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Behavioral Malware Detection in Delay Tolerant Networks

机译:时延容忍网络中的行为恶意软件检测

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The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs ("insufficient evidence versus evidence collection risk" and "filtering false evidence sequentially and distributedly"), and propose a simple yet effective method, look ahead, to address the challenges. Furthermore, we propose two extensions to look ahead, dogmatic filtering, and adaptive look ahead, to address the challenge of "malicious nodes sharing false evidence." Real mobile network traces are used to verify the effectiveness of the proposed methods.
机译:对于配备了短距离通信技术(例如蓝牙,NFC和Wi-Fi Direct)的现代移动消费类电子产品,延迟延迟网络(DTN)模型正在成为一种替代传统基础设施模型的可行通信替代方法。邻近恶意软件是一类利用DTN的机会联系和分布式性质进行传播的恶意软件。恶意软件的行为表征是检测恶意软件时模式匹配的有效替代方法,尤其是在处理多态或混淆的恶意软件时。在本文中,我们首先提出一种基于朴素贝叶斯模型的邻近恶意软件的一般行为特征,该特征已成功应用于非DTN设置中,例如过滤电子邮件垃圾邮件和检测僵尸网络。我们确定了将贝叶斯恶意软件检测扩展到DTN的两个独特挑战(“证据不足与证据收集风险”和“顺序地和分布式地过滤虚假证据”),并提出了一种简单而有效的方法,展望未来,以应对这些挑战。此外,我们提出了两种扩展方式,即前瞻性过滤和适应性前瞻,以应对“恶意节点共享虚假证据”的挑战。真实的移动网络轨迹用于验证所提出方法的有效性。

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