Classification of URL bitstreams using bag of bytes

Shima, Keiichi; Miyamoto, Daisuke; Abe, Hiroshi; Ishihara, Takeshi; Okada, Kei; Sekiya, Yuji; Asai, Hidehiro; Doi, Yusuke

doi:10.1109/icin.2018.8401597

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…Deep Neural Network: A DL based malicious URL detection framework extracted features from static HTML files and used spatial information to yield 97.5% accuracy and low FPR [243]. A DNN framework was trained using real-life datasets of URLs and achieved a detection accuracy of 94.18% [255]. In another study, ANN and DNN based phishing URL detection system was proposed and trained using 73575 URLs with experimental analysis showing 92% accuracy for ANN and 96% accuracy for DNN outperforming ML classifiers [256].…”

Section: Domain Generation Algorithms (Dgas)mentioning

confidence: 99%

Deep Learning for Cyber Security Applications: A Comprehensive Survey

Ravi¹,

Alazab²,

Srinivasan³

et al. 2021

Preprint

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Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

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Section: Domain Generation Algorithms (Dgas)mentioning

confidence: 99%

Deep Learning for Cyber Security Applications: A Comprehensive Survey

Ravi¹,

Alazab²,

Srinivasan³

et al. 2021

Preprint

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“…The proposed framework uses regular expressions to extract features and uses spatial information to yield high accuracy of 97.5% with very low false positive rate. In [578], the authors have proposed a deep neural network based framework for classifying normal and malicious URL where byte value are extracted from URL to construct a URL vector. The proposed model is trained using real-life datasets obtained from phishtank.com and from a private research organization and it achieves an accuracy of 94.18%.…”

Section: A Deep Learning In Intrusion Detectionmentioning

confidence: 99%

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Ravi¹,

Alazab²,

Sriram³

et al. 2020

Preprint

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“…En (Shima et al, 2018), se usa una técnica llamada Bag of Bytes. Con esta técnica, los datos no entrarían totalmente crudos para ser analizados con los algoritmos de Deep Learning, sino que previamente los investigadores le asignan un valor hexadecimal a cada carácter de la cadena URL, luego, esos valores se emparejan con cada valor a la derecha.…”

Section: B úLtimas Técnicas De Detección De Ataques De Phishingunclassified

Un experimento para crear conciencia en las personas acerca de los ataques de Ingeniería Social

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Fuertes

Sánchez-Gordón

2020

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La Ingeniería Social es la técnica que permite obtener información confidencial de los usuarios, de manera fraudulenta, con la finalidad de usarla en contra de ellos mismos, o de las organizaciones en las que laboran. Este estudio presenta un experimento enfocado a crear conciencia acerca de las consecuencias de este tipo de ataque, mediante la ejecución de un ataque controlado a personas de confianza. Para lograrlo, se han llevado a cabo un conjunto de engaños y actividades, que los atacantes usan comúnmente para obtener información sensible, incentivando la curiosidad de los contactos de las redes sociales para que visiten un blog personal con información ficticia. A más de esta interacción humana, se ha instalado un complemento oculto y no deseado, para recolectar información del usuario tales como: su dirección IP, país de origen, sistema operativo y tipo de navegador. Con la información recolectada, se realizó un ataque de escaneo a los puertos 80 (Web server) y 22 (SSH Server), para encontrar más información sensible. Posteriormente, se muestran los resultados a las víctimas. Además, luego del ataque se realizó una encuesta a los usuarios acerca de su conocimiento de Phishing y de Ingeniería Social. Los resultados muestran que únicamente el 2% de las personas, sospecharon o preguntaron acerca del verdadero motivo para visitar el Blog. Más aún, demuestra que las personas que visitaron el blog, no tienen conocimiento y conciencia de cómo se puede vulnerar información sensible de una forma relativamente sencilla.

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Classification of URL bitstreams using bag of bytes

Cited by 12 publications

References 4 publications

Deep Learning for Cyber Security Applications: A Comprehensive Survey

Deep Learning for Cyber Security Applications: A Comprehensive Survey

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Un experimento para crear conciencia en las personas acerca de los ataques de Ingeniería Social

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