A Study and Comparative analysis of Conditional Random Fields for Intrusion d...IJORCS
Intrusion detection systems are an important component of defensive measures protecting computer systems and networks from abuse. Intrusion detection plays one of the key roles in computer security techniques and is one of the prime areas of research. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. An intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper we study the Machine Learning and data mining techniques to solve Intrusion Detection problems within computer networks and compare the various approaches with conditional random fields and address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach.
user centric machine learning framework for cyber security operations centerVenkat Projects
In order to ensure a company's Internet security, SIEM (Security Information and Event Management) system is in place to simplify the various preventive technologies and flag alerts for security events. Inspectors (SOC) investigate warnings to determine if this is true or not. However, the number of warnings in general is wrong with the majority and is more than the ability of SCO to handle all awareness. Because of this, malicious possibility. Attacks and compromised hosts may be wrong. Machine learning is a possible approach to improving the wrong positive rate and improving the productivity of SOC analysts. In this article, we create a user-centric engineer learning framework for the Internet Safety Functional Center in the real organizational context. We discuss regular data sources in SOC, their work flow, and how to process this data and create an effective machine learning system. This article is aimed at two groups of readers. The first group is intelligent researchers who have no knowledge of data scientists or computer safety fields but who engineer should develop machine learning systems for machine safety. The second groups of visitors are Internet security practitioners that have deep knowledge and expertise in Cyber Security, but do Machine learning experiences do not exist and I'd like to create one by themselves. At the end of the paper, we use the account as an example to demonstrate full steps from data collection, label creation, feature engineering, machine learning algorithm and sample performance evaluations using the computer built in the SOC production of Seyondike.
A Study and Comparative analysis of Conditional Random Fields for Intrusion d...IJORCS
Intrusion detection systems are an important component of defensive measures protecting computer systems and networks from abuse. Intrusion detection plays one of the key roles in computer security techniques and is one of the prime areas of research. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. An intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper we study the Machine Learning and data mining techniques to solve Intrusion Detection problems within computer networks and compare the various approaches with conditional random fields and address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach.
user centric machine learning framework for cyber security operations centerVenkat Projects
In order to ensure a company's Internet security, SIEM (Security Information and Event Management) system is in place to simplify the various preventive technologies and flag alerts for security events. Inspectors (SOC) investigate warnings to determine if this is true or not. However, the number of warnings in general is wrong with the majority and is more than the ability of SCO to handle all awareness. Because of this, malicious possibility. Attacks and compromised hosts may be wrong. Machine learning is a possible approach to improving the wrong positive rate and improving the productivity of SOC analysts. In this article, we create a user-centric engineer learning framework for the Internet Safety Functional Center in the real organizational context. We discuss regular data sources in SOC, their work flow, and how to process this data and create an effective machine learning system. This article is aimed at two groups of readers. The first group is intelligent researchers who have no knowledge of data scientists or computer safety fields but who engineer should develop machine learning systems for machine safety. The second groups of visitors are Internet security practitioners that have deep knowledge and expertise in Cyber Security, but do Machine learning experiences do not exist and I'd like to create one by themselves. At the end of the paper, we use the account as an example to demonstrate full steps from data collection, label creation, feature engineering, machine learning algorithm and sample performance evaluations using the computer built in the SOC production of Seyondike.
A review of machine learning based anomaly detectionMohamed Elfadly
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns are often referred to as anomalies, outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains.
False positive reduction by combining svm and knn algoeSAT Journals
Abstract
With the growth of information technology. There emerges many intrusion detection problem such as cyber security. Intrusion detection system provides basic infrastructure to detect a number of attacks. This research work focuses on intrusion detection problem of network security. The main goal is to detect network behaviour as normal or abnormal. In this research work, two different machine learning algorithm have been combined together to reduce its weakness and takes positive feature of both algorithm. Its experimental results generates better result than other algorithm in terms of performance, accuracy and false positive rate. These combined algorithm has been applied on KDDCUP99 dataset to find better result by improving its performance, accuracy and reducing its false positive rate.
Keywords: Intrusion detection system, KDDCUP99 dataset, False positive rate.
