WHY ROUTERS ARE the NEW BULLSEYE in CYBER ATTACKS Anurag Shandilya K7 Computing, India
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Internet Infrastructure Review Vol.34
Internet Infrastructure Review Mar.2017 Vol. 34 Infrastructure Security Ursnif (Gozi) Anti-Analysis Techniques and Methods for Bypassing Them Technology Trends The Current State of Library OSes Internet Infrastructure Review March 2017 Vol.34 Executive Summary ............................................................................................................................ 3 1. Infrastructure Security .................................................................................................................. 4 1.1 Introduction ..................................................................................................................................... 4 1.2 Incident Summary ........................................................................................................................... 4 1.3 Incident Survey ...............................................................................................................................11 1.3.1 DDoS Attacks ...................................................................................................................................11 1.3.2 Malware Activities ......................................................................................................................... 13 1.3.3 SQL Injection Attacks ..................................................................................................................... 17 1.3.4 Website Alterations ....................................................................................................................... -
Evolution of Iot Attacks
Evolution of IoT Attacks By 2025, there will be 41.6 billion connected IoT devices, generating 79 zettabytes (ZB) of data (IDC). Every Internet-connected “thing,” from power grids to smart doorbells, is at risk of attack. BLUETOOTH INDUSTRIAL MEDICAL Throughout the last few decades, cyberattacks against IoT devices have become more sophisticated, more common, and unfortunately, much BOTNET GENERIC IOT SMART HOME more effective. However, the technology sector has fought back, developing and implementing new security technologies to prevent and CAR INFRASTRUCTURE WATCH/WEARABLE protect against cyberattacks. This infographic shows how the industry has evolved with new technologies, protocols, and processes to raise the bar on cybersecurity. STUXNET WATER BMW MIRAI FITBIT SAUDI PETROL SILEX LINUX DARK NEXUS VIRUS UTILITY CONNECTED BOTNET VULNERABILITY CHEMICAL MALWARE BOTNET SYSTEM DRIVE SYSTEM PLANT ATTACK (SCADA) PUERTO UNIVERSITY UKRAINIAN HAJIME AMNESIA AMAZON PHILIPS HUE RICO OF MICHIGAN POWER VIGILANTE BOTNET RING HACK LIGHTBULB SMART TRAFFIC GRID BOTNET METERS LIGHTS MEDTRONIC GERMAN TESLA CCTV PERSIRAI FANCY SWEYNTOOTH INSULIN STEEL MODEL S BOTNET BOTNET BEAR VS. FAMILY PUMPS MILL HACK REMOTE SPORTS HACK HACKABLE HEART BASHLITE FIAT CHRYSLER NYADROP SELF- REAPER THINKPHP TWO MILLION KAIJI MONITORS BOTNET REMOTE CONTROL UPDATING MALWARE BOTNET EXPLOITATION TAKEOVER MALWARE First Era | THE AGE OF EXPLORATION | 2005 - 2009 Second Era | THE AGE OF EXPLOITATION | 2011 - 2019 Third Era | THE AGE OF PROTECTION | 2020 Security is not a priority for early IoT/embedded devices. Most The number of connected devices is exploding, and cloud connectivity Connected devices are ubiquitous in every area of life, from cyberattacks are limited to malware and viruses impacting Windows- is becoming commonplace. -
The Tangled Genealogy of Iot Malware
The Tangled Genealogy of IoT Malware Emanuele Cozzi Pierre-Antoine Vervier Matteo Dell’Amico emanuele:cozzi@eurecom:fr France della@linux:it EURECOM France Sophia Antipolis, France Yun Shen Leyla Bilge Davide Balzarotti yun:shen@nortonlifelock:com leylya:yumer@nortonlifelock:com davide:balzarotti@eurecom:fr NortonLifeLock, Inc. NortonLifeLock, Inc. EURECOM Reading, United Kingdom Sophia Antipolis, France Sophia Antipolis, France ABSTRACT 1 INTRODUCTION The recent emergence of consumer off-the-shelf embedded (IoT) Over the last few years we have witnessed an increase in both the devices and the rise of large-scale IoT botnets has dramatically in- volume and sophistication of malware targeting IoT systems. Tra- creased the volume and sophistication of Linux malware observed ditional botnets and DDoS tools now cohabit with crypto-mining, in the wild. The security community has put a lot of effort to docu- spyware, ransomware, and targeted samples designed to conduct ment these threats but analysts mostly rely on manual work, which cyber espionage. To make things worse, the public availability of makes it difficult to scale and hard to regularly maintain. Moreover, the source code associated with some of the main IoT malware the vast amount of code reuse that characterizes IoT malware calls families have paved the way for myriads of variants and tangled for an automated approach to detect similarities and identify the relationships of similarities and code reuse. To make sense of this phylogenetic tree of each family. complex evolution, the security community has devoted a consider- In this paper we present the largest measurement of IoT malware able effort to analyze and document these emerging threats, mostly to date. -
Identifying and Characterizing Bashlite and Mirai C&C Servers
