The lack of security measures among the Internet of Things (IoT) devices and their persistent online connection gives adversaries a prime opportunity to target them or even abuse them as intermediary targets in larger attacks such as distributed denial-of-service (DDoS) campaigns. In this paper, we analyze IoT malware and focus on the endpoints reachable on the public Internet, that play an essential part in the IoT malware ecosystem. Namely, we analyze endpoints acting as dropzones and their targets to gain insights into the underlying dynamics in this ecosystem, such as the affinity between the dropzones and their target IP addresses, and the different patterns among endpoints. Towards this goal, we reverse-engineer 2,423 IoT malware samples and extract strings from them to obtain IP addresses. We further gather information about these endpoints from public Internet-wide scanners, such as Shodan and Censys. For the masked IP addresses, we examine the Classless Inter-Domain Routing (CIDR) networks accumulating to more than 100 million (78.2% of total active public IPv4 addresses) endpoints. Our investigation from four different perspectives provides profound insights into the role of endpoints in IoT malware attacks, which deepens our understanding of IoT malware ecosystems and can assist future defenses.
Perimeter-based detection is no longer sufficient for mitigating the threat posed by malicious software. This is evident as antivirus (AV) products are replaced by endpoint detection and response (EDR) products, the latter allowing visibility into live machine activity rather than relying on the AV to filter out malicious artefacts. This paper argues that detecting malware in real-time on an endpoint necessitates an automated response due to the rapid and destructive nature of some malware. The proposed model uses statistical filtering on top of a machine learning dynamic behavioural malware d…
Computers are widely used today by most people. Internet based applications, like ecommerce or ebanking attracts criminals, who using sophisticated techniques, tries to introduce malware on the victim computer. But not only computer users are in risk, also smartphones or smartwatch users, smart cities, Internet of Things devices, etc. Different techniques has been tested against malware. Currently, pattern matching is the default approach in antivirus software. Also, Machine Learning is successfully being used. Continuing this trend, in this article we propose an anomaly based method using the…
The growth in the number of Android and Internet of Things (IoT) devices has witnessed a parallel increase in the number of malicious software (malware), calling for new analysis approaches. We represent binaries using their graph properties of the Control Flow Graph (CFG) structure and conduct an in-depth analysis of malicious graphs extracted from the Android and IoT malware to understand their differences. Using 2,874 and 2,891 malware binaries corresponding to IoT and Android samples, we analyze both general characteristics and graph algorithmic properties. Using the CFG as an abstract str…
In the coming years, the future of military combat will include, on one hand, artificial intelligence-optimized complex command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR) and networks and, on the other hand, autonomous intelligent Things fighting autonomous intelligent Things at a fast pace. Under this perspective, enemy forces will seek to disable or disturb our autonomous Things and our complex infrastructures and systems. Autonomy, scale and complexity in our defense systems will trigger new cyber-attack strategies, and autonomous intelligent …
Internet of Things (IoT) is being considered as the growth engine for industrial revolution 4.0. The combination of IoT, cloud computing and healthcare can contribute in ensuring well-being of people. One important challenge of IoT network is maintaining privacy and to overcome security threats. This paper provides a systematic review of the security aspects of IoT. Firstly, the application of IoT in industrial and medical service scenarios are described, and the security threats are discussed for the different layers of IoT healthcare architecture. Secondly, different types of existing malwar…
The various types of communication technologies and mobility features in Internet of Things (IoT) on the one hand enable fruitful and attractive applications, but on the other hand facilitates malware propagation, thereby raising new challenges on handling IoT-empowered malware for cyber security. Comparing with the malware propagation control scheme in traditional wireless networks where nodes can be directly repaired and secured, in IoT, compromised end devices are difficult to be patched. Alternatively, blocking malware via patching intermediate nodes turns out to be a more feasible and pra…
