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Generation of an Iot Botnet Dataset in a Medium-Sized Iot Network

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Generation of an Iot Botnet Dataset in a Medium-Sized Iot Network MedBIoT: Generation of an IoT Botnet Dataset in a Medium-sized IoT Network Alejandro Guerra-Manzanares a, Jorge Medina-Galindo, Hayretdin Bahsi b and Sven Nomm˜ c Department of Software Science, Tallinn University of Technology, Tallinn, Estonia falejandro.guerra, hayretdin.bahsi, [email protected], [email protected] Keywords: Botnet, Internet of Things, Dataset, Intrusion Detection, Anomaly Detection, IoT. Abstract: The exponential growth of the Internet of Things in conjunction with the traditional lack of security mecha- nisms and resource constraints associated with these devices have posed new risks and challenges to security in networks. IoT devices are compromised and used as amplification platforms by cyber-attackers, such as DDoS attacks. Machine learning-based intrusion detection systems aim to overcome network security lim- itations relying heavily on data quantity and quality. In the case of IoT networks these data are scarce and limited to small-sized networks. This research addresses this issue by providing a labelled behavioral IoT data set, which includes normal and actual botnet malicious network traffic, in a medium-sized IoT network infrastructure (83 IoT devices). Three prominent botnet malware are deployed and data from botnet infec- tion, propagation and communication with C&C stages are collected (Mirai, BashLite and Torii). Binary and multi-class machine learning classification models are run on the acquired data demonstrating the suitability and reliability of the generated data set for machine learning-based botnet detection IDS testing, design and deployment. The generated IoT behavioral data set is released publicly available as MedBIoT data set∗. 1 INTRODUCTION lam, 2017; Bosche et al., 2018; Pratt, 2019). Their ubiquity will pose a major challenge to secu- The adoption of the Internet on an increasing wider rity as IoT devices have traditionally lacked of proper scope, i.e., providing connectivity capabilities to ev- control measures and proactive security management eryday objects, is a reality. In fact, the rise of the (e.g., usage of default passwords, no firmware up- Internet of Things (IoT) has just begun and it is ex- dates, no access control policy), featuring them as pected to have a major increase in the near future. It high vulnerable and prone to be compromised devices was estimated that there would be 26.66 billion active (Bertino and Islam, 2017). These features have been IoT devices by 2019, a figure that may be increased exploited by malicious actors, being able to compro- up to 75 billion by 2025 (Statista, 2019). 127 new mise the defenseless devices by exploiting its vulnera- IoT devices are connected to the Internet every second bilities, gaining remote access and using them as mag- (McKinsey, 2017) in a wide range of applications, nification platforms for their massive attacks (Kolias from factories and smart cities sensors to healthcare et al., 2017). An IoT botnet is just a particular type of and car products. The market size is calculated to botnet in which the compromised devices are IoT de- grow over $212 billion by 2019 and reach $1.6 trillion vices, thus showing analogous scheme and dynamics by 2025 (Liu, 2019). However, the adoption of the to computer botnets. In this regard, when a vulnera- bot IoT technology still poses usability concerns even to ble device is compromised it becomes a , a mem- early adopters and eager customers, related to device ber of a larger community of compromised devices, botnet security and data privacy issues (Bosche et al., 2018; called , under the control of a malicious actor, botmaster Sklavos et al., 2017). Thus, despite its huge growth, the . The botmaster has remote access and the Internet of Things market explosion is still being control of the bot over the Internet, without the con- limited by its main barrier: security (Bertino and Is- sent and awareness of the actual owner of the com- promised device, using a Command&Control (C&C) server (Silva et al., 2013). Botnets have been used a https://orcid.org/0000-0002-3655-5804 to perpetrate a wide range of malicious attacks, from b https://orcid.org/0000-0001-8882-4095 c https://orcid.org/0000-0001-5571-1692 ∗Available at https://cs.taltech.ee/research/data/medbiot massive SPAM and phishing campaigns to distributed As already stated, the Internet of Things is a real- denial-of-service (DDoS), the most common usage of ity that will become ubiquitous in the following years. a botnet. A DDoS attack targets the availability of This fact combined with the lack of proper security online resources, such as websites or services. The measures and devices inherent vulnerabilities make main goal is to saturate the targeted server or network IoT devices an easy and appealing target for cyber with more traffic than it can handle (e.g., receiving attackers (Bertino and Islam, 2017). Thus, proper an overwhelming amount of messages, connection re- data are in need to create machine learning-based ef- quests or forged