Internet Traffic Privacy Enhancement with Masking: Optimization and Tradeoffs

Iacovazzi, Alfonso; Baiocchi, Andrea

doi:10.1109/tpds.2013.42

Cited by 18 publications

(2 citation statements)

References 12 publications

(15 reference statements)

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“…To improve the authentication performance, we may utilize multi-dimensional attributes in higher layers (e.g., application layer) and explore more precise information of Alice, but her privacy may not be guaranteed. In other words, Bob may collect Alice's information, such as user behaviors and location-related features, for analyzing her habits, locations, and other sensitive information during the authentication process, thus leading to privacy leakage [9]. Hence, developing a privacy preserving authentication scheme based on the masking methods [14] is helpful in protecting Alice's private information during the authentication process by adding obfuscation patterns on attribute measurements.…”

Section: Supervised Learning Algorithmsmentioning

confidence: 99%

Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks

Fang

Wang

Tomasin

2019

IEEE Wireless Commun.

114

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The fifth generation (5G) and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing continuous protection, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms. In a nutshell, the machine learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous, and situation-aware device validation under unknown network conditions and unpredictable dynamics. 2 heterogeneous devices and machines [1]. The dramatically growing number of low-cost devices and access points with increased mobility and heterogeneity leads to the extremely complex and dynamic environment of 5G-and-beyond wireless networks. Specifically, due to the open broadcast nature of radio signal propagation, widely adopted standardized transmission protocols, and intermittent communication characteristic, wireless communications are extremely vulnerable to interception and spoofing attacks [2]. As shown in the conventional 'Alice-Bob-Eve' model of Fig. 1, Alice and Bob are legitimate devices and communicate with each other in the presence of a Spoofer (Eve). This adversary dynamically intends to intercept legitimate communications and to impersonate Alice for obtaining illegal advantages from Bob. The main objective of Bob is to uniquely and unambiguously identify Alice by authentication techniques. Multi-dimensional information received at Bob Time Spoofing Signal Legitimate Signal Scattering cluster Scattering cluster Scattering cluster Eve (Spoofer) Alice Bob Process-related information Physical information Fig. 1: Alice-Bob-Eve model in the complex time-varying environment. Bob identifies Alice from Spoofer based on the multi-dimensional received information, which may be related to communication links, physical environment, and communication process, just to name a few.Although cryptographic techniques have been widely studied for authentication, they may fall short of the desired performance in many emerging scenarios of 5G-and-beyond wireless networks [2]. The fundamental weaknesses of conventional cryptography techniques are the increasing latencies, communication and computation overheads for achieving better security pe...

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Section: Supervised Learning Algorithmsmentioning

confidence: 99%

Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks

Fang

Wang

Tomasin

2019

IEEE Wireless Commun.

114

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“…The creator proposed an enhanced movement for veiling calculation. It evacuates any spilling in [8]. A strategy to discover the DOS assaults in VANETs is proposed.…”

Section: Literature Surveymentioning

confidence: 99%

IPTTA: Leveraging Token-based node IP assignment and verification for WSN

Vinayagam

Parthasarathy

2014

2014 International Conference on Science Engineering and Management Research (ICSEMR)

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This paper is focusing on detection and prevention Sybil nodes in Wireless Sensor Networks. Sybil is a fake-ID based malicious activity destroying the networks behavior. Here IPTTA -[IP Traceback and Token Assigner] approach is proposed to detect and prevent Sybil attack by assigning and verifying IP address with assigned unique Token for each node in the route. IPTTA verify the node IP, Token with time stamp and fetch the contradictory node as Sybil node. The simulation results show that the IPTTA provides better result than the existing systems and reduces cost and time

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