搜索结果：prevention

DDoS attacks are one of the most prevalent and harmful cybersecurity threats faced by organizations and individuals today. In recent years, the complexity and frequency of DDoS attacks have increased significantly, making it challenging to detect and mitigate them effectively. The study analyzes various types of DDoS attacks, including volumetric, protocol, and application layer attacks, and discusses the characteristics, impact, and potential targets of each type. It also examines the existing techniques used for DDoS attack detection, such as packet filtering, intrusion detection systems, and machine learning-based approaches, and their strengths and limitations. Moreover, the study explores the prevention techniques employed to mitigate DDoS attacks, such as firewalls, rate limiting , CPP and ELD mechanism. It evaluates the effectiveness of each approach and its suitability for different types of attacks and environments. In conclusion, this study provides a comprehensive overview of the different types of DDoS attacks, their detection, and prevention techniques. It aims to provide insights and guidelines for organizations and individuals to enhance their cybersecurity posture a

In the light of increasing clues on social media impact on self-harm and suicide risks, there is still no evidence on who are and how factually engaged in suicide-related online behaviors. This study reports new findings of high-performance supercomputing investigation of publicly accessible big data sourced from one of the world-largest social networking site. Three-month supercomputer searching resulted in 570,156 young adult users who consumed suicide-related information on social media. Most of them were 21-24 year olds with higher share of females (58%) of predominantly younger age. Every eight user was alarmingly engrossed with up to 15 suicide-related online groups. Evidently, suicide groups on social media are highly underrated public health issue that might weaken the prevention efforts. Suicide prevention strategies that target social media users must be implemented extensively. While major gap in functional understanding of technologies relevance for use in public mental health still exists, current findings act for better understanding digital technologies utility for translational advance and offer relevant evidence-based framework for improving suicide prevention in g

Human papillomavirus (HPV) infection is the most common sexually transmitted infection in the world. Persistent oncogenic Human papillomavirus infection has been a leading threat to global health and can lead to serious complications such as cervical cancer. Prevention interventions including vaccination and screening have been proved effective in reducing the risk of HPV-related diseases. In recent decades, computational epidemiology has been serving as a very useful tool to study HPV transmission dynamics and evaluation of prevention strategies. In this paper, we conduct a comprehensive literature review on state-of-the-art computational epidemic models for HPV disease dynamics, transmission dynamics, as well as prevention efforts. We summarise current research trends, identify gaps in the present literature, and identify future research directions with potential in accelerating the containment and/or elimination of HPV infection.

Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.

Empirical studies show that preference for prevention versus treatment remains a subject of debate. We build a paradigm model combining a utility game for the individual-level dilemma of prevention versus treatment, and a compartmental model for the epidemic dynamic. We assume that individuals arrive to maximize the utility of voluntary prevention, as the epidemic reaches an endemic level alleviated by prevention and treatment. We thus obtain an expression for the asymptotic prevention coverage. Notably, we obtain that, if the relative cost of prevention versus treatment is sufficiently low, epidemics may be averted through the use of prevention alone.

One fundamental limitation to the research of bird strike prevention is the lack of a large-scale dataset taken directly from real-world airports. Existing relevant datasets are either small in size or not dedicated for this purpose. To advance the research and practical solutions for bird strike prevention, in this paper, we present a large-scale challenging dataset AirBirds that consists of 118,312 time-series images, where a total of 409,967 bounding boxes of flying birds are manually, carefully annotated. The average size of all annotated instances is smaller than 10 pixels in 1920x1080 images. Images in the dataset are captured over 4 seasons of a whole year by a network of cameras deployed at a real-world airport, covering diverse bird species, lighting conditions and 13 meteorological scenarios. To the best of our knowledge, it is the first large-scale image dataset that directly collects flying birds in real-world airports for bird strike prevention. This dataset is publicly available at https://airbirdsdata.github.io/.

Traditional Chinese medicine (TCM) has played an important role in the prevention and control of the novel coronavirus pneumonia (COVID-19), and community prevention has become the most essential part in reducing the spread risk and protecting populations. However, most communities use a uniform TCM prevention program for all residents, which violates the "treatment based on syndrome differentiation" principle of TCM and limits the effectiveness of prevention. In this paper, we propose an intelligent optimization method to develop diversified TCM prevention programs for community residents. First, we use a fuzzy clustering method to divide the population based on both modern medicine and TCM health characteristics; we then use an interactive optimization method, in which TCM experts develop different TCM prevention programs for different clusters, and a heuristic algorithm is used to optimize the programs under the resource constraints. We demonstrate the computational efficiency of the proposed method and report its successful application to TCM-based prevention of COVID-19 in 12 communities in Zhejiang province, China, during the peak of the pandemic.

