This presentatiom provides a method of mathematical representation of the traffic flow of network states. Anomalous behavior in this model is represented as a point, not grouped in clusters allocated by the "alpha-stream" process
Novel Methodology of Data Management in Ad Hoc Network Formulated using Nanos...Drjabez
In Ad hoc Network of Nanosensors for Wastage detection, clustering assist in nodal communication and in organization of the data fetched by the nanosensors in the network. The attempt of traditional cluster formation techniques degraded the formation of cluster in a precise manner. The data from the nanosensors which act as the nodes of the network have to be distinctively added into the clusters. The dynamic path selection cluster would achieve this distinct addition by dynamically creating a path to the data as an initial process and then redirecting the data to their appropriate cluster based to the readied scheme.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
I take our currently implemented real-time analytics platform which makes decisions and takes autonomous action within our environment and repurpose it for a hypothetical solution to a phishing problem at a hypothetical startup.
Novel Methodology of Data Management in Ad Hoc Network Formulated using Nanos...Drjabez
In Ad hoc Network of Nanosensors for Wastage detection, clustering assist in nodal communication and in organization of the data fetched by the nanosensors in the network. The attempt of traditional cluster formation techniques degraded the formation of cluster in a precise manner. The data from the nanosensors which act as the nodes of the network have to be distinctively added into the clusters. The dynamic path selection cluster would achieve this distinct addition by dynamically creating a path to the data as an initial process and then redirecting the data to their appropriate cluster based to the readied scheme.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
I take our currently implemented real-time analytics platform which makes decisions and takes autonomous action within our environment and repurpose it for a hypothetical solution to a phishing problem at a hypothetical startup.
The definition of normal - An introduction and guide to anomaly detection. Alois Reitbauer
What is normal behaviour?
How are expectations about future behaviour derived from data?
How do anomaly detection algorithms work including trending and seasonality?
How do these algorithms know whether something is an anomaly?
Which algorithms can be used for which type of data?
Five Things I Learned While Building Anomaly Detection Tools - Toufic Boubez ...tboubez
This is my presentation from LISA 2014 in Seattle on November 14, 2014.
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
How Netflix developed anomaly detection algorithm which has been applied in multiple contexts
Robust to prior anomalies
Handle high cardinality dimensions
Handles seasonality
Handle data which is not always normally distributed
Challenge - more anomalies than we can handle from a human perspective
Evaluating Real-Time Anomaly Detection: The Numenta Anomaly BenchmarkNumenta
Subutai Ahmad, VP Research presenting NAB and discussing the need for evaluating real-time anomaly detection algorithms. This presentation was delivered at MLConf (Machine Learning Conference) in San Francisco 2015.
Operational Insight: Concepts and Examples (w/o Presenter Notes)royrapoport
The 2015-06-15 Operational Insight presentation, without presenter notes (because the way Keynote handles presenter notes makes them dominate the presentation)
A brief overview of Real-Time Analytics at Netflix and the challenges we've faced in designing and deploying production ready products based on real-time data.
Anomaly detection in plain static graphsdash-javad
Detecting anomalies in data is a vital task and , with numerous high-impact applications in areas such as security, finance, health care, and law enforcement and many others.With graph data becoming ubiquitous, techniques for structured graph data have been of focus recently.In this presentation , we're going to review the techniques for anomaly detection in plain static graphs.
The definition of normal - An introduction and guide to anomaly detection. Alois Reitbauer
What is normal behaviour?
How are expectations about future behaviour derived from data?
How do anomaly detection algorithms work including trending and seasonality?
How do these algorithms know whether something is an anomaly?
Which algorithms can be used for which type of data?
Five Things I Learned While Building Anomaly Detection Tools - Toufic Boubez ...tboubez
This is my presentation from LISA 2014 in Seattle on November 14, 2014.
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
How Netflix developed anomaly detection algorithm which has been applied in multiple contexts
Robust to prior anomalies
Handle high cardinality dimensions
Handles seasonality
Handle data which is not always normally distributed
Challenge - more anomalies than we can handle from a human perspective
Evaluating Real-Time Anomaly Detection: The Numenta Anomaly BenchmarkNumenta
Subutai Ahmad, VP Research presenting NAB and discussing the need for evaluating real-time anomaly detection algorithms. This presentation was delivered at MLConf (Machine Learning Conference) in San Francisco 2015.
Operational Insight: Concepts and Examples (w/o Presenter Notes)royrapoport
The 2015-06-15 Operational Insight presentation, without presenter notes (because the way Keynote handles presenter notes makes them dominate the presentation)
A brief overview of Real-Time Analytics at Netflix and the challenges we've faced in designing and deploying production ready products based on real-time data.
