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2022-07-12
Ivanov, Michael A., Kliuchnikova, Bogdana V., Chugunkov, Ilya V., Plaksina, Anna M..  2021.  Phishing Attacks and Protection Against Them. 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). :425—428.
Phishing, ransomware and cryptojacking are the main threats to cyber security in recent years. We consider the stages of phishing attacks, examples of such attacks, specifically, attacks using ransomware, malicious PDF files, and banking trojans. The article describes the specifics of phishing emails. Advices on phishing protection are given.
2020-09-04
Asish, Madiraju Sairam, Aishwarya, R..  2019.  Cyber Security at a Glance. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:240—245.
The privacy of people on internet is getting reduced day by day. Data records of many prestigious organizations are getting corrupted due to computer malwares. Computer viruses are becoming more advanced. Hackers are able penetrate into a network and able to manipulate data. In this paper, describes the types of malwares like Trojans, boot sector virus, polymorphic virus, etc., and some of the hacking techniques which include DOS attack, DDoS attack, brute forcing, man in the middle attack, social engineering, information gathering tools, spoofing, sniffing. Counter measures for cyber attacks include VPN, proxy, tor (browser), firewall, antivirus etc., to understand the need of cyber security.
2018-04-02
Alkhateeb, E. M. S..  2017.  Dynamic Malware Detection Using API Similarity. 2017 IEEE International Conference on Computer and Information Technology (CIT). :297–301.

Hackers create different types of Malware such as Trojans which they use to steal user-confidential information (e.g. credit card details) with a few simple commands, recent malware however has been created intelligently and in an uncontrolled size, which puts malware analysis as one of the top important subjects of information security. This paper proposes an efficient dynamic malware-detection method based on API similarity. This proposed method outperform the traditional signature-based detection method. The experiment evaluated 197 malware samples and the proposed method showed promising results of correctly identified malware.