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While various covert botnets were proposed in the past, they still lack complete anonymization for their servers/botmasters or suffer from slow communication between the botmaster and the bots. In this paper, we first propose a new generation hybrid botnet that covertly and efficiently communicates over Bitcoin Lightning Network (LN), called LNBot. Exploiting various anonymity features of LN, we show the feasibility of a scalable two-layer botnet which completely anonymizes the identity of the botmaster. In the first layer, the botmaster anonymously sends the commands to the command and control (C&C) servers through regular LN payments. Specifically, LNBot allows botmaster's commands to be sent in the form of surreptitious multi-hop LN payments, where the commands are either encoded with the payments or attached to the payments to provide covert communications. In the second layer, C&C servers further relay those commands to the bots in their mini-botnets to launch any type of attacks to victim machines. We further improve on this design by introducing D-LNBot; a distributed version of LNBot that generates its C&C servers by infecting users on the Internet and forms the C&C connections by opening channels to the existing nodes on LN. In contrary to the LNBot, the whole botnet formation phase is distributed and the botmaster is never involved in the process. By utilizing Bitcoin's Testnet and the new message attachment feature of LN, we show that D-LNBot can be run for free and commands are propagated faster to all the C&C servers compared to LNBot. We presented proof-of-concept implementations for both LNBot and D-LNBot on the actual LN and extensively analyzed their delay and cost performance. Finally, we also provide and discuss a list of potential countermeasures to detect LNBot and D-LNBot activities and minimize their impacts., Comment: Revised version based on anonymous reviewers' comments. Journal extension of https://doi.org/10.1007/978-3-030-59013-0_36

In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands its usage at a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs' security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs.

The Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Cyber-Physical Systems (CPS) have become essential for our daily lives in contexts such as our homes, buildings, cities, health, transportation, manufacturing, infrastructure, and agriculture. However, they have become popular targets of attacks, due to their inherent limitations which create vulnerabilities. Honeypots and honeynets can prove essential to understand and defend against attacks on IoT, IIoT, and CPS environments by attracting attackers and deceiving them into thinking that they have gained access to the real systems. Honeypots and honeynets can complement other security solutions (i.e., firewalls, Intrusion Detection Systems - IDS) to form a strong defense against malicious entities. This paper provides a comprehensive survey of the research that has been carried out on honeypots and honeynets for IoT, IIoT, and CPS. It provides a taxonomy and extensive analysis of the existing honeypots and honeynets, states key design factors for the state-of-the-art honeypot/honeynet research and outlines open issues for future honeypots and honeynets for IoT, IIoT, and CPS environments.

Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace. Today, a myriad of blockchain and cryptocurrency systems, applications, and technologies are widely available to companies, end-users, and even malicious actors who want to exploit the computational resources of regular users through \textit{cryptojacking} malware. Especially with ready-to-use mining scripts easily provided by service providers (e.g., Coinhive) and untraceable cryptocurrencies (e.g., Monero), cryptojacking malware has become an indispensable tool for attackers. Indeed, the banking industry, major commercial websites, government and military servers (e.g., US Dept. of Defense), online video sharing platforms (e.g., Youtube), gaming platforms (e.g., Nintendo), critical infrastructure resources (e.g., routers), and even recently widely popular remote video conferencing/meeting programs (e.g., Zoom during the Covid-19 pandemic) have all been the victims of powerful cryptojacking malware campaigns. Nonetheless, existing detection methods such as browser extensions that protect users with blacklist methods or antivirus programs with different analysis methods can only provide a partial panacea to this emerging cryptojacking issue as the attackers can easily bypass them by using obfuscation techniques or changing their domains or scripts frequently. Therefore, many studies in the literature proposed cryptojacking malware detection methods using various dynamic/behavioral features., Comment: EuroS&P 2021 Conference

