DOCSLIB.ORG

An Anomaly-Based Botnet Detection Approach for Identifying Stealthy Botnets

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Anomaly-Based Botnet Detection Approach for Identifying Stealthy Botnets An Anomaly-based Botnet Detection Approach for Identifying Stealthy Botnets Sajjad Arshad1, Maghsoud Abbaspour1, Mehdi Kharrazi2, Hooman Sanatkar1 1Electrical and Computer Engineering Department, Shahid Beheshti University; G.C; Tehran, Iran 2 Department of Computer Engineering, Sharif University of Technology; Tehran, Iran [email protected], [email protected], [email protected], [email protected] Abstract—Botnets (networks of compromised computers) resistant to the changes of the C&C mechanisms, we are often used for malicious activities such as spam, click should study inherent characteristics of botnet fraud, identity theft, phishing, and distributed denial of behaviors. Bots connect to the C&C channel and service (DDoS) attacks. Most of previous researches have execute the received commands. Bots belonging to the introduced fully or partially signature-based botnet same botnet receive the same commands that causes detection approaches. In this paper, we propose a fully anomaly-based approach that requires no a priori them having similar netflows characteristics and knowledge of bot signatures, botnet C&C protocols, and performing same attacks. There are netflows that C&C server addresses. We start from inherent present communication between bots and C&C servers characteristics of botnets. Bots connect to the C&C such as binary downloading and sending spam. Also, channel and execute the received commands. Bots there are several types of attacks like scanning and belonging to the same botnet receive the same commands distributed denial of service (DDoS). Our method is that causes them having similar netflows characteristics based on detecting these anomalous behaviors and and performing same attacks. Our method clusters bots finding meaningful relation between these activities to with similar netflows and attacks in different time detect set of bots inside the monitored network. This windows and perform correlation to identify bot infected hosts. We have developed a prototype system and paper makes the following main contributions: evaluated it with real-world traces including normal • We propose an anomaly-based method that traffic and several real-world botnet traces. The results requires no a priori knowledge of bot show that our approach has high detection accuracy and signatures, botnet C&C protocols, and the C&C low false positive. server addresses. • Keywords—Botnet; Netflow; Clustering; Anomaly-based We illustrate a method that can detect bots in Detection the monitored network in real-time. • In addition to detect bots with malicious I. INTRODUCTION activities (e.g. scanning, DDoS), our method Botnet is a collection of compromised hosts (bots) can detect bots that do not perform malicious that are under control of an attacker (botmaster). Bots activities. But, our approach detects bots with are used to send spam emails, host phishing web sites, malicious activities so fast. • cooperate in distributed denial of service (DDoS) We have developed a prototype system based attacks, and other kinds of malicious activities. on our method and evaluated it with real-world Botmaster needs a command and control (C&C) network traces including normal traffic and channel to command the bots and coordinate malicious several real-world botnet traces. activities. Most of botnets C&C channels are using IRC The rest of the paper is organized as follows. We (Internet Relay Chat) protocol. In this protocol, review the related works in section 2. Section 3 botmaster has a real-time communication with the bots. describes the architecture and implementation of the There are also a few botnets that use the HTTP protocol approach. Section 4 evaluates the effectiveness of the for C&C channels [1][2]. In HTTP-based C&C, the approach on various network traces and finally the botmaster does not communicate directly with the bots. paper is concluded in section 5. Instead, the bots periodically contact the C&C server to obtain their commands. These two protocols provide a II. RELATED WORKS centralized C&C mechanism. The main disadvantage of There are several researches that propose different centralized C&C mechanism is the single-point-of- botnet detection approaches. Binkley and Singh [5] failure problem. For example, if IRC or HTTP server is combines IRC statistics and TCP work weight to detect taken down, the botmaster will not be able to IRC-based botnets. Karasaridis [6] used IRC netflows communicate with the bots anymore. Thus, botmasters and scanning activities to detect IRC botnet controllers. began using peer-to-peer (P2P) protocols for C&C Livadas [7] proposed a machine learning based channels. Currently, Storm Worm [3] and Nugache [4] approach which uses network-level traffic features of are the most popular P2P botnets. chat protocols for botnet detection. Rishi [8] is a For designing a botnet detection approach that is signature-based IRC botnet detection approach that Netflows (C&C Communication, Spam Emails, Binary Updating) Attacks (Scanning, DDoS, …) Scanning DDoS Spam Binary Updating C&C Scanning DDoS Time Spam Binary Updating Bot C&C TW1 TW2 TW3 TW4 TW5 Time Bot Figure 1. Similarity in bots behaviors in consecutive time windows finds suspicious nicknames by analyzing IRC-based by using database produced by Domain-IP Mapping botnets traffic. The above approaches are used for only component. Then Netflow Clustering and Alert detecting IRC-based botnets; whereas we do not have Clustering components cluster non-filtered netflows and assumption about botnet C&C protocols. BotHunter [9] alerts. Finally at the end of each time window uses a user-defined bot infection model in order to correlation engine correlates the generated alert clusters correlate alerts (e.g. scanning). BotSniffer [10] is a and netflow clusters in order to detect bot infected centralized botnet C&C detection approach that hosts. The following sections illustrate architecture of performs group analysis across multiple hosts through our approach in detail. Our botnet detection approach different time windows. But, it is used for detecting operates on online traffic and detects bots in real-time. centralized botnets. BotMiner [11] clusters hosts based The following sections will illustrate each component in on their netflow-level statistics (e.g., the byte count, the details. packet count, etc.) and their malicious activities (e.g., A. Traffic Dispatching scanning) and then correlates clusters to find bots. Nevertheless, it works offline and cannot detect bots This component has been designed for separating quickly. BotProbe [12] proposes an active botnet different types of traffic of Domain-IP Mapping, probing technique that detects bots by distinguishing Netflow Generating and Alert Generating components. botnet C&C dialogs from human-human conversations. It delivers DNS traffic to Domain-IP Mapping and TCP The aforementioned approaches are mostly signature- traffic to Netflow Generating component. Also TCP, based and cannot find unknown botnets. UDP and ICMP traffic is delivered to Alert Generating component. Irrelevant traffics like DHCP and ARP will III. BOTNET DETECTION APPROACH be filtered out in order to increase performance. In this section, we illustrate a new botnet detection B. Domain-IP Mapping approach whose goal is to detect groups of bot infected As discussed above, the bots can use the IPs or DNS hosts which belong to the same botnet in a monitored names hard coded in their binary file in order to connect network. Bots belonging to the same botnet have to the C&C servers. This component has been designed similar behaviors during a time window and this to map the domains and IPs to each other. This similarity continues during consecutive time windows component processes DNS queries and responses in as shown in Fig. 1. Our approach finds behavior order to store in database the IP addresses in similarity of hosts in different properties such as correspondence with the queried domain names. This netflow information through a predefined time window database only is used to filter netflow to the sites in the and tries to detect bots by correlating these similar white list as described in Netflow Filtering section. behaviors between different time windows. Fig. 2 shows the architecture of our botnet detection C. Netflow Generating approach, which consists of nine interconnected This component processes TCP traffic and generates the components that analyze traffic online. Traffic netflows between hosts. Most of the routers like Cisco dispatching component delivers traffic to Domain-IP and Juniper have the capability of generating netflows. Mapping, Netflow Generating and Alert Generating Also open source tools like ARGUS exist. We components. Domain-IP Mapping component maps the developed our own efficient tool to generate netflows. DNS domains to corresponded IPs for filtering In addition to simple netflows, our tool is able to purposes. Netflow Generating component generates generate partial netflows (netflows that is not finished TCP netflows between hosts. Alert Generating inside a time windows, is considered as a completed component reports the malicious activities of the hosts netflow, the remained part of the netflow is considered like scanning. The following five components perform as another netflow in next time windows). The most at the end of each time window. Alert Filtering important property of our tool is its efficiency and component filters useless alerts generated by Alert customized netflow record generating. Currently,
Load more

Recommended publications
  • Post-Mortem of a Zombie: Conficker Cleanup After Six Years Hadi Asghari, Michael Ciere, and Michel J.G
    Post-Mortem of a Zombie: Conficker Cleanup After Six Years Hadi Asghari, Michael Ciere, and Michel J.G. van Eeten, Delft University of Technology https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/asghari This paper is included in the Proceedings of the 24th USENIX Security Symposium August 12–14, 2015 • Washington, D.C. ISBN 978-1-939133-11-3 Open access to the Proceedings of the 24th USENIX Security Symposium is sponsored by USENIX Post-Mortem of a Zombie: Conficker Cleanup After Six Years Hadi Asghari, Michael Ciere and Michel J.G. van Eeten Delft University of Technology Abstract more sophisticated C&C mechanisms that are increas- ingly resilient against takeover attempts [30]. Research on botnet mitigation has focused predomi- In pale contrast to this wealth of work stands the lim- nantly on methods to technically disrupt the command- ited research into the other side of botnet mitigation: and-control infrastructure. Much less is known about the cleanup of the infected machines of end users. Af- effectiveness of large-scale efforts to clean up infected ter a botnet is successfully sinkholed, the bots or zom- machines. We analyze longitudinal data from the sink- bies basically remain waiting for the attackers to find hole of Conficker, one the largest botnets ever seen, to as- a way to reconnect to them, update their binaries and sess the impact of what has been emerging as a best prac- move the machines out of the sinkhole. This happens tice: national anti-botnet initiatives that support large- with some regularity. The recent sinkholing attempt of scale cleanup of end user machines.
