• Title/Summary/Keyword: Powershell

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Construction of IoT Environment for XMPP Protocol Based Medical Devices Using Powershell (Powershell을 이용한 안전한 XMPP 프로토콜 기반의 의료기기 IoT환경 구축 제안)

  • Park, Yeon-Jin;Lee, Kuen-Ho
    • Journal of Internet of Things and Convergence
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    • v.2 no.2
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    • pp.15-20
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    • 2016
  • MicroSoft Windows 10 IoT version, released in August 2015, successfully drew consumer interest by introducing the familiar Windows into the IoT market, and enabled an easier system construction of IoT web servers. Meanwhile, overdiagnosis has recently emerged as a controversy in medical society. Establishment of communication between IoT servers and medical devices will send treatment results to users and activate communication between hospitals, greatly reducing this problem. The IoT server, with its limited resources, utilizes lightweight protocols that do not generate traffic and are easy to use. This paper proposes IoT networks which will enable medical devices to easily provide ubiquitous environments to their users, through utilization of the lightweight Simple Service Discovery Protocol (SSDP) and the secure Extensible Messaging and Presence Protocol (XMPP).

Deobfuscation Processing and Deep Learning-Based Detection Method for PowerShell-Based Malware (파워쉘 기반 악성코드에 대한 역난독화 처리와 딥러닝 기반 탐지 방법)

  • Jung, Ho-jin;Ryu, Hyo-gon;Jo, Kyu-whan;Lee, Sangkyun
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.3
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    • pp.501-511
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    • 2022
  • In 2021, ransomware attacks became popular, and the number is rapidly increasing every year. Since PowerShell is used as the primary ransomware technique, the need for PowerShell-based malware detection is ever increasing. However, the existing detection techniques have limits in that they cannot detect obfuscated scripts or require a long processing time for deobfuscation. This paper proposes a simple and fast deobfuscation method and a deep learning-based classification model that can detect PowerShell-based malware. Our technique is composed of Word2Vec and a convolutional neural network to learn the meaning of a script extracting important features. We tested the proposed model using 1400 malicious codes and 8600 normal scripts provided by the AI-based PowerShell malicious script detection track of the 2021 Cybersecurity AI/Big Data Utilization Contest. Our method achieved 5.04 times faster deobfuscation than the existing methods with a perfect success rate and high detection performance with FPR of 0.01 and TPR of 0.965.