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An Intelligent Improvement of Internet-Wide Scan Engine for Fast Discovery of Vulnerable Iot Devices

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An Intelligent Improvement of Internet-Wide Scan Engine for Fast Discovery of Vulnerable Iot Devices S S symmetry Article An Intelligent Improvement of Internet-Wide Scan Engine for Fast Discovery of Vulnerable IoT Devices Hwankuk Kim ID , Taeun Kim and Daeil Jang * Korea Internet & Security Agency, 9, Jinheung-gil, Naju-si, Jeollanam-do 58324, Korea; [email protected] (H.K.); [email protected] (T.K.) * Correspondence: [email protected]; Tel.: +82-61-820-1274 Received: 31 March 2018; Accepted: 7 May 2018; Published: 10 May 2018 Abstract: Since 2016, Mirai and Persirai malware have infected hundreds of thousands of Internet of Things (IoT) devices and created a massive IoT botnet, which caused distributed denial of service (DDoS) attacks. IoT malware targets vulnerable IoT devices, which are vulnerable to security risks. Techniques are needed to prevent IoT devices from being exploited by attackers. However, unlike high-performance PCs, IoT devices are lightweight, low-power, and low-cost, having performance limitations regarding processing and memory, which makes it difficult to install security and anti-malware programs. Recently, several studies have been attempted to quickly search for vulnerable internet-connected devices to solve this real issue. Issues yet to be studied still exist regarding these types of internet-wide scan technologies, such as filtering by security devices and a shortage of collected operating system (OS) information. This paper proposes an intelligent internet-wide scan model that improves IP state scanning with advanced internet protocol (IP) randomization, reactive protocol (port) scanning, and OS fingerprinting scanning, applying k* algorithm in order to find vulnerable IoT devices. Additionally, we describe the experiment’s results compared to the existing internet-wide scan technologies, such as ZMap and Shodan. As a result, the proposed model experimentally shows improved performance. Although we improved the ZMap, the throughput per minute (TPM) performance is similar to ZMap without degrading the IP scan throughput and the performance of generating a single IP address is about 118% better than ZMap. In the protocol scan performance experiments, it is about 129% better than the Censys based ZMap, and the performance of OS fingerprinting is better than ZMap, with about 50% accuracy. Keywords: IoT; security; machine learning; vulnerability; intelligent security 1. Introduction Gartner, Inc. forecasts that 8.4 billion connected things will be in use, worldwide, in 2017, up 31% from 2016, and will reach 20.4 billion by 2020. The total spending on endpoints and services will reach nearly $2 trillion in 2017 [1]. Meanwhile, it has become a reality that vulnerable IoT devices (CCTV, etc.) are frequently involuntarily involved in DDoS (distributed denial of service) attacks. In October 2016, the DNS service provider Dyn took down hundreds of websites—including Twitter, Netflix, and The New York Times—for several hours, due to IoT devices being infected with Mirai malware. As shown in Figure1, Mirai malware primarily spreads by first infecting devices such as webcams, DVRs, and routers. It then deduces the administrative vulnerabilities of other IoT devices by means of brute force attack. Mirai mutations, such as Persia Lee, Ripper, and Bricker are generated daily [2,3]. The cause of IoT device infection by malware is mainly from security vulnerability management. This means IoT devices are used as cut-down OS, with no security functions, or are simply operated with a default ID and password. Cisco (2016) reported that more than 90% of network devices were running with known vulnerabilities, and there were 28 vulnerabilities per device on average [4]. Symmetry 2018, 10, 151; doi:10.3390/sym10050151 www.mdpi.com/journal/symmetry Symmetry 2018,, 10,, x 151 FOR PEER REVIEW 2 2of of 15 16 running with known vulnerabilities, and there were 28 vulnerabilities per device on average [4]. In additionIn addition,, according according to toa report a report by by HP HP (2015), (2015), 80% 80% of of IoT IoT devices devices have have a a default default password password that that is vulnerable toto privacyprivacy leakage, leakage, 70% 70% of of them them are are not not encrypted encrypted during during communication, communication, and 60%and of60% them of themhave ahave weak a weak webinterface, web interface and have, and nothave been not updatedbeen updated for security for security [5]. [5]. Figure 1. Operation of Mirai malware and DDoS (distributed denial of service) attacks by infected IoT Figure 1. Operation of Mirai malware and DDoS (distributed denial of service) attacks by infected botnet. IoT botnet. Today, there have been various “security by design” approaches for