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Register a new userIoT Network Security from the Perspective of Adversarial Deep Learning
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Added by
Tugba Erpek
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Machine learning finds rich applications in Internet of Things (IoT) networks such as information retrieval, traffic management, spectrum sensing, and signal authentication. While there is a surge of interest to understand the security issues of machine learning, their implications have not been understood yet for wireless applications such as those in IoT systems that are susceptible to various attacks due the open and broadcast nature of wireless communications. To support IoT systems with heterogeneous devices of different priorities, we present new techniques built upon adversarial machine learning and apply them to three types of over-the-air (OTA) wireless attacks, namely jamming, spectrum poisoning, and priority violation attacks. By observing the spectrum, the adversary starts with an exploratory attack to infer the channel access algorithm of an IoT transmitter by building a deep neural network classifier that predicts the transmission outcomes. Based on these prediction results, the wireless attack continues to either jam data transmissions or manipulate sensing results over the air (by transmitting during the sensing phase) to fool the transmitter into making wrong transmit decisions in the test phase (corresponding to an evasion attack). When the IoT transmitter collects sensing results as training data to retrain its channel access algorithm, the adversary launches a causative attack to manipulate the input data to the transmitter over the air. We show that these attacks with different levels of energy consumption and stealthiness lead to significant loss in throughput and success ratio in wireless communications for IoT systems. Then we introduce a defense mechanism that systematically increases the uncertainty of the adversary at the inference stage and improves the performance. Results provide new insights on how to attack and defend IoT networks using deep learning.
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Monitoring network traffic to identify content, services, and applications is an active research topic in network traffic control systems. While modern firewalls provide the capability to decrypt packets, this is not appealing for privacy advocates. Hence, identifying any information from encrypted traffic is a challenging task. Nonetheless, previous work has identified machine learning methods that may enable application and service identification. The process involves high level feature extraction from network packet data then training a robust machine learning classifier for traffic identification. We propose a classification technique using an ensemble of deep learning architectures on packet, payload, and inter-arrival time sequences. To our knowledge, this is the first time such deep learning architectures have been applied to the Server Name Indication (SNI) classification problem. Our ensemble model beats the state of the art machine learning methods and our up-to-date model can be found on github: url{https://github.com/niloofarbayat/NetworkClassification}
The Internet of Things (IoT) has been on the rise in the last decade as it finds applications in various domains. Hospitality is one of the pioneer sectors that has adopted this technology to create novel services such as smart hotel rooms, personalized services etc. Hotels, restaurants, theme parks, and cruise ships are some specific application areas to improve customer satisfaction by creating an intense interactive environment and data collection with the use of appropriate sensors and actuators. However, applying IoT solutions in the hospitality environment has some unique challenges such as easy physical access to devices. In addition, due to the very nature of these domains, the customers are at the epicenter of these IoT technologies that result in a massive amount of data collection from them. Such data and its management along with business purposes also raises new concerns regarding privacy and ethical considerations. Therefore, this paper surveys and analyzes security, privacy and ethical issues regarding the utilization of IoT devices by focusing on the hospitality industry specifically. We explore some exemplary uses, cases, potential problems and solutions in order to contribute to better understanding and guiding the business operators in this sector.
With an enormous range of applications, Internet of Things (IoT) has magnetized industries and academicians from everywhere. IoT facilitates operations through ubiquitous connectivity by providing Internet access to all the devices with computing capabilities. With the evolution of wireless infrastructure, the focus from simple IoT has been shifted to smart, connected and mobile IoT (M-IoT) devices and platforms, which can enable low-complexity, low-cost and efficient computing through sensors, machines, and even crowdsourcing. All these devices can be grouped under a common term of M-IoT. Even though the positive impact on applications has been tremendous, security, privacy and trust are still the major concerns for such networks and an insufficient enforcement of these requirements introduces non-negligible threats to M-IoT devices and platforms. Thus, it is important to understand the range of solutions which are available for providing a secure, privacy-compliant, and trustworthy mechanism for M-IoT. There is no direct survey available, which focuses on security, privacy, trust, secure protocols, physical layer security and handover protections in M-IoT. This paper covers such requisites and presents comparisons of state-the-art solutions for IoT which are applicable to security, privacy, and trust in smart and connected M-IoT networks. Apart from these, various challenges, applications, advantages, technologies, standards, open issues, and roadmap for security, privacy and trust are also discussed in this paper.
We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE 802.11ac) with deep learning and sustains high throughput by mitigating out-of-network interference. DeepWiFi is interoperable with baseline WiFi and builds upon the existing WiFis PHY transceiver chain without changing the MAC frame format. Users run DeepWiFi for i) RF front end processing; ii) spectrum sensing and signal classification; iii) signal authentication; iv) channel selection and access; v) power control; vi) modulation and coding scheme (MCS) adaptation; and vii) routing. DeepWiFi mitigates the effects of probabilistic, sensing-based, and adaptive jammers. RF front end processing applies a deep learning-based autoencoder to extract spectrum-representative features. Then a deep neural network is trained to classify waveforms reliably as idle, WiFi, or jammer. Utilizing channel labels, users effectively access idle or jammed channels, while avoiding interference with legitimate WiFi transmissions (authenticated by machine learning-based RF fingerprinting) resulting in higher throughput. Users optimize their transmit power for low probability of intercept/detection and their MCS to maximize link rates used by backpressure algorithm for routing. Supported by embedded platform implementation, DeepWiFi provides major throughput gains compared to baseline WiFi and another jamming-resistant protocol, especially when channels are likely to be jammed and the signal-to-interference-plus-noise-ratio is low.
Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter with cognitive radio capabilities empowered by machine learning to perform critical tasks such as spectrum awareness and spectrum sharing, they also become susceptible to new vulnerabilities due to the attacks that target the machine learning applications. This paper identifies the emerging attack surface of adversarial machine learning and corresponding attacks launched against wireless communications in the context of 5G systems. The focus is on attacks against (i) spectrum sharing of 5G communications with incumbent users such as in the Citizens Broadband Radio Service (CBRS) band and (ii) physical layer authentication of 5G User Equipment (UE) to support network slicing. For the first attack, the adversary transmits during data transmission or spectrum sensing periods to manipulate the signal-level inputs to the deep learning classifier that is deployed at the Environmental Sensing Capability (ESC) to support the 5G system. For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station. Results indicate major vulnerabilities of 5G systems to adversarial machine learning. To sustain the 5G system operations in the presence of adversaries, a defense mechanism is presented to increase the uncertainty of the adversary in training the surrogate model used for launching its subsequent attacks.
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