The detection of Denial of Service (DoS) attacks remains a challenge for the cloud environment, affecting a massive number of services and applications hosted by such virtualized infrastructures. Typically, in the literature, the detection of DoS attacks is performed solely by analyzing the traffic of packets in the ne…
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48 results for “denial of service”
Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI d…
Real-time anomaly detection for edge streams using MIDAS and MIDAS-F.
problem Detecting unusual behavior in dynamic graph edge streams.
method MIDAS-F focuses on microcluster anomalies, incorporating anomalies into internal states to reduce poisoning effect.
result MIDAS-F outperforms MIDAS in accuracy, with up to 62% higher ROC-AUC.
Low-rate application layer distributed denial of service (LDDoS) attacks are both powerful and stealthy. They force vulnerable webservers to open all available connections to the adversary, denying resources to real users. Mitigation advice focuses on solutions that potentially degrade quality of service for legitimate…
Study detects and mitigates stealthy DDoS attacks in IoT networks.
problem Stealthy DDoS attacks in IoT networks.
method Anomaly-based IDS for timely detection and mitigation.
result Demonstrated capability of detecting and mitigating small attack size per source.
The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to diffe…
The rapid digital transformation without security considerations has resulted in the rise of global-scale cyberattacks. The first line of defense against these attacks are Network Intrusion Detection Systems (NIDS). Once deployed, however, these systems work as blackboxes with a high rate of false positives with no mea…
Deep Claim predicts payer responses from claims data using deep learning.
problem Predicting payer responses from claims data to improve healthcare performance.
method Learning complex dependencies in claim inputs to create a compact representation, then using deep learning to predict responses.
result Deep Claim improves claim denial prediction by 22.21%.
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ …
Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administ…
Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and a…
Comparing Alternatives to Measure the Impact of DDoS Attack Announcements on Target Stock Pricesq-fin.ST
The attack intensity of distributed denial of service (DDoS) attacks is increasing every year. Botnets based on internet of things (IOT) devices are now being used to conduct DDoS attacks. The estimation of direct and indirect economic damages caused by these attacks is a complex problem. One of the indirect damage of …
Applications of safety, security, and rescue in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors may fail…
Deep learning detects cyber-attacks in smart grid systems.
problem Cyber-attacks on smart grid systems.
method Deep learning-based intrusion detection system trained on industrial dataset.
result Proposed system outperforms Naive Bayes, SVM, and Random Forest.
How can we find patterns and anomalies in a tensor, or multi-dimensional array, in an efficient and directly interpretable way? How can we do this in an online environment, where a new tensor arrives each time step? Finding patterns and anomalies in a tensor is a crucial problem with many applications, including buildi…
The control and sensing of large-scale systems results in combinatorial problems not only for sensor and actuator placement but also for scheduling or observability/controllability. Such combinatorial constraints in system design and implementation can be captured using a structure known as matroids. In particular, the…
Paper introduces proof-of-learning to verify ML model training.
problem No mechanism to prove ML model training parameters were obtained through optimization.
method Inspired by proof-of-work and verified computations, introduces proof-of-learning mechanism.
result Proves model training parameters were obtained through optimization with minimal adversary work.
Framework insures AI actions with reserve capital, preventing loss.
problem Ensuring safety and accountability for AI actions with varying side effects.
method Developed Actuarial Action Interface (AAI) and Authority Frontier to price and gate AI actions.
result Found common refusal and release patterns across domains, with varying required reserve capital.
Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this…
OTT services are replacing traditional telecom services, affecting revenue streams.
problem OTT services are reducing traditional telecom revenue streams.
method Quantitative analysis of mobile user trends and revenue changes.
result OTT services are becoming more prevalent and impacting telecom revenue.
The Impact of Services on Economic Complexity: Service Sophistication as Route for Economic Growthecon.GN
Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. By combining tools from network science and econometrics, a robust and stable relationship between a country's productive structure and its economic growth has been established. Here we report that not only …
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into r…
Network Based Pricing for 3D Printing Services in Two-Sided Manufacturing-as-a-Service Marketplacecs.LG
This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A…
This paper tackles fair same-day delivery service by optimizing regional service rates.
problem Fairness in same-day delivery service for different neighborhoods.
method Partition service area into regions, use multi-objective Markov decision process and deep Q-learning.
result Our approach maximizes fairness across all regions while maintaining overall service rate.
DeepSIP predicts network failures' impact using CNN from syslog and traffic data.
problem Predicting service impact from network failures.
method Temporal multimodal CNN for predicting time to recovery and traffic loss.
result DeepSIP reduced prediction error by approximately 50%.
