Recent advances in cross-technology communication can support direct communication among heterogeneous IoT devices (i.e., WiFi, ZigBee, and BLE) without requiring any modifications to the hardware, which has significantly improved the communication efficiency and shown great advantages for supporting smart applications. However, until now a key establishment protocol to support secure and reliable asymmetric cross- technology communication (CTC) is missing, which introduces severe privacy and security issues. Existing solutions are not designed for CTC, since they mainly focus on the symmetric communication among homogeneous IoT devices. In this work, we present a Key Establishment Protocol (KEP), which explores and lever- ages the unique feature of CTC - Possibility PN Sequence Reception (PSR) to not only perform key establishment between heterogeneous IoT devices with different physical layers (i.e., WiFi and ZigBee) but also improve the communication reliability at the same time. Our extensive real-world experiments show that KEP can finish the key establishment in seconds and effectively defend against multiple types of attacks. Furthermore, KEP doubles the packet reception ratio compared to the state-of-the-art solutions.

Orthogonal frequency-division multiplexing (OFDM) has been widely used in WiFi, LTE, and adopted in 5G. Recently, researchers have proposed multiple OFDM-based WiFi backscatter systems that use the same underlying design principle (i.e., codeword translation) at the OFDM symbol-level to transmit the tag data. However, since the phase error correction in WiFi receivers can eliminate the phase offset created by a tag, the codeword translation requires specific WiFi receivers that can disable the phase error correction. As a result, phase error is introduced into the decoding procedure of the codeword translation, which significantly increases the tag data decoding error. To address this issue, we designed a novel OFDM backscatter called TScatter, which uses high-granularity sample-level modulation to avoid the phase offset created by a tag being eliminated by phase error correction. Moreover, by taking advantage of the phase error correction, our system is able to work in more dynamic environments. Our design also has two advantages: much lower bit error rate (BER) and higher throughput. We conducted extensive evaluations under different scenarios. The experimental results show that TScatter has i) three to four orders of magnitude lower BER when its throughput is similar to the latest OFDM backscatter system MOXcatter; or ii) more than 212 times higher throughput when its BER is similar to MOXcatter. Our design is generic and has the potential to be applied to backscatter other OFDM signals (e.g., LTE and 5G).

Security issues have become one of the major challenges for Internet-of-Things (IoT) networks. To overcome this challenge, the recent commonly-used approaches mainly focus on conducting encryption on IoT communication or performing continuous authentication for IoT devices by using pre-shared credentials (e.g., passcode and wireless channel signatures). However, these mechanisms are deemed insufficient, in part, due to the increasing number of data breaches and the recent proliferation of sensitive IoT devices and applications. We present IoT Sentinel - a novel security system that explores the correlation between IoT devices to effectively and efficiently secure IoT networks. Specifically, our system (i) detects potential attacks, (ii) localizes the attacker, and (iii) conducts dynamic implicit authentication at the same time. Moreover, instead of requiring full physical-layer access to IoT devices for finegrained measurement of the wireless signal, IoT Sentinel uses only coarse packet-level device correlation information to secure IoT networks with negligible overhead to the network. Thus, making our approach compatible with existing constrained IoT devices. We extensively evaluate the efficacy of IoT Sentinel in different scenarios and settings. The experiment results show that our approach achieves around 96% attack detection accuracy, more than 70% attacker localization accuracy, and around 100% device authentication accuracy.

Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.

The camera is one of the most important sensors for an autonomous vehicle (AV) to perform Environment Perception and Simultaneous Localization and Mapping (SLAM). To secure the camera, current autonomous vehicles not only utilize the data gathered from multiple sensors (e.g., Camera, Ultrasonic Sensor, Radar, or LiDAR) for environment perception and SLAM but also require the human driver to always realize the driving situation, which can effectively defend against previous attack approaches (i.e., creating visible fake objects or introducing perturbations to the camera by using advanced deep learning techniques). Different from their work, in this paper, we in-depth investigate the features of Infrared light and introduce a new security challenge called I-Can-See-the-Light- Attack (ICSL Attack) that can alter environment perception results and introduce SLAM errors to the AV. Specifically, we found that the invisible infrared lights (IR light) can successfully trigger the image sensor while human eyes cannot perceive IR lights. Moreover, the IR light appears magenta color in the camera, which triggers different pixels from the ambient visible light and can be selected as key points during the AV's SLAM process. By leveraging these features, we explore to i) generate invisible traffic lights, ii) create fake invisible objects, iii) ruin the in-car user experience, and iv) introduce SLAM errors to the AV. We implement the ICSL Attack by using off-the-shelf IR light sources and conduct an extensive evaluation on Tesla Model 3 and an enterprise-level autonomous driving platform under various environments and settings. We demonstrate the effectiveness of the ICSL Attack and prove that current autonomous vehicle companies have not yet considered the ICSL Attack, which introduces severe security issues. To secure the AV, by exploring unique features of the IR light, we propose a software-based detection module to defend against the ICSL Attack.

Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi network by 1.12 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee network (i.e., 31 times faster than the state-of-the-art flooding protocol).

To support ubiquitous computing for various applications (such as smart health, smart homes, and smart cities), the communication system requires to be ubiquitously available, ultra-low-power, high throughput, and low-latency. A passive communication system such as backscatter is desirable. However, existing backscatter systems cannot achieve all of the above requirements. In this paper, we present the first LTE backscatter (LScatter) system that leverages the continuous LTE ambient traffic for ubiquitous, high throughput and low latency backscatter communication. Our design is motivated by our observation that LTE ambient traffic is continuous (v.s. bursty and intermittent WiFi/LoRa traffic), which makes LTE ambient traffic a perfect signal source of a backscatter system. Our design addresses practical issues such as time synchronization, phase modulation, as well as phase offset elimination. We extensively evaluated our design using a testbed of backscatter hardware and USRPs in multiple real-world scenarios. Results show that our LScatter's performance is consistently orders of magnitude better than WiFi backscatter in all the above scenarios. For example, LScatter's throughput is 13.63Mbps, which is 368 times higher than the latest ambient WiFi backscatter system [54]. We also demonstrate the effectiveness of our system using two real-world applications.

Internet-of-things (IoT) devices are sharing the radio frequency band (e.g., 2.4 GHz ISM band). The exponentially increasing number of IoT devices introduces potential security issues at the gateway in IoT networks. In this paper, we introduce a set of new attacks through concealed jamming - an adversary pretends to be (or compromises) a legitimate WiFi device, then sends out WiFi packets to prevent ZigBee devices' communication or collide with ZigBee's packets. By doing this, concealed jamming has the potential to severely delay the reception of ZigBee packets that may contain important information (e.g., critical health data from wearables, fire alarms, and intrusion alarms). To defend against these attacks, we designed a novel ZigBee data extraction technique that can recover ZigBee data from the ZigBee packets that were collided with WiFi packets. We extensively evaluated our design in different real-world settings. The results show that ZigBee devices (protected by our proposed methods) achieve similar performance as those that are not under the concealed jamming attack. Moreover, compared with unprotected devices, their throughput is more than 15 times higher than the unprotected one that is under concealed jamming attacks.

The password has become today's dominant method of authentication. While brute-force attack methods such as HashCat and John the Ripper have proven unpractical, the research then switches to password guessing. State-of-the-art approaches such as the Markov Model and probabilistic context-free grammar (PCFG) are all based on statistical probability. These approaches require a large amount of calculation, which is time-consuming. Neural networks have proven more accurate and practical in password guessing than traditional methods. However, a raw neural network model is not qualified for cross-site attacks because each dataset has its own features. Our work aims to generalize those leaked passwords and improves the performance in cross-site attacks. In this paper, we propose GENPass, a multi-source deep learning model for generating “general” password. GENPass learns from several datasets and ensures the output wordlist can maintain high accuracy for different datasets using adversarial generation. The password generator of GENPass is PCFG+LSTM (PL). We are the first to combine a neural network with PCFG. Compared with Long short-term memory (LSTM), PL increases the matching rate by 16%-30% in cross-site tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The results demonstrate that the matching rate of GENPass is 20% higher than by simply mixing datasets in the cross-site test. Furthermore, we propose GENPass with probability (GENPass-pro), the updated version of GENPass, which can further increase the matching rate of GENPass.

Deep neural networks are vulnerable to adversarial examples which are inputs modified with unnoticeable but malicious perturbations.Most defending methods only focus on tuning the DNN itself, but we propose a novel defending method which modifies the input data to detect the adversarial examples.We establish a detection framework based on normalizing filters that can partially erase those perturbations by smoothing the input image or depth reduction work.The framework gives the decision by comparing the classification results of original input and multiple normalized inputs.Using several combinations of gaussian blur filter, median blur filter and depth reduction filter, the evaluation results reaches a high detection rate and achieves partial restoration work of adversarial examples in MNIST dataset.The whole detection framework is a low-cost highly extensible strategy in DNN defending works.

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi networks by 1.2 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee networks (i.e., 31 times faster than the state-of-the-art flooding protocol).

