Traditionally, authentication systems have followed a non-feedback approach, requiring users to present credentials before accessing resources. Over time, users have become accustomed to this oblivious form of authentication. However, this model offers no opportunity for users to provide feedback that could enhance the system's effectiveness. Similarly, biometric-based implicit authentication, while transparent, often excludes users entirely from the feedback loop. Incorporating user feedback into authentication systems has the potential to significantly improve their performance. Achieving this, however, requires a novel framework capable of standardizing user input, extracting meaningful information from feedback, and integrating the user more closely into the system. To this end, we challenge conventional authentication paradigms and introduce User-Centered Implicit Authentication (UserIA)-a customizable approach that extends beyond the limits of traditional schemes. UserIA maintains the transparency of implicit authentication while delivering improved accuracy and reduced overhead. To enable secure feedback-driven feature adaptation, UserIA introduces a new technique calledbehavior alignment. Additionally, it appliesuser operant conditioningfrom psychology to reinforce user behavior and further enhance authentication accuracy. We have implemented and thoroughly evaluated UserIA in a real-world environment. Experimental results demonstrate that UserIA achieves a lower Equal Error Rate (EER) and consumes less time and energy compared to existing methods.

These recent years have witnessed that convolutional neural network (CNN)-based methods for detecting infrared small targets have achieved outstanding performance. However, these methods typically employ standard convolutions, neglecting to consider the spatial characteristics of the pixel distribution of infrared small targets. Therefore, we propose a novel pinwheel-shaped convolution (PConv) as a replacement for standard convolutions in the lower layers of the backbone network. PConv better aligns with the Gaussian-like spatial distribution of infrared small target, improves feature extraction, significantly expands the receptive field, and introduces only a minimal increase in parameters. Additionally, while recent loss functions combine scale and location losses, they do not adequately account for the varying sensitivity of these losses across different target scales, limiting detection performance on dim-small targets. To overcome this, we propose a scale-based dynamic (SD) Loss that dynamically adjusts the influence of scale and location losses based on target size, improving the network's ability to detect targets of varying scales. We construct a new benchmark, SIRST-UAVB, which is the largest and most challenging dataset to date for real-shot single-frame infrared small target detection. Lastly, by integrating PConv and SD Loss into the latest small target detection algorithms, we achieved significant performance improvements on IRSTD-1K and our SIRST-UAVB dataset, validating the effectiveness and generalizability of our approach.

In recent years, continuous authentication, a method of ongoing identity verification aimed at enhancing cybersecurity protection, has been receiving increasing attention. It utilizes users’ behavior data sampled from various sensors, but due to the resource limitations of smart devices, both the data and computation (including data processing, transmission, and training) have to be offloaded. However, the offloading will introduce more security vulnerabilities. In this paper, we propose LightSentinel, a lightweight continuous user identification and anomaly detection system. LightSentinel can derive users’ key behavior patterns and detect behavior changes during usage without the need for data and computation offloading, and a probability chain has been established for each user to improve the identification accuracy of the system. In our experiment, we deployed LightSentinel on Android devices and conducted evaluations to assess its identification accuracy, energy consumption, and computational efficiency. The results demonstrate that LightSentinel consumes lower power consumption and requires less computation than other applications and IA schemes. Moreover, its accuracy makes it suitable for deployment as an anomaly detection system on smart devices.

Leveraging users' behavioral data sampled by various sensors during the identification process, implicit authentication (IA) relieves users from explicit actions such as remembering and entering passwords. Various IA schemes have been proposed based on different behavioral and contextual features such as gait, touch, and GPS. However, existing IA schemes suffer from false positives, i.e., falsely accepting an adversary, and false negatives, i.e., falsely rejecting the legitimate user due to users' behavior change and noise. To deal with this problem, we propose BubbleMap (BMap), a framework that can be seamlessly incorporated into any existing IA system to balance between security (reducing false positives) and usability (reducing false negatives) as well as reducing the equal error rate (EER). To evaluate the proposed framework, we implemented BMap on five state-of-the-art IA systems. We also conducted an experiment in a real-world environment from 2016 to 2020. Most of the experimental results show that BMap can greatly enhance the IA schemes' performances in terms of the EER, security, and usability, with a small amount of penalty on energy consumption.

