• Title/Summary/Keyword: 맬웨어

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Study on Security Measures of e-Gov with Dynamic ICT Ecosystem (동적인 ICT 생태계에 따른 전자정부 보안대책 연구)

  • Choung, Young-Chul;Bae, Yong-Guen
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.6
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    • pp.1249-1254
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    • 2014
  • As ICT ecosystem changes, security-related threat on individuals and corporations has increased. With the recent sophistication of hacking strategy, hacking serves commerce and its scale becomes larger than ever. Accordingly, the analysis on cyber intrusion is required. As a number one electronic government around the world, the government's role for security solution for realization of safe electronic government. This manuscript analyzes cyber intrusion cases, speculates the government's measures and suggests political recommendation for the current phenomena.

A Study on Malicious Code Detection Using Blockchain and Deep Learning (블록체인과 딥러닝을 이용한 악성코드 탐지에 관한 연구)

  • Lee, Deok Gyu
    • KIPS Transactions on Computer and Communication Systems
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    • v.10 no.2
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    • pp.39-46
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    • 2021
  • Damages by malware have recently been increasing. Conventional signature-based antivirus solutions are helplessly vulnerable to unprecedented new threats such as Zero-day attack and ransomware. Despite that, many enterprises have retained signature-based antivirus solutions as part of the multiple endpoints security strategy. They do recognize the problem. This paper proposes a solution using the blockchain and deep learning technologies as the next-generation antivirus solution. It uses the antivirus software that updates through an existing DB server to supplement the detection unit and organizes the blockchain instead of the DB for deep learning using various samples and forms to increase the detection rate of new malware and falsified malware.

The Traffic Analysis of P2P-based Storm Botnet using Honeynet (허니넷을 이용한 P2P 기반 Storm 봇넷의 트래픽 분석)

  • Han, Kyoung-Soo;Lim, Kwang-Hyuk;Im, Eul-Gyu
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.19 no.4
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    • pp.51-61
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    • 2009
  • Recently, the cyber-attacks using botnets are being increased, Because these attacks pursue the money, the criminal aspect is also being increased, There are spreading of spam mail, DDoS(Distributed Denial of Service) attacks, propagations of malicious codes and malwares, phishings. leaks of sensitive informations as cyber-attacks that used botnets. There are many studies about detection and mitigation techniques against centralized botnets, namely IRC and HITP botnets. However, P2P botnets are still in an early stage of their studies. In this paper, we analyzed the traffics of the Peacomm bot that is one of P2P-based storm bot by using honeynet which is utilized in active analysis of network attacks. As a result, we could see that the Peacomm bot sends a large number of UDP packets to the zombies in wide network through P2P. Furthermore, we could know that the Peacomm bot makes the scale of botnet maintained and extended through these results. We expect that these results are used as a basis of detection and mitigation techniques against P2P botnets.

A study on secure electronic financial transactions in the endpoint environment infected with malware (Malware에 감염된 Endpoint환경에서 안전한 전자금융거래)

  • Lee, YeonJae;Lee, HeeJo
    • Proceedings of the Korea Information Processing Society Conference
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    • 2014.11a
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    • pp.405-408
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    • 2014
  • 유무선 인터넷이 보편화되고 이용이 확산되면서 금융권에서는 고객의 편의성 증진을 위해 영업점의 상당한 업무를 인터넷뱅킹과 모바일뱅킹 등을 이용하여 처리할 수 있는 IT환경을 제공하고 있다. 이러한 Endpoint 환경의 변화는 점점 더 지능화되고 있는 사이버 공격 기술로 보안 위협이 증대되고 있는 실정이다. 이를 해결하기 위한 방법 중의 하나로 본 연구에서는 Reverse sandboxing 기술과 화이트리스트 기반의 보안 기술이 내장된 커널 수준의 TSX(Trusted Security Extension)기술을 통하여 맬웨어가 감염된 상태에서도 안전하게 전자금융거래를 할 수 있는 Endpoint 환경을 제공한다.

A Study on Email Security through Proactive Detection and Prevention of Malware Email Attacks (악성 이메일 공격의 사전 탐지 및 차단을 통한 이메일 보안에 관한 연구)

  • Yoo, Ji-Hyun
    • Journal of IKEEE
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    • v.25 no.4
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    • pp.672-678
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    • 2021
  • New malware continues to increase and become advanced by every year. Although various studies are going on executable files to diagnose malicious codes, it is difficult to detect attacks that internalize malicious code threats in emails by exploiting non-executable document files, malicious URLs, and malicious macros and JS in documents. In this paper, we introduce a method of analyzing malicious code for email security through proactive detection and blocking of malicious email attacks, and propose a method for determining whether a non-executable document file is malicious based on AI. Among various algorithms, an efficient machine learning modeling is choosed, and an ML workflow system to diagnose malicious code using Kubeflow is proposed.