design-secure IoT devices and applicationsToday, there have[6], and been intelligent various “security and secure by design” monitoring approaches of hierarchical for design-secure topology IoT smart devices home and appapplicationsliances and [6], andenvironments intelligent and[7,8 secure] by applying monitoring lightweight of hierarchical encryption, topology authentication, smart home appliances secure communicationand environments, and [7 ,intrusion8] by applying detection lightweight [9–12]. The encryption, most general authentication, approach is secure to install communication, an antivirus programand intrusion in an detectionIoT device. [9 –However,12]. The most since general IoT devices approach are not is to high install-performance an antivirus devices program, like in PCs an, IoT or mobiledevice. devices However,, it sinceis not IoT easy devices to eliminate are not high-performancesecurity vulnerabilities devices, and like manage PCs, or periodic mobile devices,updates itby is installingnot easy toan eliminate antivirus securityprogram vulnerabilities or other malware and detection manage periodictechnology updates. Additionally, by installing low- anend antivirus devices areprogram incapable or other of performing malware heavier, detection conventional technology. cryptographic Additionally, algorithms low-end devices, due to their are incapableconstrained of resourcesperforming [13 heavier,,14]. conventional cryptographic algorithms, due to their constrained resources [13,14]. FromFrom the viewpoint of the security vulnerability management management of of IoT IoT devices, devices, there are various reasonsreasons as to to why why it it is is d difficultifficult to to eliminate eliminate security security vulnerabilities. vulnerabilities. First, since since IoT IoT devices devices have have been been operatingoperating for for a a long long period period of of time time by by using using open open source source code code and and old old communication communication technologies, technologies, theythey are are running with old vulnerabilities. Second, Second, since since IoT IoT devices devices have have h hard-codedard-coded software software or or havehave no no automatic automatic update update features features,, like like PCs PCs,, when when vulnerabilities vulnerabilities in in the the embedded embedded OS OS and and firmware firmware areare detected, it it is difficult difficult to patch them quickly. Third, since IoT device manufacturers are small enterprisesenterprises,, if if the the company company goes goes down down,, it it is is difficult difficult to to provide provide follow follow-up-up service services,s, such such as as a a security security updateupdate,, and security vulnerabilities are left unresolved [[1155].]. Recently,Recently, therethere havehave been been many many studies studies on on internet-wide internet-wide vulnerability vulnerability scanning, scanning such, such as ZMap as ZMap [16] [and16] and Shodan Shodan [17 ],[1 where7], where connected connected vulnerable vulnerable IoT IoT devices devices are are scanned scanned in in real real time,time, andand stored in aa database database,, and and their their search search results results are are shared. shared. In this In study, this study, we pr weopose propose a model a modeldesigned designed to collect to IoTcollect device IoT information device information in order to in detect order vulnerabilities to detect vulnerabilities in IoT devices in IoTconnec devicested to connectedthe internet to, and the compareinternet, andits performance compare its performancewith existing with technology. existing technology.The structure The of structure this paper of thisinvolves paper Section involves 2 explainingSection2 explaining the concept the and concept existing and stud existingies on studies passive on vulnerability passive vulnerability scanning technology scanning technology.. Section 3 presentsSection3 thepresents passive the vulnerability passive vulnerability scanning scanningengine model, engine which model, has which been hasimproved been improved from the fromopen sourcethe open ZMap source, and ZMap, explains and explains the scanning the scanning algorithm algorithm and andOS OSfingerprinting fingerprinting identification identification and and classificationclassification algorithm algorithm.. Section Section 4 explains the method of measuring its performance and the results results.. SectionSection 55 presentspresents aa comparisoncomparison withwith existingexisting technologies,technologies, suchsuch asas ZMapZMap andand Shodan,Shodan, andand explainsexplains futurefuture study study.. Symmetry 2018, 10, 151 3 of 16 Symmetry 2018, 10, x FOR PEER REVIEW 3 of 15 2. Related Related Work Workss 2.1. The The Concept Concept of of Internet Internet-Wide-Wide Vulnerability Scanning IoTIoT malware malware targets targets vulnerable vulnerable
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