Biblio

Nowadays, with the continuous increase in oil prices and the worldwide shift towards clean energy, all-electric vehicles are booming. Thence, these vehicles need widespread charging systems operating securely and reliably. Consequently, these charging systems need the most robust cybersecurity measures and strong authentication mechanisms to protect its user. This paper presents a new security scheme leveraging human biometrics in terms of iris recognition to defend against multiple types of cyber-attacks such as fraudulent identities, man-in-the-middle attacks, or unauthorized access to electric vehicle charging stations. Fundamentally, the proposed scheme implements a security mechanism based on the inherently unique characteristics of human eye biometric. The objective of the proposed scheme is to enhance the security of electric vehicle charging stations by using a low-cost and efficient authentication using k-Nearest Neighbours (KNN), which is a lightweight encryption algorithm.We tested our system on high-quality images obtained from the standard IITD iris database to search over the encrypted database and authenticate a legitimate user. The results showed that our proposed technique had minimal communication and computation overhead, which is quite suitable for the resource-limited charging station devices. Furthermore, we proved that our scheme outperforms other existing techniques.

Smartwatches are expected to become the world's best-selling electronic product after smartphones. Various smart-watches have been released to the private consumer market, but the data on smartwatches is not well protected. In this paper, we show for the first time that photoplethysmography (PPG)signals influenced by hand gestures can be used to authenticate users on smartwatches. The insight is that muscle and tendon movements caused by hand gestures compress the arterial geometry with different degrees, which has a significant impact on the blood flow. Based on this insight, novel approaches are proposed to detect the starting point and ending point of the hand gesture from raw PPG signals and determine if these PPG signals are from a normal user or an attacker. Different from existing solutions, our approach leverages the PPG sensors that are available on most smartwatches and does not need to collect training data from attackers. Also, our system can be used in more general scenarios wherever users can perform hand gestures and is robust against shoulder surfing attacks. We conduct various experiments to evaluate the performance of our system and show that our system achieves an average authentication accuracy of 96.31 % and an average true rejection rate of at least 91.64% against two types of attacks.

Smart Card has complications with validation and transmission process. Therefore, by using peeping attack, the secret code was stolen and secret filming while entering Personal Identification Number at the ATM machine. We intend to develop an authentication system to banks that protects the asset of user's. The data of a user is to be ensured that secure and isolated from the data leakage and other attacks Therefore, we propose a system, where ATM machine will have a QR code in which the information's are encrypted corresponding to the ATM machine and a mobile application in the customer's mobile which will decrypt the encoded QR information and sends the information to the server and user's details are displayed in the ATM machine and transaction can be done. Now, the user securely enters information to transfer money without risk of peeping attack in Automated Teller Machine by just scanning the QR code at the ATM by mobile application. Here, both the encryption and decryption technique are carried out by using Triple DES Algorithm (Data Encryption Standard).

In recent years, with growing demands towards big data application, various research on context-awareness has once again become active. This paper proposes a new type of context-aware user authentication that controls the authentication level of users, using the context of “physical trust relationship” that is built between users by visual contact. In our proposal, the authentication control is carried out by two mechanisms; “i-Contact” and “k-Contact”. i-Contact is the mechanism that visually confirms the user (owner of a mobile device) using the surrounding users' eyes. The authenticity of users can be reliably assessed by the people (witnesses), even when the user exhibits ambiguous behavior. k-Contact is the mechanism that dynamically changes the authentication level of each user using the context information collected through i-Contact. Once a user is authenticated by eyewitness reports, the user is no longer prompted for a password to unlock his/her mobile device and/or to access confidential resources. Thus, by leveraging the proposed authentication system, the usability for only trusted users can be securely enhanced. At the same time, our proposal anticipates the promotion of physical social communication as face-to-face communication between users is triggered by the proposed authentication system.