As technology becomes more widely available, millions of users worldwide have installed some form of smart device in their homes or workplaces. These devices are often off-the-shelf commodity systems, such as Google Home or Samsung SmartThings, that are installed by end-users looking to automate a small deployment. In contrast to these "plug-and-play" systems, purpose-built Enterprise Internet-of-Things (E-IoT) systems such as Crestron, Control4, RTI, Savant offer a smart solution for more sophisticated applications (e.g., complete lighting control, A/V management, security). In contrast to commodity systems, E-IoT systems are usually closed source, costly, require certified installers, and are overall more robust for their use cases. Due to this, E-IoT systems are often found in expensive smart homes, government and academic conference rooms, yachts, and smart private offices. However, while there has been plenty of research on the topic of commodity systems, no current study exists that provides a complete picture of E-IoT systems, their components, and relevant threats. As such, lack of knowledge of E-IoT system threats, coupled with the cost of E-IoT systems has led many to assume that E-IoT systems are secure. To address this research gap, raise awareness on E-IoT security, and motivate further research, this work emphasizes E-IoT system components, E-IoT vulnerabilities, solutions, and their security implications. In order to systematically analyze the security of E-IoT systems, we divide E-IoT systems into four layers: E-IoT Devices Layer, Communications Layer, Monitoring and Applications Layer, and Business Layer. We survey attacks and defense mechanisms, considering the E-IoT components at each layer and the associated threats. In addition, we present key observations in state-of-the-art E-IoT security and provide a list of open research problems that need further research.

In recent years, ransomware has been one of the most notorious malware targeting end users, governments, and business organizations. It has become a very profitable business for cybercriminals with revenues of millions of dollars, and a very serious threat to organizations with financial loss of billions of dollars. Numerous studies were proposed to address the ransomware threat, including surveys that cover certain aspects of ransomware research. However, no study exists in the literature that gives the complete picture on ransomware and ransomware defense research with respect to the diversity of targeted platforms. Since ransomware is already prevalent in PCs/workstations/desktops/laptops, is becoming more prevalent in mobile devices, and has already hit IoT/CPS recently, and will likely grow further in the IoT/CPS domain very soon, understanding ransomware and analyzing defense mechanisms with respect to target platforms is becoming more imperative. In order to fill this gap and motivate further research, in this paper, we present a comprehensive survey on ransomware and ransomware defense research with respect to PCs/workstations, mobile devices, and IoT/CPS platforms. Specifically, covering 137 studies over the period of 1990-2020, we give a detailed overview of ransomware evolution, comprehensively analyze the key building blocks of ransomware, present a taxonomy of notable ransomware families, and provide an extensive overview of ransomware defense research (i.e., analysis, detection, and recovery) with respect to platforms of PCs/workstations, mobile devices, and IoT/CPS. Moreover, we derive an extensive list of open issues for future ransomware research. We believe this survey will motivate further research by giving a complete picture on state-of-the-art ransomware research., Comment: Accepted to ACM Computing Surveys

The rise of IoT devices has led to the proliferation of smart buildings, offices, and homes worldwide. Although commodity IoT devices are employed by ordinary end-users, complex environments such as smart buildings, smart offices, conference rooms, or hospitality require customized and highly reliable solutions. Those systems called Enterprise Internet of Things (EIoT) connect such environments to the Internet and are professionally managed solutions usually offered by dedicated vendors. As EIoT systems require specialized training, software, and equipment to deploy, this has led to very little research investigating the security of EIoT systems and their components. In effect, EIoT systems in smart settings such as smart buildings present an unprecedented and unexplored threat vector for an attacker. In this work, we explore EIoT system vulnerabilities and insecure development practices. Specifically, focus on the usage of drivers as an attack mechanism, and introduce PoisonIvy, a number of novel attacks that demonstrate an attacker can easily compromise EIoT system controllers using malicious drivers. Specifically, we show how drivers used to integrate third-party devices to EIoT systems can be misused in a systematic fashion. To demonstrate the capabilities of attackers, we implement and evaluate PoisonIvy using a testbed of real EIoT devices. We show that an attacker can perform DoS attacks, gain remote control, and maliciously abuse system resources of EIoT systems. To the best of our knowledge, this is the first work to analyze the (in)securities of EIoT deployment practices and demonstrate the associated vulnerabilities in this ecosystem. With this work, we raise awareness on the (in)secure development practices used for EIoT systems, the consequences of which can largely impact the security, privacy, reliability, and performance of millions of EIoT systems worldwide., Comment: 10 pages, BuildSys 2020