Robots are more capable of achieving manipulation tasks for everyday activities than before. But the safety of manipulation skills that robots employ is still an open problem. Considering all possible failures during skill learning increases the complexity of the process and restrains learning an optimal policy. Beyond that, in unstructured environments, it is not easy to enumerate all possible failures beforehand. In the context of safe skill manipulation, we reformulate skills as base and failure prevention skills where base skills aim at completing tasks and failure prevention skills focus on reducing the risk of failures to occur. Then, we propose a modular and hierarchical method for safe robot manipulation by augmenting base skills by learning failure prevention skills with reinforcement learning, forming a skill library to address different safety risks. Furthermore, a skill selection policy that considers estimated risks is used for the robot to select the best control policy for safe manipulation. Our experiments show that the proposed method achieves the given goal while ensuring safety by preventing failures. We also show that with the proposed method, skill learning is

Persuasion can be a complex process. Persuaders may need to use a high degree of sensitivity to understand a persuadee's states, traits, and values. They must navigate the nuanced field of human interaction. Research on persuasive systems often overlooks the delicate nature of persuasion, favoring "one-size-fits-all" approaches and risking the alienation of certain users. This study examines the considerations made by professional burglary prevention advisors when persuading clients to enhance their home security. It illustrates how advisors adapt their approaches based on each advisee's states and traits. Specifically, the study reveals how advisors deviate from intended and technologically supported practices to accommodate the individual attributes of their advisees. It identifies multiple advisee-specific aspects likely to moderate the effectiveness of persuasive efforts and suggests strategies for addressing these differences. These findings are relevant for designing personalized persuasive systems that rely on conversational modes of persuasion.

Prevention is better than cure. This old truth applies not only to the prevention of diseases but also to the prevention of issues with AI models used in medicine. The source of malfunctioning of predictive models often lies not in the training process but reaches the data acquisition phase or design of the experiment phase. In this paper, we analyze in detail a single use case - a Kaggle competition related to the detection of abnormalities in X-ray lung images. We demonstrate how a series of simple tests for data imbalance exposes faults in the data acquisition and annotation process. Complex models are able to learn such artifacts and it is difficult to remove this bias during or after the training. Errors made at the data collection stage make it difficult to validate the model correctly. Based on this use case, we show how to monitor data and model balance (fairness) throughout the life cycle of a predictive model, from data acquisition to parity analysis of model scores.

It is imperative to develop an intrusion prevention system (IPS), specifically designed for autonomous robotic systems. This is due to the unique nature of these cyber-physical systems (CPS), which are not merely typical distributed systems. These systems employ their own systems software (i.e. robotic middleware and frameworks) and execute distinct components to facilitate interaction with various sensors and actuators, and other robotic components (e.g. cognitive subsystems). Furthermore, as cyber-physical systems, they engage in interactions with humans and their physical environment, as exemplified by social robots. These interactions can potentially lead to serious consequences, including physical damage. In response to this need, we have designed and implemented RIPS, an intrusion prevention system tailored for robotic applications based on ROS 2, the framework that has established itself as the de facto standard for developing robotic applications. This manuscript provides a comprehensive exposition of the issue, the security aspects of ROS 2 applications, and the key points of the threat model we created for our robotic environment. It also describes the architecture and th

Wildfires pose a serious threat to the environment of the world. The global wildfire season length has increased by 19% and severe wildfires have besieged nations around the world. Every year, forests are burned by wildfires, causing vast amounts of carbon dioxide to be released into the atmosphere, contributing to climate change. There is a need for a system which prevents, detects, and suppresses wildfires. The AI based Wildfire Prevention, Detection and Suppression System (WPDSS) is a novel, fully automated, end to end, AI based solution to effectively predict hotspots and detect wildfires, deploy drones to spray fire retardant, preventing and suppressing wildfires. WPDSS consists of four steps. 1. Preprocessing: WPDSS loads real time satellite data from NASA and meteorological data from NOAA of vegetation, temperature, precipitation, wind, soil moisture, and land cover for prevention. For detection, it loads the real time data of Land Cover, Humidity, Temperature, Vegetation, Burned Area Index, Ozone, and CO2. It uses the process of masking to eliminate not hotspots and not wildfires such as water bodies, and rainfall. 2. Learning: The AI model consists of a random forest class

This paper introduces a paradigm shift in the way privacy is defined, driven by a novel interpretation of the fundamental result of Dwork and Naor about the impossibility of absolute disclosure prevention. We propose a general model of utility and privacy in which utility is achieved by disclosing the value of low-entropy features of a secret $X$, while privacy is maintained by hiding the value of high-entropy features of $X$. Adopting this model, we prove that, contrary to popular opinion, it is possible to provide meaningful inferential privacy guarantees. These guarantees are given in terms of an operationally-meaningful information measure called pointwise maximal leakage (PML) and prevent privacy breaches against a large class of adversaries regardless of their prior beliefs about $X$. We show that PML-based privacy is compatible with and provides insights into existing notions such as differential privacy. We also argue that our new framework enables highly flexible mechanism designs, where the randomness of a mechanism can be adjusted to the entropy of the data, ultimately, leading to higher utility.