    [Show full text]
  • The Botnet Chronicles a Journey to Infamy
    The Botnet Chronicles A Journey to Infamy Trend Micro, Incorporated Rik Ferguson Senior Security Advisor A Trend Micro White Paper I November 2010 The Botnet Chronicles A Journey to Infamy CONTENTS A Prelude to Evolution ....................................................................................................................4 The Botnet Saga Begins .................................................................................................................5 The Birth of Organized Crime .........................................................................................................7 The Security War Rages On ........................................................................................................... 8 Lost in the White Noise................................................................................................................. 10 Where Do We Go from Here? .......................................................................................................... 11 References ...................................................................................................................................... 12 2 WHITE PAPER I THE BOTNET CHRONICLES: A JOURNEY TO INFAMY The Botnet Chronicles A Journey to Infamy The botnet time line below shows a rundown of the botnets discussed in this white paper. Clicking each botnet’s name in blue will bring you to the page where it is described in more detail. To go back to the time line below from each page, click the ~ at the end of the section. 3 WHITE
    [Show full text]
  • 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).
    [Show full text]
  • Iptrust Botnet / Malware Dictionary This List Shows the Most Common Botnet and Malware Variants Tracked by Iptrust
    ipTrust Botnet / Malware Dictionary This list shows the most common botnet and malware variants tracked by ipTrust. This is not intended to be an exhaustive list, since new threat intelligence is always being added into our global Reputation Engine. NAME DESCRIPTION Conficker A/B Conficker A/B is a downloader worm that is used to propagate additional malware. The original malware it was after was rogue AV - but the army's current focus is undefined. At this point it has no other purpose but to spread. Propagation methods include a Microsoft server service vulnerability (MS08-067) - weakly protected network shares - and removable devices like USB keys. Once on a machine, it will attach itself to current processes such as explorer.exe and search for other vulnerable machines across the network. Using a list of passwords and actively searching for legitimate usernames - the ... Mariposa Mariposa was first observed in May 2009 as an emerging botnet. Since then it has infected an ever- growing number of systems; currently, in the millions. Mariposa works by installing itself in a hidden location on the compromised system and injecting code into the critical process ͞ĞdžƉůŽƌĞƌ͘ĞdžĞ͘͟/ƚŝƐknown to affect all modern Windows versions, editing the registry to allow it to automatically start upon login. Additionally, there is a guard that prevents deletion while running, and it automatically restarts upon crash/restart of explorer.exe. In essence, Mariposa opens a backdoor on the compromised computer, which grants full shell access to ... Unknown A botnet is designated 'unknown' when it is first being tracked, or before it is given a publicly- known common name.
    [Show full text]
  • Common Threats to Cyber Security Part 1 of 2
    Common Threats to Cyber Security Part 1 of 2 Table of Contents Malware .......................................................................................................................................... 2 Viruses ............................................................................................................................................. 3 Worms ............................................................................................................................................. 4 Downloaders ................................................................................................................................... 6 Attack Scripts .................................................................................................................................. 8 Botnet ........................................................................................................................................... 10 IRCBotnet Example ....................................................................................................................... 12 Trojans (Backdoor) ........................................................................................................................ 14 Denial of Service ........................................................................................................................... 18 Rootkits ......................................................................................................................................... 20 Notices .........................................................................................................................................