The increasing availability of healthcare data requires accurate analysis of disease diagnosis, progression, and realtime monitoring to provide improved treatments to the patients. In this context, Machine Learning (ML) models are used to extract valuable features and insights from high-dimensional and heterogeneous healthcare data to detect different diseases and patient activities in a Smart Healthcare System (SHS). However, recent researches show that ML models used in different application domains are vulnerable to adversarial attacks. In this paper, we introduce a new type of adversarial attacks to exploit the ML classifiers used in a SHS. We consider an adversary who has partial knowledge of data distribution, SHS model, and ML algorithm to perform both targeted and untargeted attacks. Employing these adversarial capabilities, we manipulate medical device readings to alter patient status (disease-affected, normal condition, activities, etc.) in the outcome of the SHS. Our attack utilizes five different adversarial ML algorithms (HopSkipJump, Fast Gradient Method, Crafting Decision Tree, Carlini & Wagner, Zeroth Order Optimization) to perform different malicious activities (e.g., data poisoning, misclassify outputs, etc.) on a SHS. Moreover, based on the training and testing phase capabilities of an adversary, we perform white box and black box attacks on a SHS. We evaluate the performance of our work in different SHS settings and medical devices. Our extensive evaluation shows that our proposed adversarial attack can significantly degrade the performance of a ML-based SHS in detecting diseases and normal activities of the patients correctly, which eventually leads to erroneous treatment., Comment: 6 pages, 5 figures, Accepted in IEEE Globecom 2020

The recent advancements in computing systems and wireless communications have made healthcare systems more efficient than before. Modern healthcare devices can monitor and manage different health conditions of the patients automatically without any manual intervention from medical professionals. Additionally, the use of implantable medical devices (IMDs), body area networks (BANs), and Internet of Things (IoT) technologies in healthcare systems improve the overall patient monitoring and treatment process. However, these systems are complex in software and hardware, and optimizing between security, privacy, and treatment is crucial for healthcare systems as any security or privacy violation can lead to severe effects on patients' treatments and overall health conditions. Indeed, the healthcare domain is increasingly facing security challenges and threats due to numerous design flaws and the lack of proper security measures in healthcare devices and applications. In this paper, we explore various security and privacy threats to healthcare systems and discuss the consequences of these threats. We present a detailed survey of different potential attacks and discuss their impacts. Furthermore, we review the existing security measures proposed for healthcare systems and discuss their limitations. Finally, we conclude the paper with future research directions toward securing healthcare systems against common vulnerabilities., Comment: Security of modern healthcare systems. submitted and under review. 40 pages, 3 figures, 4 tables

While various covert botnets were proposed in the past, they still lack complete anonymization for their servers/botmasters or suffer from slow communication between the botmaster and the bots. In this paper, we propose a new generation hybrid botnet that covertly and efficiently communicates over Bitcoin Lightning Network (LN), called LNBot. LN is a payment channel network operating on top of Bitcoin network for faster Bitcoin transactions with negligible fees. Exploiting various anonymity features of LN, we designed a scalable two-layer botnet which completely anonymize the identity of the botmaster. In the first layer, the botmaster sends commands anonymously to the C&C servers through LN transactions. Specifically, LNBot allows botmaster's commands to be sent in the form of surreptitious multihop LN payments, where the commands are encoded with ASCII or Huffman encoding to provide covert communications. In the second layer, C&C servers further relay those commands to the bots they control in their mini-botnets to launch any type of attacks to victim machines. We implemented a proof-of-concept on the actual LN and extensively analyzed the delay and cost performance of LNBot. Our analysis show that LNBot achieves better scalibility compared to the other similar blockchain botnets with negligible costs. Finally, we also provide and discuss a list of potential countermeasures to detect LNBot activities and minimize its impacts.

