SelFormaly: Towards Task-Agnostic Unified Anomaly Detection

The core idea of visual anomaly detection is to learn the normality from normal images, but previous works have been developed specifically for certain tasks, leading to fragmentation among various tasks: defect detection, semantic anomaly detection, multi-class anomaly detection, and anomaly clustering. This one-task-one-model approach is resource-intensive and incurs high maintenance costs as the number of tasks increases. This paper presents SelFormaly, a universal and powerful anomaly detection framework. We emphasize the necessity of our off-the-shelf approach by pointing out a suboptimal issue with fluctuating performance in previous online encoder-based methods. In addition, we question the effectiveness of using ConvNets as previously employed in the literature and confirm that self-supervised ViTs are suitable for unified anomaly detection. We introduce back-patch masking and discover the new role of top k-ratio feature matching to achieve unified and powerful anomaly detection. Back-patch masking eliminates irrelevant regions that possibly hinder target-centric detection with representations of the scene layout. The top k-ratio feature matching unifies various anomaly levels and tasks. Finally, SelFormaly achieves state-of-the-art results across various datasets for all the aforementioned tasks.Comment: 11 pages, 7 figure

Duo: Software Defined Intrusion Tolerant System Using Dual Cluster

An intrusion tolerant system (ITS) is a network security system that is composed of redundant virtual servers that are online only in a short time window, called exposure time. The servers are periodically recovered to their clean state, and any infected servers are refreshed again, so attackers have insufficient time to succeed in breaking into the servers. However, there is a conflicting interest in determining exposure time, short for security and long for performance. In other words, the short exposure time can increase security but requires more servers to run in order to process requests in a timely manner. In this paper, we propose Duo, an ITS incorporated in SDN, which can reduce exposure time without consuming computing resources. In Duo, there are two types of servers: some servers with long exposure time (White server) and others with short exposure time (Gray server). Then, Duo classifies traffic into benign and suspicious with the help of SDN/NFV technology that also allows dynamically forwarding the classified traffic to White and Gray servers, respectively, based on the classification result. By reducing exposure time of a set of servers, Duo can decrease exposure time on average. We have implemented the prototype of Duo and evaluated its performance in a realistic environment

First Experimental Result of Power Analysis Attacks on a FPGA Implementation of LEA

The lightweight encryption algorithm (LEA) is a 128-bit block cipher introduced in 2013. It is based on Addition, rotation, XOR operations for 32-bit words. Because of its structure,it is useful for several devices to achieve a high speed of encryption and low-power consumption.However, side-channel attacks on LEA implementations have not been examined.In this study, we perform a power analysis attack on LEA. We implemented LEA with 128-bit key size on FPGA in a straightforward manner. Our experimental results show that we can successfully retrieve a 128-bit master key by attacking a first round encryption

Correlation Enhanced Distribution Adaptation for Prediction of Fall Risk

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different data sources with labeled and unlabeled samples to predict a patient\u27s condition poses a significant challenge. Traditional machine learning models assume that data for new patients follow a similar distribution. If the data does not satisfy this assumption, the trained models do not achieve the expected accuracy, leading to potential misdiagnosing risks. To address this issue, we utilize domain adaptation (DA) techniques, which employ labeled data from one or more related source domains. These DA techniques promise to tackle discrepancies in multiple data sources and achieve a robust diagnosis for new patients. In our research, we have developed an unsupervised DA model to align two domains by creating a domain-invariant feature representation. Subsequently, we have built a robust fall-risk prediction model based on these new feature representations. The results from simulation studies and real-world applications demonstrate that our proposed approach outperforms existing models

Detection of Fall Risk Behaviors in Patients with Severe Mobility Issues Using FMCW Radar: Sitting Up and Sitting on the Side of the Bed

This study aimed to detect fall risk behaviors using radar—a non-contact sensor—to prevent falling accidents, which are one of the most fatal problems faced by older adults. Hospitals and nursing homes often have patients who cannot move alone without caregivers. In this context, the process of a patient sitting up from a lying-down position shortly before standing up has been observed as a fall risk behavior. This study added movement information as a new characteristic feature to the range and velocity information used in conventional radar-based behavior recognition studies. Performance comparisons confirmed that the addition of movement information performs excellently in detecting risk situations. Furthermore, a bidirectional long short-term memory model was trained using a feature to predict risk situations. The model exhibited accuracy, recall, and precision rates of 93.84%, 88.57%, and 99.07%, respectively. Additionally, its performance in detecting fall risk behavior was verified by conducting experiments involving continuous behaviors

Tamper Resistant Software by Integrity-Based Encryption

Abstract. There are many situations in which it is desirable to protect a piece of software from illegitimate tampering once it gets distributed to the users. Protecting the software code means some level of assurance that the program will execute as expected even if it encounters the illegitimated modifications. We provide the method of protecting software from unauthorized modification. One important technique is an integrity-based encryption, by which a program, while running, checks itself to verify that it has not been modified and conceals some privacy sensitive parts of program

A Secure and Efficient Audit Mechanism for Dynamic Shared Data in Cloud Storage

With popularization of cloud services, multiple users easily share and update their data through cloud storage. For data integrity and consistency in the cloud storage, the audit mechanisms were proposed. However, existing approaches have some security vulnerabilities and require a lot of computational overheads. This paper proposes a secure and efficient audit mechanism for dynamic shared data in cloud storage. The proposed scheme prevents a malicious cloud service provider from deceiving an auditor. Moreover, it devises a new index table management method and reduces the auditing cost by employing less complex operations. We prove the resistance against some attacks and show less computation cost and shorter time for auditing when compared with conventional approaches. The results present that the proposed scheme is secure and efficient for cloud storage services managing dynamic shared data

Prediction of Fall Risk Among Community-Dwelling Older Adults Using a Wearable System

Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor\u27s potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls

Fault-Tolerant Multicasting in Multistage Interconnection Networks

Abstract In this paper, we study fault-toleran