ReP-ETD: A Repetitive Preprocessing technique for Embedded Text Detection from images in spam emails

Manek, Asha S; Shamini, D. K.; Bhat, Veena H.; Shenoy, P. Deepa; Mohan, Mahesh; Venugopal, K R; Patnaik, L. M.

doi:10.1109/iadcc.2014.6779387

Cited by 6 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors in [344] applied various pattern recognition and computer vision techniques to detect image-based spam. Further similar studies can be found in [345][346][347][348][349].…”

Section: And Spam On Twittersupporting

confidence: 64%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

View full text Add to dashboard Cite

Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

show abstract

“…Authors in [344] applied various pattern recognition and computer vision techniques to detect image-based spam. Further similar studies can be found in [345][346][347][348][349].…”

Section: And Spam On Twittersupporting

confidence: 64%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Text-based 9 [18], [19], [20], [21], [22], [23], [24], [25], [26] Image-based 16 [27], [28], [29], [9], [30], [31], [32], [33], [34], [35], [17], [5], [36], [37], [38], [39] [20], [30], [34], [5], [37], [38], [39] Dredze 10 5789 spam (spam =3239 & ham = 2550) [30], [31], [33], [34], [40], [17], [5], [37], [28], [21] Enron corpus 1 Not specified [22] SMS spam 1 Not specified [18] Princeton spam corpus [20], [24], [30], [31], [41], [32],…”

Section: No Of Studies Referencementioning

confidence: 99%

“…The proposed classifier cannot detect image spam email. The same OCR tool was used in the work of some researchers [21], [22], [24], [23]. They examined and applied OCR software to filter image spam email.…”

Section: Spam Classification Techniques Analysis and Reviewmentioning

confidence: 99%