Cyber-Physical Systems (CPS) play a significant role in our critical infrastructure networks from power-distribution to utility networks. The emerging smart-grid concept is a compelling critical CPS infrastructure that relies on two-way communications between smart devices to increase efficiency, enhance reliability, and reduce costs. However, compromised devices in the smart grid poses several security challenges. Consequences of propagating fake data or stealing sensitive smart grid information via compromised devices are costly. Hence, early behavioral detection of compromised devices is critical for protecting the smart grid's components and data. To address these concerns, in this paper, we introduce a novel and configurable system-level framework to identify compromised smart grid devices. The framework combines system and function call tracing techniques with signal processing and statistical analysis to detect compromised devices based on their behavioral characteristics. We measure the efficacy of our framework with a realistic smart grid substation testbed that includes both resource-limited and resource-rich devices. In total, using our framework, we analyze six different types of compromised device scenarios with different resources and attack payloads. To the best of our knowledge, the proposed framework is the first in detecting compromised CPS smart grid devices with system and function-level call tracing techniques. The experimental results reveal an excellent rate for the detection of compromised devices. Specifically, performance metrics include accuracy values between 95% and 99% for the different attack scenarios. Finally, the performance analysis demonstrates that the use of the proposed framework has minimal overhead on the smart grid devices' computing resources.

Users trust IoT apps to control and automate their smart devices. These apps necessarily have access to sensitive data to implement their functionality. However, users lack visibility into how their sensitive data is used (or leaked), and they often blindly trust the app developers. In this paper, we present IoTWatcH, a novel dynamic analysis tool that uncovers the privacy risks of IoT apps in real-time. We designed and built IoTWatcH based on an IoT privacy survey that considers the privacy needs of IoT users. IoTWatcH provides users with a simple interface to specify their privacy preferences with an IoT app. Then, in runtime, it analyzes both the data that is sent out of the IoT app and its recipients using Natural Language Processing (NLP) techniques. Moreover, IoTWatcH informs the users with its findings to make them aware of the privacy risks with the IoT app. We implemented IoTWatcH on real IoT applications. Specifically, we analyzed 540 IoT apps to train the NLP model and evaluate its effectiveness. IoTWatcH successfully classifies IoT app data sent to external parties to correct privacy labels with an average accuracy of 94.25%, and flags IoT apps that leak privacy data to unauthorized parties. Finally, IoTWatcH yields minimal overhead to an IoT app's execution, on average 105 ms additional latency.

In a smart home system, multiple users have access to multiple devices, typically through a dedicated app installed on a mobile device. Traditional access control mechanisms consider one unique trusted user that controls the access to the devices. However, multi-user multi-device smart home settings pose fundamentally different challenges to traditional single-user systems. For instance, in a multi-user environment, users have conflicting, complex, and dynamically changing demands on multiple devices, which cannot be handled by traditional access control techniques. To address these challenges, in this paper, we introduce Kratos, a novel multiuser and multi-device-aware access control mechanism that allows smart home users to flexibly specify their access control demands. Kratos has three main components: user interaction module, backend server, and policy manager. Users can specify their desired access control settings using the interaction module which are translated into access control policies in the backend server. The policy manager analyzes these policies and initiates negotiation between users to resolve conflicting demands and generates final policies. We implemented Kratos and evaluated its performance on real smart home deployments featuring multi-user scenarios with a rich set of configurations (309 different policies including 213 demand conflicts and 24 restriction policies). These configurations included five different threats associated with access control mechanisms. Our extensive evaluations show that Kratos is very effective in resolving conflicting access control demands with minimal overhead and robust against different attacks., Comment: Accepted in the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec 2020)

Sensors (e.g., light, gyroscope, accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor Application Programming Interface (API). In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor, (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96% without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead., Comment: Accepted in IEEE Transaction on Mobile Computing. arXiv admin note: substantial text overlap with arXiv:1706.10220