Service system dynamics occur at the interplay between customer behaviour and a service provider's response. This kind of dynamics can effectively be modeled within the framework of queuing theory where customers' arrivals are described by point process models. However, these approaches are limited by parametric assump…
An online learning framework optimizes pricing and capacity in service systems.
problem Optimizing pricing and capacity in dynamic service systems.
method Gradient-based Online Learning in Queue (GOLiQ) framework.
result GOLiQ achieves logarithmic regret bound and improves service provider's performance.
Optimizes resource allocation for virtualized network functions based on performance profiles.
problem Mapping SLA performance requirements to dynamic virtualized infrastructure resources.
method Profile-based resource allocation using VNF performance datasets and machine learning models.
result A method to predict and recommend optimal resource allocation for network services.
This work analyzes how users and services adapt to reduce risk, leading to specialization.
problem Adaptation of users and services to reduce risk affects learning and performance.
method Analyzed a class of dynamics where users allocate participation and services update parameters.
result Repeated myopic updates with multiple learners lead to better outcomes than repeated risk minimization.
The Social Internet of Things (SIoT), integration of the Internet of Things and Social Networks paradigms, has been introduced to build a network of smart nodes that are capable of establishing social links. In order to deal with misbehaving service provider nodes, service requestor nodes must evaluate their trustworth…
AI agent predicts industry and product/service codes for companies.
problem Manual curation of company data is expensive and prone to errors.
method Hierarchical multi-class industry code classifier with multi-label product/service code classifier.
result High accuracy (92-96%) achieved with limited labeled data.
I present a web service for querying an embedding of entities in the Wikidata knowledge graph. The embedding is trained on the Wikidata dump using Gensim's Word2Vec implementation and a simple graph walk. A REST API is implemented. Together with the Wikidata API the web service exposes a multilingual resource for over …
Policy certifies inventory levels meeting service requirements.
problem Maintaining stock levels meeting service requirements despite unknown demand.
method Data-driven order policy using online learning and integral action.
result Valid inference method for finite samples.
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers. We analyze a dataset of 500,000 services using (1) supervised classification with Random Forests, and (2) Association Rules. Our classifier reaches an average sensitivity of…
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
Computable contracts simplify financial transactions and reduce legal costs.
problem Difficulty in querying, executing, and analyzing text-based financial contracts.
method Develop a Contract Definition Language and illustrate use cases.
result Substantial improvements in customer experience and cost reduction.
Paper introduces privacy-preserving deep-learning service.
problem Privacy loss in deep-learning-as-a-service.
method Homomorphic Encryption tailored for CNNs.
result Effectiveness of proposed privacy-preserving architecture.
Machine learning methods are widely used for a variety of prediction problems. \emph{Prediction as a service} is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services…
Game theory helps analyze ESOs/EBIs in production and service sectors.
problem Economic incentives affect traditional production/service functions and create intangible capital.
method Uses game theory to analyze interactions in ESO/EBI transactions.
result No perfect Nash Equilibria for two-stage games involving many participants.
Digital Financial Services continue to expand and replace the delivery of traditional banking services to the customers through innovative technologies to meet the growing complex needs and globalization challenges. These diversified digital products help the organizations (service providers) to improve their firm perf…
The writers propose a mathematical Method for deriving risk weights which describe how a borrower's income, relative to their debt service obligations (serviceability) affects the probability of default of the loan. The Method considers the borrower's income not simply as a known quantity at the time the loan is made, …
LightAutoML automates ML for a large financial services company.
problem Building high-quality ML models for a complex financial ecosystem.
method Developed an AutoML system tailored to a large European financial services company's unique requirements.
result LightAutoML outperformed experienced data scientists and other open-source solutions.
Graph learning categorizes DeFi services into similar functionalities.
problem Identifying similar financial services in decentralized finance protocols.
method Graph representation learning (GRL) to categorize smart contract blocks into clusters.
result Purity of clustering reaches .888 in the best-case scenario.
AI enhances financial services but humans are irreplaceable for empathy, presence, and ethics.
problem AI's limitations in financial services, especially with small datasets and human judgment.
method EPOCH framework highlighting five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope.
result Humans are essential for trust, innovation, and consumer experience in financial services.
GOLOMB improves dialogue state tracking for unseen services.
problem Improving dialogue state tracking for multiple services and APIs.
method GOLOMB uses a BERT-based model that queries dialogue history with slot descriptions and values.
result GOLOMB achieves a joint goal accuracy of 53.97% on the SGD dataset.
This study compares community detection algorithms for delineating health service areas.
problem Delineating health service areas to improve health care services.
method Comparative analysis of community detection algorithms on hospital-patient discharge networks.
result Infomap algorithm produced the best delineation of health service areas.
The professional services sector is at a turning point, with some industries showing growth opportunities.
problem Identifying growth opportunities in the professional services sector after decades of growth.
method A simple framework applied to the US economic context to diagnose growth opportunities.
result The professional services sector is expected to stall at a national level, but some industries still offer growth opportunities.