Identifying and Characterizing Bashlite and Mirai C&C Servers Gabriel Bastos∗, Artur Marzano∗, Osvaldo Fonseca∗, Elverton Fazzion∗y, Cristine Hoepersz, Klaus Steding-Jessenz, Marcelo H. P. C. Chavesz, Italo´ Cunha∗, Dorgival Guedes∗, Wagner Meira Jr.∗ ∗Department of Computer Science – Universidade Federal de Minas Gerais (UFMG) yDepartment of Computing – Universidade Federal de Sao˜ Joao˜ del-Rei (UFSJ) zCERT.br - Brazilian National Computer Emergency Response Team NIC.br - Brazilian Network Information Center Abstract—IoT devices are often a vector for assembling massive which contributes to the rise of a large number of variants. botnets, as a consequence of being broadly available, having Variants exploit other vulnerabilities, include new forms of limited security protections, and significant challenges in deploy- attack, and use different mechanisms to circumvent existing ing software upgrades. Such botnets are usually controlled by centralized Command-and-Control (C&C) servers, which need forms of defense. The increasing number of variants of these to be identified and taken down to mitigate threats. In this paper malwares makes the analysis and reverse engineering process we propose a framework to infer C&C server IP addresses using expensive, hindering the development of countermeasures and four heuristics. Our heuristics employ static and dynamic analysis mitigations. To address the growing number of threats, security to automatically extract information from malware binaries. We analysts and researchers need tools to automate the collection, use active measurements to validate inferences, and demonstrate the efficacy of our framework by identifying and characterizing analysis, and reverse engineering of malware. C&C servers for 62% of 1050 malware binaries collected using The C&C server is a core component of botnets, responsible 47 honeypots. -
Designing an Effective Network Forensic Framework for The
Designing an effective network forensic framework for the investigation of botnets in the Internet of Things Nickolaos Koroniotis A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy School of Engineering and Information Technology The University of New South Wales Australia March 2020 COPYRIGHT STATEMENT ‘I hereby grant the University of New South Wales or its agents a non-exclusive licence to archive and to make available (including to members of the public) my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known. I acknowledge that I retain all intellectual property rights which subsist in my thesis or dissertation, such as copyright and patent rights, subject to applicable law. I also retain the right to use all or part of my thesis or dissertation in future works (such as articles or books).’ ‘For any substantial portions of copyright material used in this thesis, written permission for use has been obtained, or the copyright material is removed from the final public version of the thesis.’ Signed ……………………………………………........................... Date …………………………………………….............................. AUTHENTICITY STATEMENT ‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis.’ Signed ……………………………………………........................... Date …………………………………………….............................. 1 Thesis Dissertation Sheet Surname/Family Name : Koroniotis Given Name/s : Nickolaos Abbreviation for degree : PhD as give in the University calendar Faculty : UNSW Canberra at ADFA School : UC Engineering & Info Tech Thesis Title : Designing an effective network forensic framework for the investigation of botnets in the Internet of Things 2 Abstract 350 words maximum: The emergence of the Internet of Things (IoT), has heralded a new attack surface, where attackers exploit the security weaknesses inherent in smart things. -
Techniques for Detecting Compromised Iot Devices
MSc System and network Engineering Research Project 1 Techniques for detecting compromised IoT devices Project Report Ivo van der Elzen Jeroen van Heugten February 12, 2017 Abstract The amount of connected Internet of Things (IoT) devices is growing, expected to hit 50 billion devices in 2020. Many of those devices lack proper security. This has led to malware families designed to infect IoT devices and use them in Distributed Denial of Service (DDoS) attacks. In this paper we do an in-depth analysis of two families of IoT malware to determine common properties which can be used for detection. Focusing on the ISP-level, we evaluate commonly available detection techniques and apply the results from our analysis to detect IoT malware activity in an ISP network. Applying our detection method to a real-world data set we find indications for a Mirai malware infection. Using generic honeypots we gain new insight in IoT malware behavior. 1 Introduction ple of days after the attacks, the source code for the malware (dubbed \Mirai") was pub- September 20th 2016: A record setting Dis- lished [9] [10]. tributed Denial of Service (DDoS) attack of over 660 Gbps is launched on the website krebsonse- With the rise of these IoT based DDoS at- curity.com of infosec journalist Brian Krebs [1]. tacks [11] it has become clear that this problem A few days later, web hosting company OVH will only become bigger in the future, unless reports they have been hit with a DDoS of over IoT device vendors accept responsibility and 1 Tbps [2]. -
Securing Your Home Routers: Understanding Attacks and Defense Strategies of the Modem’S Serial Number As a Password