Our everyday lives are expanding fast with the introduction of new Smart Home Systems (SHSs). Today, a myriad of SHS devices and applications are widely available to users and have already started to re-define our modern lives. Smart home users utilize the apps to control and automate such devices. Users can develop their own apps or easily download and install them from vendor-specific app markets. App-based SHSs offer many tangible benefits to our lives, but also unfold diverse security risks. Several attacks have already been reported for SHSs. However, current security solutions consider smart home devices and apps individually to detect malicious actions rather than the context of the SHS as a whole. The existing mechanisms cannot capture user activities and sensor-device-user interactions in a holistic fashion. To address these issues, in this paper, we introduce Aegis, a novel context-aware security framework to detect malicious behavior in a SHS. Specifically, Aegis observes the states of the connected smart home entities (sensors and devices) for different user activities and usage patterns in a SHS and builds a contextual model to differentiate between malicious and benign behavior. We evaluated the efficacy and performance of Aegis in multiple smart home settings (i.e., single bedroom, double bedroom, duplex) with real-life users performing day-to-day activities and real SHS devices. We also measured the performance of Aegis against five different malicious behaviors. Our detailed evaluation shows that Aegis can detect malicious behavior in SHS with high accuracy (over 95%) and secure the SHS regardless of the smart home layout, device configuration, installed apps, and enforced user policies. Finally, Aegis achieves minimum overhead in detecting malicious behavior in SHS, ensuring easy deployability in real-life smart environments., Comment: Accepted in 2019 Annual Computer Security Applications Conference (ACSAC)

The High Definition Multimedia Interface (HDMI) is the de-facto standard for Audio/Video interfacing between video-enabled devices. Today, almost tens of billions of HDMI devices exist worldwide and are widely used to distribute A/V signals in smart homes, offices, concert halls, and sporting events making HDMI one of the most highly deployed systems in the world. An important component in HDMI is the Consumer Electronics Control (CEC) protocol, which allows for the interaction between devices within an HDMI distribution network. Nonetheless, existing network security mechanisms only protect traditional networking components, leaving CEC outside of their scope. In this work, we identify and tap into CEC protocol vulnerabilities, using them to implement realistic proof-of-work attacks on HDMI distribution networks. We study, how current insecure CEC protocol practices and HDMI distributions may grant an adversary a novel attack surface for HDMI devices otherwise thought to be unreachable. To introduce this novel attack surface, we present HDMI-Walk, which opens a realm of remote and local CEC attacks to HDMI devices. Specifically, with HDMI-Walk, an attacker can perform malicious analysis of devices, eavesdropping, Denial of Service attacks, targeted device attacks, and even facilitate well-known existing attacks through HDMI. With HDMI-Walk, we prove it is feasible for an attacker to gain arbitrary control of HDMI devices. We demonstrate the implementations of both local and remote attacks with commodity HDMI devices. Finally, we discuss security mechanisms to provide impactful and comprehensive security evaluation to these real-world systems while guaranteeing deployability and providing minimal overhead considering the current limitations of the CEC protocol. To the best of our knowledge, this is the first work solely investigating the security of HDMI device distribution networks., Comment: 10 Pages, ACSAC 2019, 8 Figures

We present an empirical and large-scale analysis of malware samples captured from two different enterprises from 2017 to early 2018. Particularly, we perform threat vector, social-engineering, vulnerability and time-series analysis on our dataset. Unlike existing malware studies, our analysis is specifically focused on the recent enterprise malware samples. First of all, based on our analysis on the combined datasets of two enterprises, our results confirm the general consensus that AV-only solutions are not enough for real-time defenses in enterprise settings because on average 40% of the malware samples, when first appeared, are not detected by most AVs on VirusTotal or not uploaded to VT at all (i.e., never seen in the wild yet). Moreover, our analysis also shows that enterprise users transfer documents more than executables and other types of files. Therefore, attackers embed malicious codes into documents to download and install the actual malicious payload instead of sending malicious payload directly or using vulnerability exploits. Moreover, we also found that financial matters (e.g., purchase orders and invoices) are still the most common subject seen in Business Email Compromise (BEC) scams that aim to trick employees. Finally, based on our analysis on the timestamps of captured malware samples, we found that 93% of the malware samples were delivered on weekdays. Our further analysis also showed that while the malware samples that require user interaction such as macro-based malware samples have been captured during the working hours of the employees, the massive malware attacks are triggered during the off-times of the employees to be able to silently spread over the networks.

