Securing wireless transmissions of the messages between the vehicles is critical for privacy and confidentiality of platoon's driving pattern. However, due to the broadcast nature of radio channels, the transmissions are vulnerable to eavesdropping. In this paper, we propose a cooperative secret key agreement (CoopKey) scheme for encrypting/decrypting the control messages, where the vehicles in PVCPS generate a unified secret key based on the quantized fading channel randomness. Channel quantization intervals are optimized by dynamic programming to minimize the mismatch of keys. A platooning testbed is built with autonomous robotic vehicles, where a TelosB wireless node is used for onboard data processing and multi-hop dissemination. Extensive real-world experiments demonstrate that CoopKey achieves significantly low secret bit mismatch rate in a variety of settings. Moreover, the standard NIST test suite is employed to verify randomness of the generated keys, where the p-values of our CoopKey pass all the randomness tests. We also evaluate CoopKey with an extended platoon size via simulations to investigate the effect of system scalability on performance.

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“…A modified version of Mathur et al lossy scheme is proposed in [17] where m consecutive excursions need not be on the same side (either below the lower threshold or above higher threshold). Despite being more influenced by sharp magnitude changes, resulting bit-streams are the direct concatenation of whole quantised excursions instead of considering only the exchanged indices, as in the inspiring protocol.…”

Section: B Quantisation Schemesmentioning

confidence: 99%

“…None of the previous schemes considers the presence and the possible interactions among vehicles and intermediate objects. Work in [17] incorporates three-dimensional scattering and scatterers' mobility to obtain a more realistic system model and corresponding simulation [18]. The application of a non-reciprocity compensation technique in conjunction with an information reconciliation phase based on turbo codes, record a significant improvement in both key generation rate and bit mismatch rate (defined in Section 3).…”

Section: Introductionmentioning

confidence: 99%

Quantisation feasibility and performance of RSS-based secret key extraction in VANETs

Bottarelli

Epiphaniou

Ismail

et al. 2018

2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security)

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Vehicular Ad Hoc Networks (VANETs) has emerged as a unique implementation of Mobile Ad Hoc Networks (MANETs). These networks promise to increase road safety and improve the driving experience by exploiting recent advances in wireless technologies for both intra-vehicle and inter-vehicle communications. Physical layer security is a promising alternative approach to secure communication in VANETs where physical and applications' constraints encourage the use of lightweight and fast cryptographic algorithms. Our work focuses on the quantisation stage of the secret generation process, by reviewing existing schemes in the public domain and associated performance metrics. Evaluations are done through simulation with the aid of a wireless channel model which includes three-dimensional scattering and scatterers' mobility. Preliminary findings show that RSS-based algorithms do not perform efficiently in the proposed vehicular stochastic wireless model. Hence they are not able to satisfy the typical low latency required in safetyrelated broadcasting messaging. We conclude that more research is desirable to design protocols capable of taking advantage from the nodes' high-mobility and the consequent variability of both coherence intervals and level crossing rates, to further improve secret bit extraction throughput.

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Nonreciprocity Compensation Combined With Turbo Codes for Secret Key Generation in Vehicular Ad Hoc Social IoT Networks

Cited by 43 publications

References 58 publications

Detection of advanced persistent threat using machine-learning correlation analysis

Detection of advanced persistent threat using machine-learning correlation analysis

Design and Implementation of Secret Key Agreement for Platoon-based Vehicular Cyber-physical Systems

Quantisation feasibility and performance of RSS-based secret key extraction in VANETs

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