Securing Your Home Routers Understanding Attacks and Defense Strategies Joey Costoya, Ryan Flores, Lion Gu, and Fernando Mercês Trend Micro Forward-Looking Threat Research (FTR) Team A TrendLabsSM Research Paper TREND MICRO LEGAL DISCLAIMER The information provided herein is for general information Contents and educational purposes only. It is not intended and should not be construed to constitute legal advice. The information contained herein may not be applicable to all situations and may not reflect the most current situation. 4 Nothing contained herein should be relied on or acted upon without the benefit of legal advice based on the particular facts and circumstances presented and nothing Entry Points: How Threats herein should be construed otherwise. Trend Micro reserves the right to modify the contents of this document Can Infiltrate Your Home at any time without prior notice. Router Translations of any material into other languages are intended solely as a convenience. Translation accuracy is not guaranteed nor implied. If any questions arise related to the accuracy of a translation, please refer to 10 the original language official version of the document. Any discrepancies or differences created in the translation are not binding and have no legal effect for compliance or Postcompromise: Threats enforcement purposes. to Home Routers Although Trend Micro uses reasonable efforts to include accurate and up-to-date information herein, Trend Micro makes no warranties or representations of any kind as to its accuracy, currency, or completeness. You agree that access to and use of and reliance on this document 17 and the content thereof is at your own risk. -
Early Detection of Mirai-Like Iot Bots in Large-Scale Networks Through Sub-Sampled Packet Traffic Analysis
Early Detection Of Mirai-Like IoT Bots In Large-Scale Networks Through Sub-Sampled Packet Traffic Analysis Ayush Kumar and Teng Joon Lim Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119077 [email protected], [email protected] Abstract. The widespread adoption of Internet of Things has led to many secu- rity issues. Recently, there have been malware attacks on IoT devices, the most prominent one being that of Mirai. IoT devices such as IP cameras, DVRs and routers were compromised by the Mirai malware and later large-scale DDoS at- tacks were propagated using those infected devices (bots) in October 2016. In this research, we develop a network-based algorithm which can be used to detect IoT bots infected by Mirai or similar malware in large-scale networks (e.g. ISP network). The algorithm particularly targets bots scanning the network for vul- nerable devices since the typical scanning phase for botnets lasts for months and the bots can be detected much before they are involved in an actual attack. We analyze the unique signatures of the Mirai malware to identify its presence in an IoT device. The prospective deployment of our bot detection solution is discussed next along with the countermeasures which can be taken post detection. Further, to optimize the usage of computational resources, we use a two-dimensional (2D) packet sampling approach, wherein we sample the packets transmitted by IoT devices both across time and across the devices. Leveraging the Mirai signatures identified and the 2D packet sampling approach, a bot detection algorithm is pro- posed. -
Honor Among Thieves: Towards Understanding the Dynamics and Interdependencies in Iot Botnets
Honor Among Thieves: Towards Understanding the Dynamics and Interdependencies in IoT Botnets Jinchun Choi1;2, Ahmed Abusnaina1, Afsah Anwar1, An Wang3, Songqing Chen4, DaeHun Nyang2, Aziz Mohaisen1 1University of Central Florida 2Inha University 3Case Western Reserve University 4George Mason University Abstract—In this paper, we analyze the Internet of Things (IoT) affected. The same botnet was able to take down the Internet Linux malware binaries to understand the dependencies among of the African nation of Liberia [14]. malware. Towards this end, we use static analysis to extract Competition and coordination among botnets are not well endpoints that malware communicates with, and classify such endpoints into targets and dropzones (equivalent to Command explored, although recent reports have highlighted the potential and Control). In total, we extracted 1,457 unique dropzone of such competition as demonstrated in adversarial behaviors IP addresses that target 294 unique IP addresses and 1,018 of Gafgyt towards competing botnets [9]. Understanding a masked target IP addresses. We highlight various characteristics phenomenon in behavior is important for multiple reasons. of those dropzones and targets, including spatial, network, and First, such an analysis would highlight the competition and organizational affinities. Towards the analysis of dropzones’ interdependencies and dynamics, we identify dropzones chains. alliances among IoT botnets (or malactors; i.e., those who Overall, we identify 56 unique chains, which unveil coordination are behind the botnet), which could shed light on cybercrime (and possible attacks) among different malware families. Further economics. Second, understanding such a competition would analysis of chains with higher node counts reveals centralization. necessarily require appropriate analysis modalities, and such We suggest a centrality-based defense and monitoring mechanism a competition signifies those modalities, even when they are to limit the propagation and impact of malware. -
Turning Internet of Things(Iot) Into Internet of Vulnerabilities (Iov) : Iot Botnets