The integration of Internet-of-Things and pervasive computing in medical devices have made the modern healthcare system "smart". Today, the function of the healthcare system is not limited to treat the patients only. With the help of implantable medical devices and wearables, Smart Healthcare System (SHS) can continuously monitor different vital signs of a patient and automatically detect and prevent critical medical conditions. However, these increasing functionalities of SHS raise several security concerns and attackers can exploit the SHS in numerous ways: they can impede normal function of the SHS, inject false data to change vital signs, and tamper a medical device to change the outcome of a medical emergency. In this paper, we propose HealthGuard, a novel machine learning-based security framework to detect malicious activities in a SHS. HealthGuard observes the vital signs of different connected devices of a SHS and correlates the vitals to understand the changes in body functions of the patient to distinguish benign and malicious activities. HealthGuard utilizes four different machine learning-based detection techniques (Artificial Neural Network, Decision Tree, Random Forest, k-Nearest Neighbor) to detect malicious activities in a SHS. We trained HealthGuard with data collected for eight different smart medical devices for twelve benign events including seven normal user activities and five disease-affected events. Furthermore, we evaluated the performance of HealthGuard against three different malicious threats. Our extensive evaluation shows that HealthGuard is an effective security framework for SHS with an accuracy of 91% and an F-1 score of 90%., Comment: Accepted to be appeared in The International Symposium on Health and Medical informatics, Management and Security (HMiMS 2019)

The ubiquitous nature of the IoT devices has brought serious security implications to its users. A lot of consumer IoT devices have little to no security implementation at all, thus risking user's privacy and making them target of mass cyber-attacks. Indeed, recent outbreak of Mirai botnet and its variants have already proved the lack of security on the IoT world. Hence, it is important to understand the security issues and attack vectors in the IoT domain. Though significant research has been done to secure traditional computing systems, little focus was given to the IoT realm. In this work, we reduce this gap by developing a honeypot framework for IoT devices. Specifically, we introduce U-PoT: a novel honeypot framework for capturing attacks on IoT devices that use Universal Plug and Play (UPnP) protocol. A myriad of smart home devices including smart switches, smart bulbs, surveillance cameras, smart hubs, etc. uses the UPnP protocol. Indeed, a simple search on Shodan IoT search engine lists 1,676,591 UPnP devices that are exposed to public network. The popularity and ubiquitous nature of UPnP-based IoT device necessitates a full-fledged IoT honeypot system for UPnP devices. Our novel framework automatically creates a honeypot from UPnP device description documents and is extendable to any device types or vendors that use UPnP for communication. To the best of our knowledge, this is the first work towards a flexible and configurable honeypot framework for UPnP-based IoT devices. We released U-PoT under an open source license for further research and created a database of UPnP device descriptions. We also evaluated our framework on two emulated deices. Our experiments show that the emulated devices are able to mimic the behavior of a real IoT device and trick vendor-provided device management applications or popular IoT search engines while having minimal performance ovherhead., Comment: International Performance Computing and Communications Conference

With wearable devices such as smartwatches on the rise in the consumer electronics market, securing these wearables is vital. However, the current security mechanisms only focus on validating the user not the device itself. Indeed, wearables can be (1) unauthorized wearable devices with correct credentials accessing valuable systems and networks, (2) passive insiders or outsider wearable devices, or (3) information-leaking wearables devices. Fingerprinting via machine learning can provide necessary cyber threat intelligence to address all these cyber attacks. In this work, we introduce a wearable fingerprinting technique focusing on Bluetooth classic protocol, which is a common protocol used by the wearables and other IoT devices. Specifically, we propose a non-intrusive wearable device identification framework which utilizes 20 different Machine Learning (ML) algorithms in the training phase of the classification process and selects the best performing algorithm for the testing phase. Furthermore, we evaluate the performance of proposed wearable fingerprinting technique on real wearable devices, including various off-the-shelf smartwatches. Our evaluation demonstrates the feasibility of the proposed technique to provide reliable cyber threat intelligence. Specifically, our detailed accuracy results show on average 98.5%, 98.3% precision and recall for identifying wearables using the Bluetooth classic protocol., Comment: 15 pages, 10 figures

