Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information, spreading malware, or simply to increase their advertisement revenue. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted to be published in app markets. We show that for the problem of counterfeit detection, a novel approach of combiningcontent embeddingsandstyle embeddings(given by the Gram matrix of CNN feature maps) outperforms the baseline methods for image similarity such as SIFT, SURF, LATCH, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that, content, style, and combined embeddings increaseprecision@kandrecall@kby 10-15 percent and 12-25 percent, respectively when retrieving five nearest neighbours. Second specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12 and 14 percent increase inprecision@kandrecall@k, respectively compared to the baseline methods. We also show that adding text embeddings further increases the performance by 5 and 6 percent in terms ofprecision@kandrecall@k, respectively when$k$is five. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a conservative assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.

Counterfeit apps impersonate existing popular apps in attempts to misguide users. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. In this paper, we propose a novel approach of combining content embeddings and style embeddings generated from pre-trained convolutional neural networks to detect counterfeit apps. We present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 apps. Under conservative assumptions, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.

A navigation algorithm for source seeking in a sensor network environment is presented in this paper. The solution consists of a gradient-free approach and maximum likelihood topology maps of sensor networks. The robot is navigated using an angular velocity limited by maximum and minimum constants, and by sensor measurements gathered by sensors that are close to robot's current location. The location of the robot is calculated using sensor topology coordinates, which is an alternative to the physical coordinate system and does not depend on physical distance measurement techniques such as received signal strength. However, actual physical distances are hidden in topology maps due to nonlinear distortions compared to physical distance between nodes. Thus, the proposed control law does not depend on any distance-based information. The performance of the algorithm is evaluated using computer simulations and experiments with a real mobile robot.

The use of directional antennas in mobile wireless networks has emerged recently due to their numerous advantages such as high gain, low interference and low transmission power to reach the same distance as omni antennas. However, directional antennas limit the coverage and connectivity in the network. In this paper, we propose an algorithm to calculate a topology preserving map for wireless sensor networks using directional antennas. In the literature, topology mapping have been proposed for sensor networks equipped with omni antennas; however, to the best of our knowledge none of the existing work has considered the use of directional antennas in sensor network topology mapping. The application of existing algorithms in directional sensor networks will consume significantly high energy, as it requires transmitting the number of sectors times more beacon messages to cover the omini antenna range. Thus in this paper, a novel beacon forwarding algorithm limiting the number of forwarding sectors based on packet received direction is proposed. The simulation results show that it is possible to generate an accurate topology preserving map can be generated by only forwarding beacons in total of 135° angle. Moreover, the energy and the number of edges used to generate the topology map can be reduced by nearly 50% compared to the topology map generation with omni antennas based on received signal strength and hop count.

Millimeter wave communication shows promise in realizing next generation wireless sensor networks for bandwidth demanding applications. Despite its support of multi Gbps data rates, MmWaves requires unobstructed line-of-sight and suffers from heavy path losses. Overcoming these in complex 3D environments requires sectored antenna arrays with narrow beam widths and adaptive beamforming. Therefore, network topology maps would be significant than ever in millimeter wave sensor networks. Traditional topology mapping algorithms rely on omnidirectional transmission and reception and are thus not tailored to such networks. A novel topology mapping algorithm, Millimeter Wave Topology Map (MmTM) is proposed for 3D deployments, which take advantage of the directional information available from beamforming antennas as well as their beam steering capability. An autonomous robot traverses the network recording the packet reception from different nodes along with the receiving antenna sector ID that delivers the packet with highest signal quality. The techniques used in standard IEEE 802.11ad protocol are used for the optimum sector selection and collision avoidance. MmTM is evaluated using two realistic sensor network environments and compared with prominent localization approaches based on received signal strength and hop count. The results show that proposed algorithm has a less than 0.7m distance error and more than 50% of nodes are located in the correct direction, which is 7m and 35% improvement in distance error and sector displacement matrices compared to other algorithms.

A distributed target search and predictive approach that uses wireless sensor network topology map is presented in this paper. The decision-making and information gathering is distributed, and the target prediction is based on the weighted least square method. The knowledge of target location and moving direction is obtained by time sets recorded by each sensor node. Time set is a collection of time values that describes when the target was detected by a particular node. All the locations are calculated in a topology map, which is an economical alternative to physical map. The results show that the proposed method has been able to search the target, even though the target changes the moving pattern from time to time. In addition, the robot avoids the obstacles and searches for the target in an effective way.

Topology maps represent the layout arrangement of nodes while maintaining the connectivity. As it is extracted using connectivity information only, it does not accurately represent the physical layout such as physical voids, shape, and relative distances among physical positions of sensor nodes. A novel concept Maximum Likelihood-Topology Maps for Wireless Sensor Networks is presented. As it is based on a packet reception probability function, which is sensitive to the distance, it represents the physical layout more accurately. In this paper, we use a binary matrix recorded by a mobile robot representing the reception of packets from sensor nodes by the mobile robot at different locations along the robots trajectory. Maximum likelihood topology coordinates are then extracted from the binary matrix by using a packet receiving probability function. Also, the robot trajectory is automated to avoid the obstacles and cover the entire network within least possible amount of time. The result shows that our algorithm generates topology maps for various network shapes under different environmental conditions accurately, and that it outperforms the existing algorithms by representing the physical layout of the network more accurately.

Wireless sensor networks may contain hundreds or thousands number of sensor nodes, monitoring specified parameters in the environment. These sensor nodes have the ability to communicate with each other or directly to an external base station. Current clustering algorithms provide an effective way to prolong the network lifetime. However, gathering information and communicating with each other is crucial in an emergency situation. To address this problem, the paper proposes an emergency response unequal clustering algorithm for wireless sensor networks. The proposed algorithm has been able to utilize the energy usage of the network with the novel approach in unequal clustering. Furthermore, this addresses the node failures in an emergency situation. The simulation result shows that the proposed algorithm has optimized the energy usage and reduce the communication failure compared to other existing algorithms.