We analyze the optimal control of disease prevention and treatment in a basic SIS model. We develop a simple macroeconomic setup in which the social planner determines how to optimally intervene, through income taxation, in order to minimize the social cost, inclusive of infection and economic costs, of the spread of an epidemic disease. The disease lowers economic production and thus income by reducing the size of the labor force employed in productive activities, tightening thus the economy's overall resources constraint. We consider a framework in which the planner uses the collected tax revenue to intervene in either prevention (aimed at reducing the rate of infection) or treatment (aimed at increasing the speed of recovery). Both optimal prevention and treatment policies allow the economy to achieve a disease-free equilibrium in the long run but their associated costs are substantially different along the transitional dynamic path. By quantifying the social costs associated with prevention and treatment we determine which policy is most cost-effective under different circumstances, showing that prevention (treatment) is desirable whenever the infectivity rate is low (high).

Distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily in distributed deep learning applications when multiple collectives circularly wait for each other. GPU collective deadlocks pose a significant challenge to the correct functioning and efficiency of distributed deep learning, and no general effective solutions are currently available. Only in specific scenarios, ad-hoc methods, making an application invoke collectives in a consistent order across GPUs, can be used to prevent circular collective dependency and deadlocks. This paper presents DFCCL, a novel GPU collective communication library that provides a comprehensive approach for GPU collective deadlock prevention while maintaining high performance. DFCCL achieves preemption for GPU collectives at the bottom library level, effectively preventing deadlocks even if applications cause circular collective dependency. DFCCL ensures high performance with its execution and scheduling methods for collectives. Experiments show that DFCCL effectively prevents GPU collective deadlocks in various situations. Moreo

The increasing use of online channels for service delivery raises new challenges in service failure prevention. This work-in-progress paper reports on the first phase of an action-design research project to develop a service failure prevention methodology. In this paper we review the literature on online services, failure prevention and failure recovery and develop a theoretical framework for online service failure prevention. This provides the theoretical grounding for the artefact (the methodology) to be developed. We use this framework to develop an initial draft of our methodology. We then outline the remaining phases of the research, and offer some initial conclusions gained from the project to date.

Untrusted deserialization exploits, where a serialised object graph is used to achieve denial-of-service or arbitrary code execution, have become so prominent that they were introduced in the 2017 OWASP Top 10. In this paper, we present a novel and lightweight approach for runtime prevention of deserialization attacks using Markov chains. The intuition behind our work is that the features and ordering of classes in malicious object graphs make them distinguishable from benign ones. Preliminary results indeed show that our approach achieves an F1-score of 0.94 on a dataset of 264 serialised payloads, collected from an industrial Java EE application server and a repository of deserialization exploits.

Police officer presence at an intersection discourages a potential traffic violator from violating the law. It also alerts the motorists' consciousness to take precaution and follow the rules. However, due to the abundant intersections and shortage of human resources, it is not possible to assign a police officer to every intersection. In this paper, we propose an intelligent and optimal policing strategy for traffic violation prevention. Our model consists of a specific number of targeted intersections and two police officers with no prior knowledge on the number of the traffic violations in the designated intersections. At each time interval, the proposed strategy, assigns the two police officers to different intersections such that at the end of the time horizon, maximum traffic violation prevention is achieved. Our proposed methodology adapts the PROLA (Play and Random Observe Learning Algorithm) algorithm [1] to achieve an optimal traffic violation prevention strategy. Finally, we conduct a case study to evaluate and demonstrate the performance of the proposed method.

Pressure Ulcer is one of the most problems in patients with bed rest. Reposition and skin care are deterrent against the incidence of pressure ulcer. Objective: This study aimed to analyze the effectiveness of sesame oil for the prevention of pressure ulcer in patients with bed rest undergoing hospitalization. Method: This study used a randomized controlled trial design. Forty samples were divided groups: control and intervention groups. This study was analysed using Chi Square. Results: The results showed that there was a significant difference between two group (p=0,04). Conclusions: Skin care with sesame oil can prevention of pressure ulcers. These results recommended that sesame oil can be used for nursing intervention for the prevention of pressure ulcers.