IoT devices and sensors have been utilized in a cooperative manner to enable the concept of a smart environment. In these smart settings, abundant data is generated as a result of the interactions between devices and users' day-to-day activities. Such data contain valuable forensic information about events and actions occurring inside the smart environment and, if analyzed, may help hold those violating security policies accountable. In this paper, we introduce IoTDots, a novel digital forensic framework for a smart environment such as smart homes and smart offices. IoTDots has two main components: IoTDots-Modifier and IoTDots-Analyzer. At compile time, IoTDots-Modifier performs the source code analysis of smart apps, detects forensically-relevant information, and automatically insert tracing logs. Then, at runtime, the logs are stored into a IoTDots database. Later, in the event of a forensic investigation, the IoTDots-Analyzer applies data processing and machine learning techniques to extract valuable and usable forensic information from the devices' activity. In order to test the performance of IoTDots, we tested IoTDots in a realistic smart office environment with a total of 22 devices and sensors. The evaluation results show that IoTDots can achieve, on average, over 98% of accuracy on detecting user activities and over 96% accuracy on detecting the behavior of users, devices, and apps in a smart environment. Finally, IoTDots performance yields no overhead to the smart devices and very minimal overhead to the cloud server.

The smart grid concept has transformed the traditional power grid into a massive cyber-physical system that depends on advanced two-way communication infrastructure to integrate a myriad of different smart devices. While the introduction of the cyber component has made the grid much more flexible and efficient with so many smart devices, it also broadened the attack surface of the power grid. Particularly, compromised devices pose a great danger to the healthy operations of the smart-grid. For instance, the attackers can control the devices to change the behaviour of the grid and can impact the measurements. In this paper, to detect such misbehaving malicious smart grid devices, we propose a machine learning and convolution-based classification framework. Our framework specifically utilizes system and library call lists at the kernel level of the operating system on both resource-limited and resource-rich smart grid devices such as RTUs, PLCs, PMUs, and IEDs. Focusing on the types and other valuable features extracted from the system calls, the framework can successfully identify malicious smart-grid devices. In order to test the efficacy of the proposed framework, we built a representative testbed conforming to the IEC-61850 protocol suite and evaluated its performance with different system calls. The proposed framework in different evaluation scenarios yields very high accuracy (avg. 91%) which reveals that the framework is effective to overcome compromised smart grid devices problem., Comment: 6 Pages, Accepted for publication in International Conference on Communications

One-time login process in conventional authentication systems does not guarantee that the identified user is the actual user throughout the session. However, it is necessary to re-verify the user identity periodically throughout a login session without reducing the user convenience. Continuous authentication can address this issue. However, existing methods are either not reliable or not usable. In this paper, we introduce a usable and reliable method called Wearable Assisted Continuous Authentication (WACA). WACA relies on the sensor based keystroke dynamics, where the authentication data is acquired through the built in sensors of a wearable (e.g., smartwatch) while the user is typing. We implemented the WACA framework and evaluated its performance on real devices with real users. The empirical evaluation of WACA reveals that WACA is feasible and its error rate is as low as 1 percent with 30 seconds of processing time and 2 3% for 20 seconds. The computational overhead is minimal. Furthermore, we tested WACA against different attack scenarios. WACA is capable of identifying insider threats with very high accuracy (99.2%) and also robust against powerful adversaries such as imitation and statistical attackers., Comment: A shorter version of this paper will appear in BioSTAR 2018 Workshop. This is the full version of the paper. 1st update: added ACK section

Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital connectivity has had profound effects on society--smart homes, personal monitoring devices, enhanced manufacturing and other IoT apps have changed the way we live, play, and work. Yet extant IoT platforms provide few means of evaluating the use (and potential avenues for misuse) of sensitive information. Thus, consumers and organizations have little information to assess the security and privacy risks these devices present. In this paper, we present SainT, a static taint analysis tool for IoT applications. SainT operates in three phases; (a) translation of platform-specific IoT source code into an intermediate representation (IR), (b) identifying sensitive sources and sinks, and (c) performing static analysis to identify sensitive data flows. We evaluate SainT on 230 SmartThings market apps and find 138 (60%) include sensitive data flows. In addition, we demonstrate SainT on IoTBench, a novel open-source test suite containing 19 apps with 27 unique data leaks. Through this effort, we introduce a rigorously grounded framework for evaluating the use of sensitive information in IoT apps---and therein provide developers, markets, and consumers a means of identifying potential threats to security and privacy., Comment: first submission

