Abstract Human gait recognition in a multicamera environment is a challenging task in biometrics because of the presence of the large pose and variations in illumination among different views. In this work, to address the problem of variations in view, we present a novel stacked autoencoder for learning discriminant view‐invariant gait representations. Our autoencoder can efficiently and progressively translate skeleton joint coordinates from any arbitrary view to a common canonical view without requiring the prior estimation of the view angle or covariate type and without losing temporal information. Then, we construct a discriminative gait feature vector by fusing the encoded features with two other spatiotemporal gait features to feed into the main recurrent neural network. Experimental evaluations of the challenging CASIA A and CASIA B gait datasets demonstrate that the proposed approach outperformed other state‐of‐the‐art methods on single‐view gait recognition. In particular, the proposed method achieved 46.31% and 33.86% average correct class recognition on probe set ProbeBG and ProbeCL, respectively, of the CASIA B dataset while considering the view variation; this is 0.3% and 30.68% higher than previous best‐performing methods. Furthermore, in cross‐view recognition, our method shows better results over other state‐of‐the‐art methods when the view‐angle variation is large than 36°.

Caller ID (caller identification) is a service provided by telephone operators where the phone number and/or the name of the caller is transmitted to inform the callee who is calling. Today, most people trust the caller ID information and some banks even use Caller ID to authenticate customers. However, with the proliferation of smartphones and VoIP, it is easy to spoof caller ID information by installing a particular application on the smartphone or by using service providers that offer Caller ID spoofing. As the phone network is fragmented between countries and companies and upgrades of old hardware is costly, no mechanism is available today to let end-users easily detect Caller ID spoofing attacks. In this article, we propose a new approach of using end-to-end caller ID verification schemes that leverage features of the existing phone network infrastructure (CallerDec). We design an SMS-based and a timing-based version of CallerDec that works with existing combinations of landlines, cellular and VoIP networks and can be deployed at the liberty of the users. We implemented both CallerDec schemes as an App for Android-based phones and validated their effectiveness in detecting spoofing attacks in various scenarios.

The Global Positioning System (GPS) is widely used in critical infrastructures but is vulnerable to radio frequency (RF) interference. A common source of interference are commercial drivers that use GPS jammers to circumvent vehicle tracking systems. Existing mechanisms to detect and identify such interference emitting vehicles on roadways require a large number of specialized detectors or a manual observation process. In this paper, we design a practical, automated system to facilitate enforcement actions. Our system combines information from roadside monitoring points at key locations along the roadway as well as mobile detectors (e.g., smartphones and other mobile GPS systems). Rather than attempting precise localization at a given time, the system exploits the inherent variation in driving speeds and the resulting diverging trajectories of vehicles to uniquely identify the interfering vehicle. Through our experiments on a local highway with a vehicle transmitting interference in the 900MHz ISM band, we found that the vehicle identification rate of our mechanism is 65% for a single-point setup and 100% for a two-point setup. We performed 200 hours of passive monitoring of GPS L1 band on roadways and found two episodes of real interference. We also demonstrate that our mobile detector-based crowdsourced smartphone profiles are sufficiently consistent in time and space to enable reliable interference detection.

Caller ID (caller identification) is a service provided by telephone carriers to transmit the phone number and/or the name of a caller to a callee. Today, most people trust the caller ID information, and it is increasingly used to authenticate customers (e.g., by banks or credit card companies). However, with the proliferation of smartphones and VoIP, it is easy to spoof caller ID by installing corresponding Apps on smartphones or by using fake ID providers. As telephone networks are fragmented between enterprises and countries, no mechanism is available today to easily detect such spoofing attacks. This vulnerability has already been exploited with crucial consequences such as faking caller IDs to emergency services (e.g., 9-1-1) or to commit fraud. In this paper, we propose an end-to-end caller ID verification mechanism CallerDec that works with existing combinations of landlines, cellular and VoIP networks. CallerDec can be deployed at the liberty of users, without any modification to the existing infrastructures. We implemented our scheme as an App for Android-based phones and validated the effectiveness of our solution in detecting spoofing attacks in various scenarios.

Research on smart meters has shown that fine-grained energy usage data poses privacy risks since it allows inferences about activities inside the home. While smart meter deployments are very limited, more than 40 million meters in the United States have been equipped with Automatic Meter Reading (AMR) technology over the past decades. AMR utilizes wireless communication for remotely collecting usage data from electricity, gas, and water meters. Yet to the best of our knowledge, AMR has so far received no attention from the security research community. In this paper, we conduct a security and privacy analysis of this technology. Based on our reverse engineering and experimentation, we find that the technology lacks basic security measures to ensure privacy, integrity, and authenticity of the data. Moreover, the AMR meters we examined continuously broadcast their energy usage data over insecure wireless links every 30s, even though these broadcasts can only be received when a truck from the utility company passes by. We show how this design allows any individual to monitor energy usage from hundreds of homes in a neighborhood with modest technical effort and how this data allows identifying unoccupied residences or people's routines. To cope with the issues, we recommend security remedies, including a solution based on defensive jamming that may be easier to deploy than upgrading the meters themselves.

The rapidly increasing data usage and overload in mobile broadband networks has driven mobile network providers to actively detect and bill customers who tether tablets and laptops to their mobile phone for mobile Internet access. However, users may not be willing to pay additional fees only because they use their bandwidth dierently, and may consider tethering detection as violation of their privacy. Furthermore, accurate tethering detection is becoming harder for providers as many modern smartphones are under full control of the user, running customized, complex software and applications similar to desktop systems.

Jamming attacks are especially harmful to the reliability of wireless communication, as they can effectively disrupt communication between any node pairs. Existing jamming defenses primarily focus on repairing connectivity between adjacent nodes. In this paper, we address jamming at the network level and focus on restoring the end-to-end data delivery through multipath routing. As long as all paths do not fail concurrently, the end-to-end path availability is maintained. Prior work in multipath selection improves routing availability by choosing node-disjoint paths or link-disjoint paths. However, through our experiments on jamming effects using MicaZ nodes, we show that disjointness is insufficient for selecting fault-independent paths. Thus, we address multipath selection based on the knowledge of a path's availability history. Using Availability History Vectors (AHVs) of paths, we present a centralized AHV-based algorithm to select fault-independent paths, and a distributed AHV-based routing protocol built on top of a classic routing algorithm in ad hoc networks. Our extensive simulation results validate that both AHV-based algorithms are effective in overcoming the jamming impact by maximizing the end-to-end availability of the selected paths.

Jamming attacks are especially harmful to the reliability of wireless communication, as they can effectively disrupt communication. Existing jamming defenses primarily focus on repairing connectivity between adjacent nodes. In this paper, we address jamming at the network level and focus on restoring the end-to-end data delivery through multipath routing. As long as all paths do not fail concurrently, the end-to-end path availability is maintained. Prior work in multipath selection improves routing by choosing node-disjoint paths or link-disjoint paths. However, through our experiments on jamming effects using MicaZ nodes, we show that topological disjointness is insufficient for selecting fault-independent paths. Thus, we address multipath selection based on the knowledge of a path's availability history. Using Availability History Vectors (AHVs) of paths, we present an AHV-based Link-State (ALS) algorithm to select fault-independent paths. Our extensive simulation results validate that the ALS algorithm is effective in overcoming the jamming impact by maximizing the end-to-end availability of the selected paths.