Anomaly detection in plain static graphsdash-javad
Detecting anomalies in data is a vital task and , with numerous high-impact applications in areas such as security, finance, health care, and law enforcement and many others.With graph data becoming ubiquitous, techniques for structured graph data have been of focus recently.In this presentation , we're going to review the techniques for anomaly detection in plain static graphs.
JPD1424 A System for Denial-of-Service Attack Detection Based on Multivariat...chennaijp
We have best 2014 free dot not projects topics are available along with all document, you can easy to find out number of documents for various projects titles.
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IEEE 2014 DOTNET PARALLEL DISTRIBUTED PROJECTS A system-for-denial-of-service...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Android malware detection through online learningIJARIIT
Android malware constantly evolves so as to evade detection. The entire malware population to be nonstationary.
Contrary to this fact, most of the prior works on machine learning based android malware detection have
assumed that the distribution of the observed malware characteristics (i.e., features) does not change over time. The
problem of malware population drift and propose a novel online learning based framework to detect malware, named
CASANDRA (Context-aware, Adaptive and Scalable Android malware detector). In order to perform accurate
detection, a novel graph kernel that facilitates capturing apps security-sensitive behaviours along with their context
information from dependence graphs is proposed. Besides being accurate and scalable, CASANDRA has specific
advantages: first, being adaptive to the evolution in malware features over time; second, explaining the significant
features that led to an apps classification as being malicious or benign.
Intrusion Detection System Using Self Organizing Map AlgorithmsEditor IJCATR
With the rapid expansion of computer usage and computer network the security of the computer system has became very
im
portant. Every day new kind of attacks are being faced by industries. Many methods have been proposed for the development of
intrusion detection system using artificial intelligence technique. In this paper we will have a look at an algorithm based o
n neur
al
networks that are suitable for Intrusion Detection Systems (IDS)
.
The name of this
algorithm is "Self Organizing Maps" (SOM).
So
far, many different methods have been used to build a detector that Wide variety of different ways in the covers. Among the
methods
used to detect attacks in intrusion detection is done, In this paper we investigate the
Self
-
Organizing
Map
method.
Image morphing has been the subject of much attention in recent years. It has proven to be a powerful visual effects tool
in film and television, depicting the fluid transformation of one digital image into another. This paper reviews the growth of this field
and describes recent advances in image morphing in terms of three areas: feature specification, warp generation methods, and
transition control. These areas relate to the ease of use and quality of results. We will describe the role of radial basis functions, thin
plate splines, energy minimization, and multilevel free-form deformations in advancing the state-of-the-art in image morphing. A
comparison of various techniques for morphing one digital image in to another is made. We will compare various morphing techniques
such as Feature based image morphing, Mesh and Thin Plate Splines based image morphing based on different attributes such as
Computational Time, Visual Quality of Morphs obtained and Complexity involved in Selection of features. We will demonstrate the
pros and cons of various techniques so as to allow the user to make an informed decision to suit his particular needs. Recent work on a
generalized framework for morphing among multiple images will be described.
Intrusion Detection System Using Self Organizing Map AlgorithmsEditor IJCATR
With the rapid expansion of computer usage and computer network the security of the computer system has became very
important. Every day new kind of attacks are being faced by industries. Many methods have been proposed for the development of
intrusion detection system using artificial intelligence technique. In this paper we will have a look at an algorithm based on neural
networks that are suitable for Intrusion Detection Systems (IDS). The name of this algorithm is "Self Organizing Maps" (SOM). So
far, many different methods have been used to build a detector that Wide variety of different ways in the covers. Among the methods
used to detect attacks in intrusion detection is done, In this paper we investigate the Self-Organizing Map method.
New kind of intrusions causes deviation in the normal behaviour of traffic flow in
computer networks every day. This study focused on enhancing the learning capabilities of IDS
to detect the anomalies present in a network traffic flow by comparing the k-means approach of
data mining for intrusion detection and the outlier detection approach. The k-means approach
uses clustering mechanisms to group the traffic flow data into normal and abnormal clusters.
Outlier detection calculates an outlier score (neighbourhood outlier factor (NOF)) for each flow
record, whose value decides whether a traffic flow is normal or abnormal. These two methods
were then compared in terms of various performance metrics and the amount of computer
resources consumed by them. Overall, k-means was more accurate and precise and has better
classification rate than outlier detection in intrusion detection using traffic flows. This will help
systems administrators in their choice of IDS.