    [Show full text]
  • Building an Intrusion Detection System (IDS) to Neutralize Botnet Attacks
    International Journal of Computer Applications Technology and Research Volume 4– Issue 2, 103 - 107, 2015, ISSN:- 2319–8656 Malware Hunter: Building an Intrusion Detection System (IDS) to Neutralize Botnet Attacks R. Kannan A.V.Ramani Department of Computer Science Department of Computer Science Sri Ramakrishna Mission Vidyalaya Sri Ramakrishna Mission Vidyalaya College of Arts and Science College of Arts and Science Coimbatore ,Tamilnadu,India. Coimbatore ,Tamilnadu,India Abstract: Among the various forms of malware attacks such as Denial of service, Sniffer, Buffer overflows are the most dreaded threats to computer networks. These attacks are known as botnet attacks and self-propagating in nature and act as an agent or user interface to control the computers which they attack. In the process of controlling a malware, Bot header(s) use a program to control remote systems through internet with the help of zombie systems. Botnets are collection of compromised computers (Bots) which are remotely controlled by its originator (Bot-Master) under a common Command-and-Control (C&C) structure. A server commands to the bot and botnet and receives the reports from the bot. The bots use Trojan horses and subsequently communicate with a central server using IRC. Botnet employs different techniques like Honeypot, communication protocols (e.g. HTTP and DNS) to intrude in new systems in different stages of their lifecycle. Therefore, identifying the botnets has become very challenging; because the botnets are upgrading their methods periodically for affecting the networks. Here, the focus on addressing the botnet detection problem in an Enterprise Network This research introduces novel Solution to mitigate the malicious activities of Botnet attacks through the Principle of component analysis of each traffic data, measurement and countermeasure selection mechanism called Malware Hunter.
    [Show full text]
  • ITU Botnet Mitigation Toolkit Background Information
    ITU Botnet Mitigation Toolkit Background Information ICT Applications and Cybersecurity Division Policies and Strategies Department ITU Telecommunication Development Sector January 2008 Acknowledgements Botnets (also called zombie armies or drone armies) are networks of compromised computers infected with viruses or malware to turn them into “zombies” or “robots” – computers that can be controlled without the owners’ knowledge. Criminals can use the collective computing power and connected bandwidth of these externally-controlled networks for malicious purposes and criminal activities, including, inter alia, generation of spam e-mails, launching of Distributed Denial of Service (DDoS) attacks, alteration or destruction of data, and identity theft. The threat from botnets is growing fast. The latest (2007) generation of botnets such as the Storm Worm uses particularly aggressive techniques such as fast-flux networks and striking back with DDoS attacks against security vendors trying to mitigate them. An underground economy has now sprung up around botnets, yielding significant revenues for authors of computer viruses, botnet controllers and criminals who commission this illegal activity by renting botnets. In response to this growing threat, ITU is developing a Botnet Mitigation Toolkit to assist in mitigating the problem of botnets. This document provides background information on the toolkit. The toolkit, developed by Mr. Suresh Ramasubramanian, draws on existing resources, identifies relevant local and international stakeholders, and
    [Show full text]
  • Botnet Threat Tracking | Intelligence Services | Kaspersky
    INTELLIGENCE SERVICES: BOTNET THREAT TRACKING Expert monitoring and notification services to identify botnets threatening your reputation and customers Many network attacks are organized using botnets. Such attacks might target casual Internet users, but often these threats are aimed at specific organizations and their online customers. Kaspersky Lab’s expert solution tracks the activity of botnets and provides real-time notifications of threats associated with specific enterprise brands. You can use this information to advise and inform your customers, security services providers and law enforcement about current threats. Protect your organization’s reputation and customers today with Kaspersky Lab’s Botnet Threats Notification Service. TAKE ACTION WITH REAL-TIME DELIVERABLES: The service provides a subscription to personalized email or JSON format notifications containing intelligence about matching brand names by tracking keywords in the botnets monitored by Kaspersky Lab. Notifications include: Targeted URL of the botnet — Bot malware is designed to wait until the user accesses the URL(s) of the targeted organization and then starts the attack rule. Botnet type — Understand exactly what malware threat is being employed by the cybercriminal to affect your customers. Examples include Zeus, SpyEye, and Citadel. Attack type — Identify what the cybercriminals are using the malware to do; for example, web data injection, keylogging, screen wipes or video capture. Attack rules — Know what different rules of web code injection are being used such as HTML requests (GET / POST), data of web page before injection, data of web page after injection. Command and Control (C&C) server address — Enables you to notify the Internet service provider of the offending server for faster dismantling of the threat.
    [Show full text]
  • Top 5 Least Wanted Malware
    Halloween Edition WHAT IS MALWARE? Malware is an abbreviated term meaning “malicious software.” This is software that is specically designed to gain access or damage a computer without the knowledge of the owner. TOP 5 LEAST WANTED MALWARE Ransomware Botnet POS Malware RAT Browser-based 5 5 Malware 5 5 5 $ $ $ 4 $ (malware that restricts access 4 ur computer while to yo y a ransom demanding you pa 4 (also known as a zombie army) to get access back) 4 (a malicious software expressly 4 written to steal customer payment data) (Remote Access Trojan) (also known as “Man in the browser”) 2 4 1 3 5 Remote Access Trojan or RAT A malware program that includes a 1 back door for administrative control over the target computer. RATs are usually downloaded invisibly with a user-requested program – such as a game – or sent as an email attachment. Botnet (also known as a zombie army) is a number of Internet computers that, although their owners are unaware of it, have been set up to forward transmissions (including spam or viruses) to other computers on the Internet. Browser-based malware (also known as “Man in the browser”) is a security attack where the perpetrator 3 installs a trojan horse on a victim's computer that's capable of modifying that user's web transactions as they occur in real time. Ransomware A form of computer malware that restricts access to your 4 computer or its information, while demanding you pay a ransom to get access back. $ POS malware $ $ A malicious software $ expressly written to steal customer payment data – especially credit card data – from retail checkout.