Internet of Things devices have seen a rapid growth and popularity in recent years with many more ordinary devices gaining network capability and becoming part of the ever growing IoT network. With this exponential growth and the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to its evolving faster than the defence mechanisms can handle with. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this paper, we aim to address this issue by introducing a novel IoT malwar…
The main goal of this study is to investigate the robustness of graph-based Deep Learning (DL) models used for Internet of Things (IoT) malware classification against Adversarial Learning (AL). We designed two approaches to craft adversarial IoT software, including Off-the-Shelf Adversarial Attack (OSAA) methods, using six different AL attack approaches, and Graph Embedding and Augmentation (GEA). The GEA approach aims to preserve the functionality and practicality of the generated adversarial sample through a careful embedding of a benign sample to a malicious one. Our evaluations demonstrate…
The wide acceptance of Internet of Things (IoT) for both household and industrial applications is accompanied by several security concerns. A major security concern is their probable abuse by adversaries towards their malicious intent. Understanding and analyzing IoT malicious behaviors is crucial, especially with their rapid growth and adoption in wide-range of applications. However, recent studies have shown that machine learning-based approaches are susceptible to adversarial attacks by adding junk codes to the binaries, for example, with an intention to fool those machine learning or deep …
A major security challenge for modern Internet of Things (IoT) deployments is to ensure that the devices run legitimate firmware free from malware. This challenge can be addressed through a security primitive called attestation which allows a remote backend to verify the firmware integrity of the devices it manages. In order to accelerate broad attestation adoption in the IoT domain the Trusted Computing Group (TCG) has introduced the Device Identifier Composition Engine (DICE) series of specifications. DICE is a hardware-software architecture for constrained, e.g., microcontroller-based IoT d…
The widespread adoption of Internet of Things has led to many security issues. Post the Mirai-based DDoS attack in 2016 which compromised IoT devices, a host of new malware using Mirai's leaked source code and targeting IoT devices have cropped up, e.g. Satori, Reaper, Amnesia, Masuta etc. These malware exploit software vulnerabilities to infect IoT devices instead of open TELNET ports (like Mirai) making them more difficult to block using existing solutions such as firewalls. In this research, we present EDIMA, a distributed modular solution which can be used towards the detection of IoT malw…
University of Central Florida（中佛罗里达大学）；Wayne State University（韦恩州立大学）；Ewha Womans University（梨花女子大学）
The rapid growth of the Internet of Things (IoT) devices is paralleled by them being on the front-line of malicious attacks. This has led to an explosion in the number of IoT malware, with continued mutations, evolution, and sophistication. These malicious software are detected using machine learning (ML) algorithms alongside the traditional signature-based methods. Although ML-based detectors improve the detection performance, they are susceptible to malware evolution and sophistication, making them limited to the patterns that they have been trained upon. This continuous trend motivates the …
Due to its simple installation and connectivity, the Internet of Things (IoT) is susceptible to malware attacks. Being able to operate autonomously. As IoT devices have become more prevalent, they have become the most tempting targets for malware. Weak, guessable, or hard-coded passwords, and a lack of security measures contribute to these vulnerabilities along with insecure network connections and outdated update procedures. To understand IoT malware, current methods and analysis ,using static methods, are ineffective. The field of deep learning has made great strides in recent years due to t…
Kyushu University（九州大学）；Indian Institute of Technology Delhi（德里印度理工学院）；Royal Holloway University of London（伦敦大学皇家霍洛威学院）
The Internet of Things (IoT) is an extension of the traditional Internet, which allows a very large number of smart devices, such as home appliances, network cameras, sensors and controllers to connect to one another to share information and improve user experiences. Current IoT devices are typically micro-computers for domain-specific computations rather than traditional functionspecific embedded devices. Therefore, many existing attacks, targeted at traditional computers connected to the Internet, may also be directed at IoT devices. For example, DDoS attacks have become very common in IoT e…
In cybersecurity, attackers range from brash, unsophisticated script kiddies and cybercriminals to stealthy, patient advanced persistent threats. When modeling these attackers, we can observe that they demonstrate different risk-seeking and risk-averse behaviors. This work explores how an attacker's risk seeking or risk averse behavior affects their operations against detection-optimizing defenders in an Internet of Things ecosystem. Using an evaluation framework which uses real, parametrizable malware, we develop a game that is played by a defender against attackers with a suite of malware th…
