Semantic Detection of Targeted Attacks Using DOC2VEC Embedding
The targeted attack is one of the social engineering attacks. The detection of this type of attack is considered a challenge as it depends on semantic extraction of the intent of the attacker. However, previous research has primarily relies on the Natural Language Processing or Word Embedding techni...
| Permalink: | http://skupnikatalog.nsk.hr/Record/nsk.NSK01001163143/Details |
|---|---|
| Matična publikacija: |
Journal of communications software and systems (Online) 17 (2021), 4 ; str. 334-341 |
| Glavni autori: | S. El-Rahmany, Mariam (Author), Hussein Mohamed, Ensaf, H. Haggag, Mohamed |
| Vrsta građe: | e-članak |
| Jezik: | eng |
| Online pristup: |
https://doi.org/10.24138/jcomss-2021-0113 Elektronička verzija članka Elektronička verzija članka Elektronička verzija članka |
| LEADER | 02687naa a22003614i 4500 | ||
|---|---|---|---|
| 001 | NSK01001163143 | ||
| 003 | HR-ZaNSK | ||
| 005 | 20230213112508.0 | ||
| 006 | m d | ||
| 007 | cr|||||||||||| | ||
| 008 | 230213s2021 ci |o |0|| ||eng | ||
| 024 | 7 | |2 doi |a 10.24138/jcomss-2021-0113 | |
| 035 | |a (HR-ZaNSK)001163143 | ||
| 040 | |a HR-ZaNSK |b hrv |c HR-ZaNSK |e ppiak | ||
| 041 | 0 | |a eng | |
| 042 | |a croatica | ||
| 044 | |a ci |c hr | ||
| 080 | 1 | |2 2011 | |
| 100 | 1 | |a S. El-Rahmany, Mariam |4 aut |9 HR-ZaNSK | |
| 245 | 1 | 0 | |a Semantic Detection of Targeted Attacks Using DOC2VEC Embedding |h [Elektronička građa] |c Mariam S. El-Rahmany, Ensaf Hussein Mohamed, Mohamed H. Haggag. |
| 300 | |b Ilustr. | ||
| 504 | |a Bibliografija: | ||
| 504 | |a Summary. | ||
| 520 | |a The targeted attack is one of the social engineering attacks. The detection of this type of attack is considered a challenge as it depends on semantic extraction of the intent of the attacker. However, previous research has primarily relies on the Natural Language Processing or Word Embedding techniques that lack the context of the attacker's text message. Based on Sentence Embedding and machine learning approaches, this paper introduces a model for semantic detection of targeted attacks. This model has the advantage of encoding relevant information, which helps to improve the performance of the multi-class classification process. Messages will be categorized based on the type of security rule that the attacker has violated. The suggested model was tested using a dialogue dataset taken from phone calls, which was manually categorized into four categories. The text is pre-processed using natural language processing techniques, and the semantic features are extracted as Sentence Embedding vectors that are augmented with security policy sentences. Machine Learning algorithms are applied to classify text messages. The experimental results show that sentence embeddings with doc2vec achieved high prediction accuracy 96.8%. So, it outperformed the method applied to the same dialog dataset. | ||
| 700 | 1 | |a Hussein Mohamed, Ensaf |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a H. Haggag, Mohamed |4 aut |9 HR-ZaNSK | |
| 773 | 0 | |t Journal of communications software and systems (Online) |x 1846-6079 |g 17 (2021), 4 ; str. 334-341 |w nsk.(HR-ZaNSK)000644741 | |
| 981 | |b Be2021 | ||
| 856 | 4 | 0 | |u https://doi.org/10.24138/jcomss-2021-0113 |
| 856 | 4 | 0 | |u https://jcoms.fesb.unist.hr/10.24138/jcomss-2021-0113/ |y Elektronička verzija članka |
| 856 | 4 | 0 | |u https://jcoms.fesb.unist.hr/pdfs/v17n4_2021-0113_rahmany.pdf |y Elektronička verzija članka |
| 856 | 4 | 1 | |y Digitalna.nsk.hr |
| 856 | 4 | 0 | |u https://hrcak.srce.hr/270738 |y Elektronička verzija članka |