    [Show full text]
  • The Botnet As a Vehicle for Online Crime
    Botnets as a Vehicle for Online Crime Aaron Hackworth Nicholas Ianelli © 2006 Carnegie Mellon University Agenda Overview of CERT®/CC How botnets are built What capabilities botnets possess How botnets are operated How botnets are maintained and defended Tracking Botnets and Bot Herders © 2006 Carnegie Mellon University 2 CERT®/CC Teams Vulnerability Artifact Handling Analysis Analyze flaws Study intruder in Internet developed Systems code to exploit flaws Incident Handling Measure exploitation of flaws, assist in remediation © 2006 Carnegie Mellon University 3 What You Need To Understand It is about the underground economy – Information Theft –Extortion It is about the money Malware and malicious techniques evolve as quickly as necessary to maintain or create new revenue streams The Underground Motto… ”Just enough is good enough” © 2006 Carnegie Mellon University 4 How Botnets are Built © 2006 Carnegie Mellon University Building from scratch Vulnerability exploitation – Scan – Exploit Social Engineering – Collecting a target list • Web Client Attacks • Email Attacks • Instant Messaging Attacks © 2006 Carnegie Mellon University 6 Hijacking, Purchasing, Trading Hijacking – Many botnets include packet sniffers Underground economy – Purchase – Trade © 2006 Carnegie Mellon University 7 Botnet Capabilities © 2006 Carnegie Mellon University Botnet capabilities Scanning/Autorooting © 2006 Carnegie Mellon University 9 Botnet capabilities (2) DDoS –Extortion Download and Installation – Malicious Executables – Spyware/Adware © 2006
    [Show full text]
  • Android Botnet: an Upcoming Challenge
    IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, PP 05-10 www.iosrjournals.org Android Botnet: An Upcoming Challenge Sangeeta Joshi1, Dr Ravinder Khanna2, Lalit Kumar Joshi3 1 (Department of Computer Science, Mata Gujri College, Fatehgarh Sahib, India) 2(Department of Electronics, Maharishi Markandeshwar University, Sadopur, India) 3 (Department of Computer Science, Mata Gujri College, Fatehgarh Sahib, India) ABSTRACT: Mobile devices are now a day well incorporated with advanced capabilities and technologies such as the Internet, GPS, and Bluetooth etc. Mobile security has become a globally sensitive issue because of the high usage of mobile devices. But as compared to computers and computer networks, they are less protected. The user’s of these devices pay less attention to the security updates. The most popular mobile operating system today in the industry is Android. However, with the growing market share of Android smart phone, the malware (malicious software) writers have begun to target the Android operating system. Recently, the most dangerous threat which is targeting Android is ‘botnets’. In this paper, we present an introduction of Android botnets and an analysis of currently available families of botnets. By analyzing these samples we reveal common characteristics and behavior of these botnets which will aid in the identification of new malware on Android devices. Keywords – Android, Application, Botnet, Malware, Mobile, I. INTRODUCTION The appearance of new technologies and advance features of mobile devices makes mobile communication an essential part of every aspect of human activity. Mobile devices are now well integrated with the Internet; therefore, with the increasing use of these devices on a global scale, mobile security has become a fundamental issue [1].
    [Show full text]
  • Cyber Risk – Common Threats Part 1 of 2
    Cyber Risk – Common Threats Part 1 of 2 Table of Contents Threats to Information Systems ..................................................................................................... 2 Malware .......................................................................................................................................... 4 Viruses ............................................................................................................................................. 5 Virus Examples ................................................................................................................................ 6 Worms ............................................................................................................................................. 8 Brief Virus and Worm History ......................................................................................................... 9 Downloaders ................................................................................................................................. 11 Attack Scripts ................................................................................................................................ 13 Botnet -1 ....................................................................................................................................... 15 Botnet -2 ....................................................................................................................................... 17 IRCBotnet Example ......................................................................................................................
    [Show full text]