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 on IoT devices. IoT devices are vulnerable to sensor-based threats due to the lack of proper security measurements available to control use of sensors by apps. By exploiting the sensors (e.g., accelerometer, gyroscope, microphone, light sensor, etc.) on an IoT device, attackers can extract information from the device, transfer malware to a device, or trigger a malicious activity to compromise the device. In this survey, we explore various threats targeting IoT devices and discuss how their sensors can be abused for malicious purposes. Specifically, we present a detailed survey about existing sensor-based threats to IoT devices and countermeasures that are developed specifically to secure the sensors of IoT devices. Furthermore, we discuss security and privacy issues of IoT devices in the context of sensor-based threats and conclude with future research directions., Comment: Sensor-based Threats, Internet-of-Things, Smart Devices, Side Channel Attack

The Internet of Things (IoT) extends the idea of interconnecting computers to a plethora of different devices, collectively referred to as smart devices. These are physical items - i.e., "things" - such as wearable devices, home appliances, and vehicles, enriched with computational and networking capabilities. Due to the huge set of devices involved - and therefore, its pervasiveness - IoT is a great platform to leverage for building new applications and services or extending existing ones. In this regard, expanding online advertising into the IoT realm is an under-investigated yet promising research direction, especially considering that traditional Internet advertising market is already worth hundreds of billions of dollars. In this paper, we first propose the architecture of an IoT advertising platform inspired by the well-known business ecosystem, which the traditional Internet advertising is based on. Additionally, we discuss the key challenges to implement such a platform with a special focus on issues related to architecture, advertisement content delivery, security, and privacy of the users., Comment: Accepted for publication at IEEE Communications Magazine

Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does not provide any guarantees not to leak any information about individuals to adversaries. Differential privacy (DP) can be utilized to address this; however, achieving SMC with DP is not a trivial task, either. In this paper, we propose a novel Secure Multiparty Distributed Differentially Private (SM-DDP) protocol to achieve secure and private computations in a multiparty environment. Specifically, with our protocol, we simultaneously achieve SMC and DP in distributed settings focusing on linear regression on horizontally distributed data. That is, parties do not see each others' data and further, can not infer information about individuals from the final constructed statistical model. Any statistical model function that allows independent calculation of local statistics can be computed through our protocol. The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. In this work, we first introduce the theoretical foundation for the SM-DDP protocol and then evaluate its efficacy and performance on two different datasets. Our results show that one can achieve individual-level privacy through the proposed protocol with distributed DP, which is independently applied by each party in a distributed fashion. Moreover, our results also show that the SM-DDP protocol incurs minimal computational overhead, is scalable, and provides security and privacy guarantees.

Sensors (e.g., light, gyroscope, accelerotmeter) and sensing enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor API. In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT) to detect malicious behavior associated with sensors. We implemented 6thSense on a sensor-rich Android smart device (i.e., smartphone) and collected data from typical daily activities of 50 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor (e.g., light), (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96% without compromising the normal functionality of the device. Moreover, our framework costs minimal overhead., Comment: 18 pages, Cyber-security, smart devices, sensors, sensor-based threats, 26th USENIX Security Symposium

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems., Comment: - Updated. (October 6, 2017) - This paper is an early draft of the survey that is being submitted to ACM CSUR and has been uploaded to arXiv for feedback from stakeholders

Data sharing among partners---users, organizations, companies---is crucial for the advancement of data analytics in many domains. Sharing through secure computation and differential privacy allows these partners to perform private computations on their sensitive data in controlled ways. However, in reality, there exist complex relationships among members. Politics, regulations, interest, trust, data demands and needs are one of the many reasons. Thus, there is a need for a mechanism to meet these conflicting relationships on data sharing. This paper presents Curie, an approach to exchange data among members whose membership has complex relationships. The CPL policy language that allows members to define the specifications of data exchange requirements is introduced. Members (partners) assert who and what to exchange through their local policies and negotiate a global sharing agreement. The agreement is implemented in a multi-party computation that guarantees sharing among members will comply with the policy as negotiated. The use of Curie is validated through an example of a health care application built on recently introduced secure multi-party computation and differential privacy frameworks, and policy and performance trade-offs are explored., Comment: updated (this version has been accepted to CODASPY 2019)