Anomaly Detection using multidimensional reduction Principal Component AnalysisIOSR Journals
Anomaly detection has been an important research topic in data mining and machine learning. Many
real-world applications such as intrusion or credit card fraud detection require an effective and efficient
framework to identify deviated data instances. However, most anomaly detection methods are typically
implemented in batch mode, and thus cannot be easily extended to large-scale problems without sacrificing
computation and memory requirements. In this paper, we propose multidimensional reduction principal
component analysis (MdrPCA) algorithm to address this problem, and we aim at detecting the presence of
outliers from a large amount of data via an online updating technique. Unlike prior principal component
analysis (PCA)-based approaches, we do not store the entire data matrix or covariance matrix, and thus our
approach is especially of interest in online or large-scale problems. By using multidimensional reduction PCA
the target instance and extracting the principal direction of the data, the proposed MdrPCA allows us to
determine the anomaly of the target instance according to the variation of the resulting dominant eigenvector.
Since our MdrPCA need not perform eigen analysis explicitly, the proposed framework is favored for online
applications which have computation or memory limitations. Compared with the well-known power method for
PCA and other popular anomaly detection algorithms
Годовой отчет Qrator Labs об угрозах интернета 2017Qrator Labs
Узнаваемость проблематики DDoS растет одновременно с увеличением агрессии интернета и изменения его здорового состояния. DDoS-атаки похожи на акул в океане — вы знаете, что они есть, даже не видя плавников над водой. Эта картина в полной мере описывает происходящее в современном интернете, где атаки происходят каждую минуту, становясь новой нормальностью. Те, кто продает защиту и доступность, адаптируются соответствующим образом. В 2017 году интернет-бизнес без защиты от DDoS и без WAF прекратил свое существование.
DDoS awareness grows with the attack state shifting towards the healthy state of the Internet. DDoS attacks are like sharks in the ocean—you know they are there, even if you do not see any shark fins above the water. This picture describes what’s happening in the modern internet, where DDoS attacks occur every minute—they become the new normal, and those serving accessibility are adapting by including such services in their bundles. In 2017 an internet business without DDoS mitigation and WAF is ceased to exist.
Memcached amplification DDoS: a 2018 threat. Qrator Labs
In November 2017, researchers have found a new class of amplification DDoS attacks: memcached amplification. At the end of February 2018 those attacks are in the wild, with a bandwidth already close to 0,5 Gbps. This lightning talk is a short analysis of the threat structure, consequences and possible ways to mitigate the threat.
A contemporary network service heavily depends on domain name system operating normally. Yet, often issues and caveats of typical DNS setup are being overlooked. DNS (like BGP before) is expected to "just work" everywhere, however, just as BGP, this is a complex protocol and a complex solution where a lot of things could go wrong in multiple ways under different circumstances. This talk is supposed to provide some assistance both in maintaining your own DNS infrastructure and in relying on service providers doing this.
The global routing incidents have already become regular. Its source is engineers mistakes, but the tolerance to these anomalies at the level of IP-transit allows these incidents to have global consequences. In this report, I will make a review of different methods of ingress route filtering and discuss possible future solutions.
At the Ripe74 routing working group, Qrator Labs leading engineer Alexander Azimov gave a status update on the BGP route leaks issue. These are the slides to the video: https://youtu.be/4NAlJzVRwM0
Презентация Артема Гавриченкова, технического директора Qrator Labs, на конференции "Хакер, вендор, клиент: безопасность без купюр" (https://vulners.com/conference).
Qrator and Wallarm 2016 State of Network Security report is dedicated to the main events and strong trends in the network security industry. Particular attention is payed to the DDoS, Internet infrastructure, hacks and vulnerabilities in software and hardware, like connected devices.
Состояние сетевой безопасности в 2016 году Qrator Labs
Отчёт компаний Qrator и Wallarm, представленный вашему вниманию, посвящён главным событиям и основным тенденциям в области сетевой безопасности.
Отдельное внимание в отчёте уделяется проблематике DDoS, инфраструктуры Интернета и уязвимостям, а также взломам широко используемого ПО и других продуктов с электронной составляющей — устройств, подключённых к Сети.
Сколько стоит доступ в память, и что с этим делатьQrator Labs
Конференция Highload++ / 7 ноября 2016 / Спикер - Антон Орлов, занимается исследованием аппаратных компонентов, пригодных к использованию в платформе фильтрации трафика Qrator Labs.
В пересчёте на количество транзисторов оперативная память занимает в современном сервере не менее 85% (если добавить сюда внутрипроцессорные кэши, то и сильно за 90%). Все эти транзисторы оплачены, они греются. Хотелось бы использовать их по максимуму. При этом уже с середины 90-х годов именно скорость доступа к данным ограничивает производительность большинства вычислений (фоннеймановское узкое горло, стена памяти).