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same Industrial, Scientific, and Medical band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT , opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes’ priorities and reduce the delivery delay from high-priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi access point. We extensively evaluate ECT under different network settings, and results show that our ECT’s packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

This study presents a new method for detecting Shrew DDoS (Distributed Denial of Service) attacks and analyzes the characteristics of the Shrew DDoS attack. Shrew DDoS is periodic to be suitable for the server’s TCP (Transmission Control Protocol) timer. It has lower maximum to bypass peak detection. This periodicity makes it distinguishable from normal data packets. By proposing the CCID (Cross-Correlation Identity Distinction) method to distinguish the flow properties, it quantifies the difference between a normal flow and an attack flow. Simultaneously, we calculated the cross-correlation between the attack flow and the normal flow in three different situations. The server can use its own TCP flow timer to construct a periodic attack flow. The cross-correlation between Gaussian white noise and simulated attack flow is less than 0.3. The cross-correlation between single-door function and simulated attack flow is 0.28. The cross-correlation between actual attack flow and simulated attack flow is more than 0.8. This shows that we can quantitatively distinguish the attack effects of different signals. By testing 4 million data, we can prove that it has a certain effect in practice.

Password has become today's dominant method of authentication in social network. While the brute-force attack methods, such as HashCat and John the Ripper, are unpractical, the research then switches to the password guess. The state-of-the-art approaches, such as Markov Model and probabilistic context-free grammars(PCFG), are all based on statistical probability. These approaches have a low matching rate. The methods on neural network have been proved more accurate and practical for password guessing than traditional methods. However, a raw neural network model is not qualified for cross-sites attack since each data set has its own features. This paper proposes a general deep learning model for password guessing, called GENPass. GENPass can learn features from several data sets and ensure the output wordlist high accuracy in different data sets by using adversarial generation. The password generator of GENPass is PCFG+LSTM(PL), where LSTM is a kind of Recurrent Neural Network. We combine neural network with PCFG because we found people were used to set their passwords with meaningful strings. Compared with LSTM, PL increased the matching rate by 16%-30% in the cross-sites tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The result shows that the matching rate of GENPass is 20% higher than that of simply mixing those datasets in the cross-sites test.

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same ISM band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT, opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes' priorities and reduce the delivery delay from high priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi AP. We extensively evaluate ECT under different network settings and results show that our ECT's packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

Researchers have demonstrated the feasibility of detecting human motion behind the wall with radio frequency (RF) sensing techniques. With these techniques, an eavesdropper can monitor people's behavior from outside of the room without the need to access the room. This introduces a severe privacy-leakage issue. To address this issue, we propose Aegis, an interference-negligible RF sensing shield that i) incapacitates the RF sensing of eavesdroppers that work at any unknown locations outside of the protected area; ii) has minimum interference to the ongoing WiFi communication; and iii) preserves authorized RF sensing inside the private region. Our extensive evaluation shows that when Aegis is activated, it i) has a negligible impact on the legitimate sensing system; ii) effectively prevents the illegitimate sensing system from sensing human motions. Moreover, the ongoing data communication throughput is even increased.

The exponentially increasing number of Internet-of-Thing (IoT) devices introduces a spectrum crisis in the shared ISM band. However, it also introduces opportunities for conducting radio frequency (RF) sensing using pervasively available signals generated by heterogeneous IoT devices. In this paper, we explore how to leverage the ambient wireless traffic that i) generated by uncontrollable IoT devices and ii sensed by ambient noise floor measurements (a widely available metric in IoT devices) for human gesture recognition. Specifically, we introduce our system EAR, which can conduct fine-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources. We conducted extensive evaluations in both residential and academic buildings. Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%. Moreover, EAR can recognize fine-grained human gestures with high accuracy even under extremely low traffic rate (i.e., 4%) from uncontrollable ambient signal sources.

Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.

Energy internet is now an industry hot spot which enables the interconnection and sharing of energy just like the Internet. Inspired by the concept of energy internet, this paper will focus on a designed energy distribution network, using city bus lines running Electric Vehicles (EV) to achieve electric power storage and transmission. This network is made of renewable energy sources providing power, charge stations for power exchange and bus lines as delivery, electric buses serving as the carriers of flowing power in network. This paper will mainly discuss and solve the problem of placing charge stations on city bus map to compose the network framework. Our work includes two optimization algorithms using some ideas of graph theory, simulating with real-world transporting data of different city maps and analyzing the results to evaluate efficiency as well as advantages and disadvantages on algorithms and data sets.