Implicit authentication (IA) is gaining popularity over recent years due to its use of user behavior as the main input, relieving users from explicit actions such as remembering and entering passwords. However, such convenience comes with a cost of authentication accuracy and delay which we propose to improve in this paper. Authentication accuracy deteriorates as users' behaviors change as a result of mood, age, a change of routine, etc. Current authentication systems handle failed authentication attempts by locking the users out of their mobile devices. It is unsuitable for IA whose accuracy deterioration induces a high false reject rate, rendering the IA system unusable. Furthermore, existing IA systems leverage computationally expensive machine learning, which can introduce a large authentication delay. It is challenging to improve the authentication accuracy of these systems without sacrificing authentication delay. In this paper, we propose a multi-level privilege control (MPC) scheme that dynamically adjusts users' access privilege based on their behavior change. MPC increases the system's confidence in users' legitimacy even when their behaviors deviate from historical data, thus improving authentication accuracy. It is a lightweight feature added to the existing IA schemes that helps avoid frequent and expensive retraining of machine learning models, thus improving authentication delay. We demonstrate that MPC increases authentication accuracy by 18.63% and reduces authentication delay by 7.02 minutes on average, using a public dataset that contains comprehensive user behavior data.

In this paper, we propose a scheme to protect the Software Defined Network(SDN) controller from Distributed Denial-of-Service(DDoS) attacks. We first predict the amount of new requests for each openflow switch periodically based on Taylor series, and the requests will then be directed to the security gateway if the prediction value is beyond the threshold. The requests that caused the dramatic decrease of entropy will be filtered out and rules will be made in security gateway by our algorithm; the rules of these requests will be sent to the controller. The controller will send the rules to each switch to make them direct the flows matching with the rules to the honey pot. The simulation shows the averages of both false positive and false negative are less than 2%.

As accurate malware detection on mobile devices requires fast process of a large number of application traces, cloud-based malware detection can utilize the data sharing and powerful computational resources of security servers to improve the detection performance. In this paper, we investigate the cloud-based malware detection game, in which mobile devices offload their application traces to security servers via base stations or access points in dynamic networks. We derive the Nash equilibrium (NE) of the static malware detection game and present the existence condition of the NE, showing how mobile devices share their application traces at the security server to improve the detection accuracy, and compete for the limited radio bandwidth, the computational and communication resources of the server. We design a malware detection scheme with Q-learning for a mobile device to derive the optimal offloading rate without knowing the trace generation and the radio bandwidth model of other mobile devices. The detection performance is further improved with the Dyna architecture, in which a mobile device learns from the hypothetical experience to increase its convergence rate. We also design a post-decision state learning-based scheme that utilizes the known radio channel model to accelerate the reinforcement learning process in the malware detection. Simulation results show that the proposed schemes improve the detection accuracy, reduce the detection delay, and increase the utility of a mobile device in the dynamic malware detection game, compared with the benchmark strategy.

Abstract In this paper, we propose a novel scheme that can achieve data privacy by hybrid cloud, which consists of public and private cloud, and reduce storage and computation in private cloud, as well as communication overhead between private and public cloud. Meanwhile, we propose a novel algorithm to process private image data. Our experimental results show that (1) our algorithm achieves data privacy but only takes about 1/1000, time of the Advanced Encryption Standard algorithm and (2) the delay of our hybrid cloud approach (including the private and public cloud communications) is only 3%–5% more compared with the traditional public cloud‐only approach. Copyright © 2015 John Wiley & Sons, Ltd.

The growing concerns about the privacy of data stored in public cloud have hindered the widespread adoption of cloud. On one hand, large part of data, such as medical data, has a lot of images, and this kind of data may be private. On the other hand, the cloud service providers have the full access of data, and they may bleach the data for financial or other reasons. The traditional method to protect the privacy of data is to employ cryptographic algorithms, which unavoidably introduces heavy computation. Another way is hybrid cloud consisting of public and private cloud. The sensitive data is separated from non-sensitive data, and only the non-sensitive data is outsourced to public cloud. If we use hybrid cloud method directly, all the private images have to be stored in private cloud, which makes the adoption of cloud computing meaningless. Besides achieving data privacy, we should reduce computation and storage overhead in private cloud, as well as communication overhead between private and public cloud. In this paper, we propose a novel scheme to achieve the above goals. We test our scheme in real network environments (including Amazon EC2). We also propose a novel algorithm to process private image data. Our experimental results show that: (1) Our algorithm achieves data privacy but only takes about 1/1,000 the time of the AES algorithm. (2) The delay of our hybrid cloud approach (including the private and public cloud communications) is only 3%-5% more compared to the traditional public-cloud-only approach.

In this paper, we present an efficient routing protocol for Mobile Hybrid Wireless Networks (MHWNs), which consists of an infrastructure wireless network and a few mobile ad hoc networks. MHWNs have several advantages over traditional wireless networks (such as cellular networks and WLANs). For example, MHWNs can achieve higher throughput, larger coverage, and better load balancing cross cells. In this paper, we present an efficient routing protocol for MHWNs. The protocol utilizes node location information, moving speed, and remaining battery life for routing decision. The protocol is energy efficient (for mobile nodes) and can find robust routing path. We evaluate its performance by NS-2 simulations. Our results show that the protocol significantly reduces the routing overhead, and the routing path is relatively stable.