University of Electronic and Science Technology of China（电子科技大学）；Temple University（天普大学）；University of Idaho（爱达荷大学）
Many IoT(Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aim…
Internet of Things (IoT) is becoming more frequently used in more applications as the number of connected devices is in a rapid increase. More connected devices result in bigger challenges in terms of scalability, maintainability and most importantly security especially when it comes to 5G networks. The security aspect of IoT devices is an infant field, which is why it is our focus in this paper. Multiple IoT device manufacturers do not consider securing the devices they produce for different reasons like cost reduction or to avoid using energy-harvesting components. Such potentially malicious…
The upcoming Internet of things (IoT) is foreseen to encompass massive numbers of connected devices, smart objects, and cyber-physical systems. Due to the large-scale and massive deployment of devices, it is deemed infeasible to safeguard 100% of the devices with state-of-the-art security countermeasures. Hence, large-scale IoT has inevitable loopholes for network intrusion and malware infiltration. Even worse, exploiting the high density of devices and direct wireless connectivity, malware infection can stealthily propagate through susceptible (i.e., unsecured) devices and form an epidemic ou…
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学研究院）
Interaction between devices, people, and the Internet has given birth to a new digital communication model, the Internet of Things (IoT). The seamless network of these smart devices is the core of this IoT model. However, on the other hand, integrating smart devices to constitute a network introduces many security challenges. These connected devices have created a security blind spot, where cybercriminals can easily launch an attack to compromise the devices using malware proliferation techniques. Therefore, malware detection is considered a lifeline for the survival of IoT devices against cyb…
Shiraz University of Technology（设拉子理工大学）；University of Surrey（萨里大学）
In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practice available malware features for IoT devices and lack real-time prediction behaviors. More research is thus required on malware detection to cope with real-time misclassification of the input IoT data. Motivated by this, in this paper we propose an adversarial self-supervised architecture for detecting malware in IoT networks, SETTI, considering samples o…
Split Learning-Enabled Framework for Secure and Light-weight Internet of Medical Things Systems
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne（量子云计算与分布式系统实验室，计算机与信息系统学院，墨尔本大学）；Department of Electrical and Electronics Engineering, BITS-Pilani, Pilani Campus（电气与电子工程系，比特斯-帕利尼学院）；Department of Computer Science and Information Systems, BITS-Pilani, Pilani Campus（计算机科学与信息系统系，比特斯-帕利尼学院）
IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
Department of Computer and Information Sciences, Northumbria University（北安普顿大学计算机与信息科学系）；School of Computing, Engineering & Digital Technologies, Teesside University（泰赛大学计算、工程与数字技术学院）
The concept of Internet of Things (IoT) has become more popular in the modern era of technology than ever before. From small household devices to large industrial machines, the vision of IoT has made it possible to connect the devices with the physical world around them. This increasing popularity has also made the IoT devices and applications in the center of attention among attackers. Already, several types of malicious activities exist that attempt to compromise the security and privacy of the IoT devices. One interesting emerging threat vector is the attacks that abuse the use of sensors o…
The widespread adoption of Internet of Things has led to many security 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 attacks 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 ne…
In recent years, the buildings where we spend most part of our life are rapidly evolving. They are becoming fully automated environments where energy consumption, access control, heating and many other subsystems are all integrated within a single system commonly referred to as smart building (SB). To support the growing complexity of building operations, building automation systems (BAS) powering SBs are integrating consumer range Internet of Things (IoT) devices such as IP cameras alongside with operational technology (OT) controllers and actuators. However, these changes pose important cybe…
From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures
École de technologie supérieure (ÉTS)（高等技术学院（ÉTS））；University of Calgary（卡尔加里大学）