TURNING IOT INTO IOV : IOT BOTNETS 1 Turning Internet of Things(IoT) into Internet of Vulnerabilities (IoV) : IoT Botnets Kishore Angrishi Abstract—Internet of Things (IoT) is the next big evolutionary step in the world of internet. The main intention behind the IoT is to enable safer living and risk mitigation on different levels of life. Home Automation / With the advent of IoT botnets, the view towards IoT devices Smart Home has changed from enabler of enhanced living into Internet of vulnerabilities for cyber criminals. IoT botnets has exposed two Industrial Control Systems different glaring issues, 1) A large number of IoT devices are Smart City accessible over public Internet. 2) Security (if considered at all) is often an afterthought in the architecture of many wide spread Internet of Things (IoT) IoT devices. In this article, we briefly outline the anatomy of Autonomous the IoT botnets and their basic mode of operations. Some of the Vehicles Medical and healthcare major DDoS incidents using IoT botnets in recent times along Smart traffic & parking with the corresponding exploited vulnerabilities will be discussed. control We also provide remedies and recommendations to mitigate IoT Smart Metering & related cyber risks and briefly illustrate the importance of cyber Smart Grids insurance in the modern connected world. Index Terms—DDoS, IoT, IoT Botnets, Mirai Botnet, Cyber Insurance, Security I. INTRODUCTION Fig. 1. Internet of Things (IoT) The Internet of Things (IoT) is key in the digital world of connected living. The futuristic appeal to make life bit more enjoyable in a hectic day-to-day routine is enticing to many. -
On the State of Internet of Things Security: Vulnerabilities, Attacks, and Recent Countermeasures
On the State of Internet of Things Security: Vulnerabilities, Attacks, and Recent Countermeasures DEVKISHEN SISODIA, University of Oregon In this paper, we review the state of Internet of Things (IoT) security research, with a focus on recent countermeasures that attempt to address vulnerabilities and attacks in IoT networks. Due to the fact that IoT encompasses a large range of significantly distinct environments, each of which merits their own survey, our survey focuses mainly on the smart home environment. Based on the papers surveyed, we pinpoint several challenges and open issues that have yet to be adequately addressed in the realm of IoT security research. Lastly, in order to address these open issues, we provide a list of future research directions on which we believe researchers should focus. CCS Concepts: • Security and privacy → Mobile and wireless security. Additional Key Words and Phrases: Internet of Things (IoT); security; vulnerabilities; attacks; countermeasures 1 INTRODUCTION The Internet of Things (IoT) is a computer networking paradigm that refers to scenarios where network connectivity and computing capability extends to embedded sensors and everyday devices, allowing them to generate, exchange, consume, and act upon data with minimal to no human intervention. This paradigm is possible due to recent advancements in the miniaturization of electronics, networking capabilities, and computing power. Even in its relative infancy, IoT is already impacting our everyday lives in profound ways, from healthcare to home automation. In 2018, 7 billion devices were connected to the Internet [1] and this number is expected to increase to 19.8 billion by 2023 [2]. While IoT devices and traditional machines suffer from the same types of attacks, IoT devices tend to be harder to secure due to some unique properties. -
Early Detection of Iot Malware Network Activity Using Machine Learning Techniques
EDIMA: Early Detection of IoT Malware Network Activity Using Machine Learning Techniques Ayush Kumar and Teng Joon Lim Department of Electrical and Computer Engineering National University of Singapore Singapore [email protected], [email protected] Abstract—The widespread adoption of Internet of Things has open TELNET ports and attempting to login using a built-in led to many security issues. Post the Mirai-based DDoS attack dictionary of commonly used credentials (Remaiten, Hajime), in 2016 which compromised IoT devices, a host of new malware or more sophisticated malware that exploit software vulner- using Mirai’s leaked source code and targeting IoT devices have cropped up, e.g. Satori, Reaper, Amnesia, Masuta etc. These abilities to execute remote command injections on vulnera- malware exploit software vulnerabilities to infect IoT devices ble devices (Reaper, Satori, Masuta, Linux.Darlloz, Amnesia instead of open TELNET ports (like Mirai) making them more etc.). Even though TELNET port scanning can be countered difficult to block using existing solutions such as firewalls. In by deploying firewalls (at the user access gateway) which this research, we present EDIMA, a distributed modular solution block incoming/outgoing TELNET traffic, malware exploiting which can be used towards the detection of IoT malware network activity in large-scale networks (e.g. ISP, enterprise networks) software vulnerabilities involving application protocols such during the scanning/infecting phase rather than during an attack. as HTTP, SOAP, PHP etc. are more difficult to block using EDIMA employs machine learning algorithms for edge devices’ firewalls because those application protocols form a part of traffic classification, a packet traffic feature vector database, legitimate traffic as well.