NFC technology is considered extremely secure for communication and the number of phones that can support NFC is also at a rise. The technology is gaining worldwide recognition and as it is easy to implement and since it is really economical numerous applications are using it. Most of NFC applications involve the usage of tags, which can easily be duplicated or can be replaced by a fake one. Therefore, though the technology is so useful and secure, this weakness makes it vulnerable to certain attacks.
NFC has numerous applications but in this thesis, I will be discussing various security threats related to NFC applications involving NFC tag and an NFC enabled smartphone, for example smart posters. This thesis will evaluate various security threats like phishing, exposure to adult content etc., what they are and how an attacker can carry out these attacks. Further discussion will be on what an artificial neural network (ANN) is and how it can be used to eliminate these threats. The thesis also proposes a security model that will use ANN, to provide security against threats and will also provide user confidentiality, anonymity and privacy, and a category classifier to increase the overall efficiency of the model and to decrease the memory usage, and will also provide users with an added feature of personalizing their security according to their requirements.
Table of Contents
1. Introduction
2. Background
2.1 Near Field Communication
2.1.1 Tag Reader/Writer Mode
2.1.2 Peer to Peer Mode
2.1.3 Card Emulation Mode
2.2 NFC tags
2.2.1 Tag types
2.3 NFC Data Exchange Format (NDEF)
2.4 Reading NDEF data from an NFC tag
2.5 Cryptography
2.5.1 Symmetric Key Cryptography
2.5.2 Public Key Cryptography
2.6 Artificial Neural Network (ANN)
2.7 Category Classifier
3. NFC Security Threats
3.1 Exposure to Adult/Objectionable content
3.2 Phishing
3.3 Automated malware download and malicious web pages
3.4 Eavesdropping
3.5 Data Corruption
3.6 Data Modification
4. Counter-Measures
4.1 Exposure to Adult/Objectionable content
4.2 Phishing
4.3 Automated malware downloads and malicious websites
5. Proposed Security Model
5.1 Components
5.2 User Sign-Up
5.3 Working
5.3.1 Personalised Security List
5.3.2 Data Uploading
5.3.3 Data Retrieval
5.4 Anonymity
6. Conclusion and Future Work
6.1 Conclusion
6.2 Future work
Objectives and Research Themes
This thesis aims to develop a robust security model for NFC-enabled smartphones to protect users from malicious data scanned from NFC tags. The research focuses on mitigating specific security threats—such as phishing, unauthorized exposure to adult content, and automated malware delivery—without compromising the inherent high-speed transmission performance of NFC technology.
- Implementation of Artificial Neural Networks (ANN) for real-time threat detection and URL classification.
- Development of a user-centric "Personalised Security List" for customizable white-listing and black-listing.
- Methods for ensuring user anonymity and confidentiality via unique ID (UID) generation and encrypted data storage.
- Evaluation of counter-measures against common NFC vulnerabilities like eavesdropping, data corruption, and data modification.
Excerpt from the Book
3.1 Exposure to Adult/Objectionable content
Paedophiles, hate literature, violence, pornography are few typical examples of adult or objectionable content. The sort of data these contents contain and the negative effect that they can have on the web users has made it one of the biggest social issues that needs to be resolve (Choi, et al., August 2005). An attacker can expose teenage and other web users to such content by just re writing or replacing a legitimate NFC tag with one containing adult content. For example, an organisation ABC is launching a new product and for its promotion they make use of smart posters. They embed every poster with an NFC tag, the NFC tag contains the company’s URL which when scanned by the user, directs the user to the company’s website. Now, an attacker, with intent of destroying company’s reputation, replaces the NFC tag embedded in to the poster with one of his tags. Say, hat replaced tag contains URL of a porn site. So, now whenever a user will scan that tag he/she will be directed to that porn site instead of the company’s website. Since the NFC enabled device performed its action solely based on the type of data stored on the tag and it didn’t perform any actions to check the authenticity or the credibility of the data, the attacker was successful in launching this attack.
Summary of Chapters
Chapter 1: Introduction: Outlines the primary goal of the thesis, which is to implement security checks for NFC tag data without reducing transmission speed, and provides a brief structure of the remaining report.
Chapter 2: Background: Introduces the fundamental concepts of NFC technology, communication modes, tag types, NDEF data formats, cryptographic principles, and the role of Artificial Neural Networks (ANN) and category classifiers.
Chapter 3: NFC Security Threats: Details various attack vectors targeting NFC, including exposure to objectionable content, phishing, automated malware downloads, eavesdropping, data corruption, and unauthorized data modification.
Chapter 4: Counter-Measures: Explores strategies to neutralize threats, specifically utilizing ANN training techniques to classify web content as infectious or non-infectious based on page features and URL structures.
Chapter 5: Proposed Security Model: Presents the core architecture of the proposed security solution, including component integration, user sign-up procedures via UID generation, data uploading/retrieval mechanisms, and anonymity protection.
Chapter 6: Conclusion and Future Work: Summarizes the findings regarding the effectiveness of the proposed ANN-based model and discusses potential future optimizations, such as prioritizing detection algorithms to reduce processing time.
Keywords
NFC Technology, Security Model, Artificial Neural Network, Phishing, Malware, Data Confidentiality, User Anonymity, NDEF, Cryptography, NFC Tags, URL Classification, Personalised Security List, Data Integrity, Threat Detection, Smart Posters
Frequently Asked Questions
What is the core focus of this research?
The work focuses on enhancing the security of Near Field Communication (NFC) environments by implementing a model that filters data scanned from NFC tags to protect users from malicious web content and phishing attempts.
What are the primary security threats addressed in the work?
The thesis specifically addresses the risks of users being directed to adult or objectionable content, phishing websites, and pages that trigger automated malware downloads.
What is the main goal or research question?
The primary goal is to implement security checks on NFC-scanned data before it is handled by the user's browser, ensuring that these security measures do not decrease the high data transmission speed of NFC.
Which scientific methods are employed?
The author employs Artificial Neural Networks (ANN) for threat classification and training, along with cryptographic methods like unique ID (UID) generation, One-Way functions, and SSL-based encrypted data transmission.
What does the main body of the work cover?
It provides an overview of NFC technology, an analysis of common NFC-related vulnerabilities, a review of existing counter-measures, and the detailed architecture and working steps of a novel security model.
Which keywords best describe this study?
Key terms include NFC Technology, ANN, Phishing, Security Model, Data Confidentiality, and User Anonymity.
How does the UID generator prevent unauthorized tracking?
The UID generator functions as a One-Way function; it is computationally easy to compute the UID from user credentials but nearly impossible to reverse the process to reveal the original identity, ensuring anonymity.
How does the "Personalised Security List" improve efficiency?
Instead of storing individual URLs, the model classifies and stores categories in a personal list. This approach drastically reduces memory usage and speeds up the verification process when a user scans a tag.
Why is a "two-stage classification model" used for malware detection?
This approach uses static feature extraction to quickly identify clearly harmless or harmful URLs, only passing suspicious links to a more resource-intensive run-time feature monitor, thus balancing security and performance.
- Arbeit zitieren
- Siddharth Sharma (Autor:in), 2015, Secure data transmission between an NFC tag and an NFC enabled smartphone, München, GRIN Verlag, https://www.grin.com/document/337906
