QRFence: A flexible and scalable QR link security detection framework for Android devices

Song, Jun; Gao, Kun; Shen, Xinyang; Qi, Xiaotian; Liu, Rui; Choo, Kim‐Kwang Raymond

doi:10.1016/j.future.2018.05.082

Cited by 4 publications

(10 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The security and privacy issues of QR code scanners have been relatively well studied over the past few years [6][7][8][9], [15][16][17], [19][20][21][22][23][24][25][26][27][28][29]. We will briefly review the literature on the security and privacy of current QR code scanners.…”

Section: Related Workmentioning

confidence: 99%

“…Existing security techniques for detecting malicious QR links concentrate mostly on the security of the web browser. The efficiency and accuracy of the applications are entirely dependent on the capability of the browser and its malicious link detection method and plug-ins [24]. Nonetheless, the majority of these browsers rely on the blacklist approach, which is not the most effective means of identifying recently developed websites.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

QsecR: Secure QR Code Scanner According to a Novel Malicious URL Detection Framework

Rafsanjani,

Kamaruddin,

Rusli

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QsecR: Secure QR Code Scanner According to a Novel Malicious URL Detection Framework

Rafsanjani,

Kamaruddin,

Rusli

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Reference [15] proposed a QR link security detection method for android devices. The authors' employed link characteristics with several permissions and achieved flexible and scalable results.…”

Section: Related Workmentioning

confidence: 99%

Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Wahsheh

Al-Zahrani

2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

Web attackers aim to propagate malicious links using various techniques to deceive users. They attempt to control victims’ devices or obtain their passwords remotely, thereby acquiring access to bank accounts, financial transactions, or private and sensitive information they trade via the Internet. QR codes are accessible, free, easy to use, and can be scanned through several free apps on smartphones. As there is no standard structure or authentication phase in QR code generation, such codes are vulnerable to suspicious online content embedding, i.e., phishing, Cross-Site Scripting (XSS), and malware. Many studies have highlighted the attacks that may be perpetrated using barcodes, and there are some security countermeasures. Several of these solutions are limited to malicious link detection methods or require knowledge of cryptographic techniques. This study’s main objective is to detect malicious URLs embedded in QR codes. A dataset of 90 000 benign and malicious URLs was collected from various resources, and their lexical properties were extracted. Two computational intelligence models, fuzzy logic and multilayer perceptron artificial neural network (MLP-ANN), were applied and compared. An MLP-ANN was identified as the best classifier for detecting malicious URLs, and a proactive, secure, real-time computational intelligence barcode scanner implementation (BarCI ) against malicious QR code links was proposed based on this classifier. The results demonstrate that this approach enables efficient real-time attack detection with 82.9% accuracy

show abstract

“…Kriptografi asimetris adalah sebuah algoritma yang kriptografi yang menggunakan 2 kunci yang berbeda untuk melakukan enkripsi dan dekripsi (Kittur and Pais, 2017), (Ani et al, 2011). Metode template matching juga handal untuk face detection (Çinar Akakin et al, 2006), (Wijanarko et al, 2017).…”

Section: Pendahuluanunclassified

Sistem Presensi Android Berbasis Qrcode Dan Dsrsa Menggunakan Template Matching

Putra¹,

Rahmad²,

Yuninanto³

et al. 2020

JIP

View full text Add to dashboard Cite

Pelaksanaan sistem presensi pada Jurusan Teknologi Informasi di Politeknik Negeri Malang perlu dikembangkan agar lebih efektif dalam implementasinya. Efektifitas yang dimaksud adalah mahasiswa tidak perlu lagi untuk mengambil lembar presensi saat akan melakukan presensi perkuliahan dan mengembalikan lembar tersebut setelah selesai melakukan presensi. Penelitian ini bertujuan untuk membangun suatu sistem presensi untuk menjawab permasalahan yang telah dipaparkan. Dalam pembangunan sistem presensi, digunakan 2 algoritma yaitu DS RSA dan Template Matching. Algoritma DS RSA yang disisipkan pada pembangkitan QR Code digunakan demi keamanan dan reliabilitas pada pengacakan kunci yang telah diinisialisasi oleh algoritma DS RSA. Pengacakan itu bermaksud untuk memberikan batasan pada mahasiswa agar tidak bisa melakukan presensi jika kelasnya tidak sesuai. Sedangkan untuk Template Matching digunakan untuk pengecekan wajah mahasiswa. Hal ini dilakukan untuk memastikan bahwa mahasiswa tersebut hadir pada kelas dan dosen dapat mengantisipasi adanya kecurangan mahasiswa dalam melakukan absensi. Penerapan sistem presensi ini membutuhkan setiap mahasiswa untuk memfoto wajah mereka lalu membaca QR Code yang telah dibangkitkan dengan smartphone sesuai dengan kelas dan ditampilkan pada layar proyektor kelas. Penelitian ini berhasil membangun sistem presensi berbasis QR Code dengan mengimplementasikan algoritma DS RSA dan template matching. Sistem pembangkitan QR Code ini memiliki reliabilitas sangat baik dan akurasi tertinggi dari algoritma template matching adalah 93.33% dengan jumlah 80 template.

show abstract

QRFence: A flexible and scalable QR link security detection framework for Android devices

Cited by 4 publications

References 54 publications

QsecR: Secure QR Code Scanner According to a Novel Malicious URL Detection Framework

QsecR: Secure QR Code Scanner According to a Novel Malicious URL Detection Framework

Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Sistem Presensi Android Berbasis Qrcode Dan Dsrsa Menggunakan Template Matching

Contact Info

Product

Resources

About