Мы так привыкли к слову RAM, что порой принимаем название random access за чистую монету. Однако во что на самом деле обходится доступ в память? И как это узнать? И что потом с этим делать?
Анализ количества посетителей на сайте [Считаем уникальные элементы]Qrator Labs
Конференция Highload++ / 7 ноября 2016 / Спикер - Константин Игнатов, инженер-разработчик в отделе исследований Qrator Labs.
Для точного ответа на вопрос, сколько уникальных посетителей было на моём сайте за произвольный интервал времени в прошлом, нужно через равные интервалы времени сохранять множество посетителей сайта (пусть это для простоты будут IP-адреса), которых мы за прошедший интервал увидели. Понятное дело, что такой объём информации хранить нереально, а даже, если получится, придётся объединять большое количество множеств и считать элементы в том множестве, которое получилось в итоге. Это очень долго. Не спасает ситуацию даже переход от точных алгоритмов к приблизительным: гарантировать точность либо не получится, либо придётся использовать объём памяти и вычислительные ресурсы, сопоставимые с точным алгоритмом.
Конференция Highload++ / 7 ноября 2016 / Спикер - Александр Азимов, network Architect at Qrator Labs, руководитель проекта "Radar by Qrator"
Многим известна проблема исчерпания адресного пространства IPv4, из года в год делаются доклады о том, что адреса кончаются, кончаются, да никак не кончатся. На этом фоне польза от внедрения IPv6 кажется абсолютно неочевидной.
В докладе пойдет речь о причинах неизбежности прихода и массового внедрения IPv6 вне зависимости от судьбы адресного пространства IPv4, с описанием как пользы от использования Dual Stack, так и возникающих рисков.
During last years much attention was paid for process of deploying IPv6 in different regions. And with growing IPv6 adoption the requirements also grew up. Today we require not just working IPv6 but reliable IPv6. Of course this aim brings to our attention IPv6 network latency which we want to be comparable to IPv4 latency. First measurements gave community very interesting and promising results – for some networks IPv6 was faster than IPv4. But why IPv6 had less latency – there was no clear evidence. In my report I’m going to discuss reasons why in some cases IPv6 is faster than IPv4 and why it is slower in other ones.
Особенности использования машинного обучения при защите от DDoS-атакQrator Labs
В докладе мы взглянем на проблему DDOS, с одной стороны, более широко — как на проблему обеспечения доступности ресурса, с другой стороны более конкретно — как на проблему информационной безопасности.
Поговорим о том, как автоматизировать борьбу с DDOS-атаками при помощи машинного обучения, и чем такая автоматизация может быть опасна.
Наконец, рассмотрим пару примеров и обсудим, с чего начинать строить систему защиты от DDOS.
Финансовый сектор. Аспекты информационной безопасности 2016Qrator Labs
Целью настоящего исследования было изучить актуальность проблематики и масштаб угрозы DDoS-атак и атак на уязвимости приложений в российском финансовом секторе (банки и платежные системы), а также оценить уровень защищенности внешнего сетевого периметра организаций.
White Paper. Эволюция DDoS-атак и средств противодействия данной угрозеQrator Labs
В области DDoS-атак, как и во всех других сферах кибербезопасности, не прекращается борьба щита и меча. Злоумышленники используют всё более изощрённые методы. Поставщики решений следуют за ними, выпуская всё новые продукты для того, чтобы помешать злому умыслу. Старые средства перестают работать, требуются новые подходы и инструменты для того, чтобы не стать жертвой киберпреступников. В данном документе рассматривается путь развития, который проходят инструменты противодействия DDoS-атакам, под влиянием меняющихся подходов киберпреступников.
Информация в данном документе будет полезной компаниям, которые хотят быть уверены, что их интернет-ресурсы защищены современными средствами противодействия, а не решениями, основанными на устаревших неэффективных более технологиях, которые всё ещё предлагаются на рынке. Также, документ предназначен для специалистов в области информационной безопасности и широкого круга людей, интересующихся данной темой.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
ER(Entity Relationship) Diagram for online shopping - TAEHimani415946
https://bit.ly/3KACoyV
The ER diagram for the project is the foundation for the building of the database of the project. The properties, datatypes, and attributes are defined by the ER diagram.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
2. Anomaly Recognition Qrator Labs2
The threat
Network attack is becoming a major threat on nations,
governmental institutions, critical infrastructures and business
organizations. Some attacks are focused on exploiting software
vulnerabilities to implement denial of service attacks, damage or
steal important data. Other use a large number of infected
machines to implement denial-of-service attacks. In this
presentation we are focusing on detecting network attacks by
detecting the anomalies in network traffic flow data and
anomalous behavior of the network applications. The goal is to
detect the beginning of the attack in a real-time and to detect
when the system is returned back to the normal state.
