Secure the Internet, one home at a time. Issue 16 (19th July 2016)

• Record Type: Journal Article
• Authors: Xu, Kuai; Wang, Feng; Jia, Xiaohua
• Is Part Of: Security and communication networks. Volume 9:Issue 16(2016)
• Page Start: 3821
• Page End: 3832
• Publication Date: 2016-07-19
• Subjects: home network security -- traffic analysis -- cyber threats; Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1002/sec.1569
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract: The rapid growth of residential broadband connections and Internet‐enabled home devices have driven the success of many useful applications such as video streaming and remote healthcare. However, poorly managed routers and connected devices in the home are vulnerable under persistent threats and exploitations from cyber attackers across the Internet who continuously identify, compromise, and control devices as part of botnets for launching click fraud, denial of service attacks, spam campaigns. These growing threats and broad damages have made it imperative to understand, characterize, filter, and reduce exploit traffic towards millions of home routers and billions of connected devices in the home. This paper presents a bloom‐filter based analytics framework to capture persistent threats towards the same home routers and to identify correlated attacks towards distributed home networks. Our experimental results based on network traffic collected from real homes over 18months have revealed a number of interesting findings on persistent and correlated threats towards home networks, which calls for improved security and management of home networks. To the best of our knowledge, this paper is the first effort to characterize cyber threats towards home networks and to propose a simple and yet effective approach to identify persistent and aggressive attacks towards home networks. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : Key Findings: We study the intensity and diversity of unwanted traffic towards distributed home networks via data sets collected from five real home networks over an 18‐month time span. We explore counting bloom filter to identify cyber attackers of persistent threats towards home routers and connected devices and characterize behavioral patterns of these attackers. We demonstrate the benefits of compressed bloom filters for sharing and correlating aggressive attackers across distributed home networks.