packets) thus provoking the service fective detection systems that may help to overcome or website to crash and become unavailable to legiti- these limitations. In this regard, there is a remarkable mate users requests (Weisman, 2019). lack of available data sets that might help to build ef- fective IDSs in IoT networks. This research aims to 1.1 Data Sets for IoT Anomaly fill this significant gap in IoT anomaly-based IDSs by Detection providing a novel IoT data set obtained from medium size IoT network architecture (more than 80 devices), which includes normal and malicious behavior from The phenomenon of botnet detection in computer net- different devices (real and emulated) and the deploy- works has been widely studied (Garcia et al., 2014; ment of prominent IoT botnets (Mirai, BashLite and Feily et al., 2009), with many available data sets at Torii). The scale extension enables to capture mal- hand (Shiravi et al., 2012), while the most recent IoT ware spreading patterns that cannot be seen in small- network botnet phenomenon has not received the re- sized networks, thus providing a more realistic envi- quired attention yet, showing a remarkable lack of ronment. Additionally, this data set includes the be- available data sources. Data sets for building effective IoT anomaly de- havior of Torii botnet malware which has not been tection methods rely on the acquisition of both le- addressed in any other data set before. Finally, this gitimate (normal) and malicious (botnet) behavioral data set provides data for the first stages of botnet de- data from IoT networks. Anomaly models are built ployment (i.e., infection, propagation and communi- and trained using only legitimate data to establish the cation with C&C server stages), thus complementing so-called normality patterns. The induced models are the available data sets which mainly focus on attack tested using legitimate and malicious data, where the detection, the main outcome and part of the last stages metrics related to model’s detection performance are of the botnet lifecycle (Hachem et al., 2011; Kiruba- evaluated. Therefore, proper and complete data are vathi and Anitha, 2014). key components for a high-performance effective in- This paper is structured as follows: Section 2 pro- trusion detection system (IDS). Table 1 summarizes vides background information and a review of re- the available data sets for IoT anomaly-based intru- lated literature, Section 3 explains the methodology sion detection systems. As can be observed, a small followed to implement the experimental setup while amount of data sets are available for the specific IoT Section 4 offers a detailed overview of the main out- botnets issue. The available data sets are focused on come of this study, a novel IoT data set for botnet small-sized IoT networks, reflecting the behavior of detection, and its verification. Finally, Section 5 con- a small set of IoT devices. Additionally, a specific cludes the study and highlights its main contributions. and small variety of devices are used (mostly security cameras) limiting the scope of the IoT devices ana- lyzed from the broad domain of available IoT devices. 2 LITERATURE REVIEW & None of the available IoT data sets combine real and emulated devices, which limit the scope of their re- BACKGROUND INFORMATION sults to either real or emulated devices. In this regard, our generated data set combines real and emulated de- 2.1 Botnets & DDoS Attacks vices, using different but common types of IoT de- vices, not investigated by previous data sets (i.e., fans, Botnets have been used to perpetrate record-breaking locks, light bulbs and switches), in a medium-sized DDoS attacks. In this regard, in 2016, the journalist network composed of more than 80 devices. Fur- Brian Krebs was the target of a record-breaking attack thermore, our data set focuses on the first stages of (620 Gbps) to its blog KrebsOnSecurity.com, specifi- a botnet deployment, such as infection and propaga- cally tailored to take the site offline (Krebs, 2016). A tion, while the rest of the data sets focus on the last month later, the french hosting provider OVH was at- stages of the botnet lifecycle, mainly detection of at- tacked by the same botnet (probably BashLite), reach- tacks (Kirubavathi and Anitha, 2014). ing 1 Tbps and involving over 140.000 compromised Table 1: Data sets for IoT Anomaly-based IDS. Number Real or Network Data set Botnet Device type Data set features Date Reference of devices Emulated Size Doorbell (Meidan et al., Webcam Mirai 2018a) N-Baiot 9 Thermostat Real Small 115 - statistics 2018 BashLite Baby monitor (Meidan et al., Security Camera 2018b) Hajime Aidra (Bezerra et al., IoT host-based BashLite Multimedia Center NA - PCAP & 2018a) datasets for ID Mirai 2 Emulated Small 2018 Security Camera Netflow/Host (Bezerra et al., research Doflo Tsunami 2018b) Wroba IoT Network Speaker (Kang et al., Mirai 2 Real Small NA - PCAP 2019 Intrusion Dataset Wi-Fi Camera 2019) Refrigerator (Moustafa, No actual Smart Garage door 2019) Bot-IoT malware - 5 Weather Monitoring Emulated Small 31+14 - flow 2019 (Koroniotis simulated Smart Lights Smart thermostat et al., 2019) cameras/dvr (Pritchard, 2018).
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