3. Anomaly Recognition Qrator Labs3
The threat
The network traffic flow data can be represented by a set of network-level
metrics (amount of packets for different protocols, inbound and outbound
traffic, etc.) and application-level metrics (like the response duration
histogram for web server). These metrics are collected by the traffic analyser
at fixed rate. The goal for the state analyzer is to detect anomalous network
and application behavior basing on these metrics.
The input data for the analyzer is statistics matrix that contains a single row for
every traffic time slice. Each row contains the network-level and application-
level features that come from different scales. This matrix is the input for the
intrusion detection processes (both training and detection steps).
4. Anomaly Recognition Qrator Labs4
DARPA: simulated attacks on air base[1]
The example of IP-domen traffic’s features due one day and its relations (features)
The stochastic process X={x1,…xn} where x_i- all features at the moment of the time
5. Anomaly Recognition Qrator Labs5
The threat
Challenge: How to process an “ocean” of data in order to find abnormal
patterns in the data? How to fuse data from different sources (sensors) to find
correlations and anomalies? How to find distances in high-dimensional data?
How can we determine whether a point belongs to a cluster/segment or not?
The goal is to identify points that deviate from normal behaviour which reside
in the cluster. How we treat huge high dimensional data that is dynamically
and constantly changes? How can we model the high dimensional data to find
deviations from normal behavior?
7. Anomaly Recognition Qrator Labs7
Electronic intelligence and Cyber threat management:
Generic approach
Theory, efficient algorithms, software and prototypes (integrated system) which
process data in real time to detect anomalies that deviate from normal behavior
12. Anomaly Recognition Qrator Labs12
Standard approach: Diffusion Maps (DM)
[2] R.R. Coifman, S. Lafon, Diffusion maps, Applied and Computational
Harmonic Analysis, 21, 5-30, 2006.
13. Anomaly Recognition Qrator Labs13
Standard approach: Diffusion Maps (DM)
It is easy to see that the map has the following properties:
• The map represents the data in a space of dimension m.
• The map is not linear.
• The distance between the images of points is equal to the diffuse
distance, that is, the probability to get from point x to point y via
random walk on the graph for the time t.
14. Anomaly Recognition Qrator Labs14
Standard approach: Diffusion Maps (DM)
The figure illustrates the effectiveness of the separation of mixed known
clusters via “diffusion maps”. If the generated data is represented as two
interlocking rings (marked different shades of blue), no any linear methods is
able to divide it. Nevertheless, a random walk on the graph represented by
these rings, have ability to divide the classes. The probability remain inside
the same ring by random walk is greater than the probability of jumping
from one ring to another.
15. Anomaly Recognition Qrator Labs15
Diffusion Maps (DM): The problem
Classification background and anomaly?
17. Anomaly Recognition Qrator Labs17
Diffusion Maps (DM): The problem
Anomalies are not grouped in clusters
18. Anomaly Recognition Qrator Labs18
Advanced approach: Homotopy in Temporal Diffusion Maps (DM)
2
2
2
1
2
2
mod)(
ji xxDji
ij eeG
Diffusion operator
The diffusion geometry is oriented around a smooth parametric curve. The
curve represents the day and night
19. Anomaly Recognition Qrator Labs19
Advanced approach: Homotopy in Temporal Diffusion Maps (DM)
Once X is mapped - extension of to , using
representatives from X (sampling)
f Xx
Xx
20. Anomaly Recognition Qrator Labs20
Advanced approach: Homotopy in Temporal Diffusion Maps (DM)
iELet
be approximating
curve and Xx
iE
Define homotopy G(x)
i
i
iEx
iExiE
xxG
))(,(
))(,()(
)(
)( xG
21. Anomaly Recognition Qrator Labs21
Advanced approach: Homotopy in Temporal Diffusion Maps (DM)
iELet
be approximating
curve and Xx
iE
Define homotopy G(x)
i
i
iEx
iExiE
xxG
))(,(
))(,()(
)(
)( xG
30. Anomaly Recognition Qrator Labs30
anomalies background
anomalies 0,95 0,05
background 0,03 0,97
Table 1: distribution of the “false-
positive” and “true-negative” for the
result of presented algorithm.
anomalies background
anomalies 0,63 0,37
background 0,29 0,71
Table 2: distribution of the “false-
positive” and “true-negative” for the
result of projection on PCA.