Machine learning (ML) used for static portable executable (PE) malware detection typically employs per-file numerical feature vector representations as input with one or more target labels during training. However, there is much orthogonal information that can be gleaned from the \textit{context} in which the file was seen. In this paper, we propose utilizing a static source of contextual information -- the path of the PE file -- as an auxiliary input to the classifier. While file paths are not malicious or benign in and of themselves, they do provide valuable context for a malicious/benign de…
An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to conduct distributed denial of service (DDoS) attacks on critical Internet infrastructure. This motivates the development of new techniques to automatically detect consumer IoT attack traffic. In this paper, we demonstrate that using IoT-specific network behaviors (e.g. limited number of endpoints and regular time intervals between packets) to …
The Internet of Things (IoT) is a paradigm that can tremendously revolutionize health care thus benefiting both hospitals, doctors and patients. In this context, protecting the IoT in health care against interference, including service attacks and malwares, is challenging. Opacity is a confidentiality property capturing a system's ability to keep a subset of its behavior hidden from passive observers. In this work, we seek to introduce an IoT-based heart attack detection system, that could be life-saving for patients without risking their need for privacy through the verification and enforceme…
A large majority of cheap Internet of Things (IoT) devices that arrive brand new, and are configured with out-of-the-box settings, are not being properly secured by the manufactures, and are vulnerable to existing malware lurking on the Internet. Among them is the Mirai botnet which has had its source code leaked to the world, allowing any malicious actor to configure and unleash it. A combination of software assets not being utilised safely and effectively are exposing consumers to a full compromise. We configured and attacked 4 different IoT devices using the Mirai libraries. Our experiments…
University of Wisconsin – Eau Claire（威斯康星大学欧克莱尔分校）
Modern scientific advancements often contribute to the introduction and refinement of never-before-seen technologies. This can be quite the task for humans to maintain and monitor and as a result, our society has become reliant on machine learning to assist in this task. With new technology comes new methods and thus new ways to circumvent existing cyber security measures. This study examines the effectiveness of three distinct Internet of Things cyber security algorithms currently used in industry today for malware and intrusion detection: Random Forest (RF), Support-Vector Machine (SVM), and…
The number of embedded devices that connect to a wireless network has been growing for the past decade. This interaction creates a network of Internet of Things (IoT) devices where data travel continuously. With the increase of devices and the need for the network to extend via fog computing, we have fog-based IoT networks. However, with more endpoints introduced to it, the network becomes open to malicious attackers. This work attempts to protect fog-based IoT networks by creating a platform that secures the endpoints through public-key encryption. The servers are allowed to mask the data pac…
The Internet of Things is progressively getting broader, evol-ving its scope while creating new markets and adding more to the existing ones. However, both generation and analysis of large amounts of data, which are integral to this concept, may require the proper protection and privacy-awareness of some sensitive information. In order to control the access to this data, allowing devices to verify the reliability of their own interactions with other endpoints of the network is a crucial step to ensure this required safeness. Through the implementation of a blockchain-based Public Key Infrastru…
In recent years, the emerging Internet-of-Things (IoT) has led to rising concerns about the security of networked embedded devices. In this work, we focus on the adaptation of Honeypots for improving the security of IoTs. Low-interaction honeypots are used so far in the context of IoT. Such honeypots are limited and easily detectable, and thus, there is a need to find ways how to develop high-interaction, reliable, IoT honeypots that will attract skilled attackers. In this work, we propose the SIPHON architecture - a Scalable high-Interaction Honeypot platform for IoT devices. Our architecture…
Cybersecurity is a very emerging field that protects systems, networks, and data from digital attacks. With the increase in the scale of the Internet and the evolution of cyber attacks, developing novel cybersecurity tools has become important, particularly for Internet of things (IoT) networks. This paper provides a systematic review of the application of deep learning (DL) approaches for cybersecurity. This paper provides a short description of DL methods which is used in cybersecurity, including deep belief networks, generative adversarial networks, recurrent neural networks, and others. Ne…