Wmn06MODERNIZED INTRUSION DETECTION USING ENHANCED APRIORI ALGORITHM ijwmn
Communication networks are essential and it will create many crucial issues today. Nowadays, we
consider that the firewalls are the first line of defense but that policies cannot meet the particular
requirements of needed process to achieve security. Most of the research has been done in this area but
we are lagging to achieve security needs. Already many models such as ADAM, DHP, LERAD and
ENTROPHY are proposed to resolve security problems but we need an efficient model to detect new types
of various intrusions within the entire network. In this paper, we proposed to design a modernized
intrusion detection system which consist of two methods such as anomaly and misuse detection. Both are
integrated and also used to detect novel attacks. Our system proposed to discover temporal pattern of
attacker behaviors, which is profiled using an algorithm EAA (Enhanced Apriori Algorithm). This is
experimented with a simple interface to display the behaviors of attacks effectively
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
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.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
A Novel and Advanced Data Mining Model Based Hybrid Intrusion Detection Frame...Radita Apriana
An Intrusion can be defined as any practice or act that attempt to crack the integrity,
confidentiality or availability of a resource. This may contain of a deliberate unauthorized attempt to access
the information, manipulate the data, or make a system unreliable or unusable. With the expansion of
computer networks at an alarming rate during the past decade, security has become one of the serious
issues of computer systems.IDS, is a detection mechanism for detecting the intrusive activities hidden
among the normal activities. The revolutionary establishment of IDS has attracted analysts to work
dedicatedly enabling the system to deal with technological advancements. Hence, in this regard, various
beneficial schemes and models have been proposed in order to achieve enhanced IDS. This paper
proposes a novel hybrid model for intrusion detection. The proposed framework in this paper may be
expected as another step towards advancement of IDS. The framework utilizes the crucial data mining
classification algorithms beneficial for intrusion detection. The Hybrid framework would hence forth, will
lead to effective, adaptive and intelligent intrusion detection.
Machine learning are used for numerous functions like image processing, data mining, prediction analysis, online shopping, cybersecurity, digital forensics, network security etc. the aim of this research work is to explore on the research work that implement security system or provide a framework for system security using machine learning algorithms. Furthermore to explore other fields that applied machine learning algorithms to solve their problems. Stipulate the essential use of the technique, once an algorithm was trained on how to manipulate the provided data, the process of implementation remain automatic.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
In this presentation, Sheba introduces the topic of IoT and the associated trends. Sheba's interest areas lie in applications of IoT in healthcare and more particularly Physiotherapy.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
Energy Management with Disaster Intimation and Control using IoTIJEACS
In the area of digitization and automation, the life of
human being is getting simpler as almost everything is
automated. Nowadays humans have made internet an integral
part of their everyday life without which they are helpless.
Internet of things (IoT) gives a platform which allows different
devices to inter-connect, sense and control the things remotely
across a network infrastructure without any limitation to the
coverage area. In our proposed work, we stress on Wireless-
Home-Automation-System (WHAS) using IoT, it is a system uses
computers or smart phone to control basic home functions and
features automatically through internet from anywhere around
the world, an automated home is sometimes called a smart home.
The proposed system is able to monitor the entire things
connected to the internet and also to be maintaining the status of
individual devices for further action. We have built the home
automation with several devices and sensors, here sensor help to
monitor the device status and intimate the authorized person to
take particular action.
A review of machine learning based anomaly detectionMohamed Elfadly
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns are often referred to as anomalies, outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains.
False positive reduction by combining svm and knn algoeSAT Journals
Abstract
With the growth of information technology. There emerges many intrusion detection problem such as cyber security. Intrusion detection system provides basic infrastructure to detect a number of attacks. This research work focuses on intrusion detection problem of network security. The main goal is to detect network behaviour as normal or abnormal. In this research work, two different machine learning algorithm have been combined together to reduce its weakness and takes positive feature of both algorithm. Its experimental results generates better result than other algorithm in terms of performance, accuracy and false positive rate. These combined algorithm has been applied on KDDCUP99 dataset to find better result by improving its performance, accuracy and reducing its false positive rate.
Keywords: Intrusion detection system, KDDCUP99 dataset, False positive rate.
Wmn06MODERNIZED INTRUSION DETECTION USING ENHANCED APRIORI ALGORITHM ijwmn
Communication networks are essential and it will create many crucial issues today. Nowadays, we
consider that the firewalls are the first line of defense but that policies cannot meet the particular
requirements of needed process to achieve security. Most of the research has been done in this area but
we are lagging to achieve security needs. Already many models such as ADAM, DHP, LERAD and
ENTROPHY are proposed to resolve security problems but we need an efficient model to detect new types
of various intrusions within the entire network. In this paper, we proposed to design a modernized
intrusion detection system which consist of two methods such as anomaly and misuse detection. Both are
integrated and also used to detect novel attacks. Our system proposed to discover temporal pattern of
attacker behaviors, which is profiled using an algorithm EAA (Enhanced Apriori Algorithm). This is
experimented with a simple interface to display the behaviors of attacks effectively
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
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.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
A Novel and Advanced Data Mining Model Based Hybrid Intrusion Detection Frame...Radita Apriana
An Intrusion can be defined as any practice or act that attempt to crack the integrity,
confidentiality or availability of a resource. This may contain of a deliberate unauthorized attempt to access
the information, manipulate the data, or make a system unreliable or unusable. With the expansion of
computer networks at an alarming rate during the past decade, security has become one of the serious
issues of computer systems.IDS, is a detection mechanism for detecting the intrusive activities hidden
among the normal activities. The revolutionary establishment of IDS has attracted analysts to work
dedicatedly enabling the system to deal with technological advancements. Hence, in this regard, various
beneficial schemes and models have been proposed in order to achieve enhanced IDS. This paper
proposes a novel hybrid model for intrusion detection. The proposed framework in this paper may be
expected as another step towards advancement of IDS. The framework utilizes the crucial data mining
classification algorithms beneficial for intrusion detection. The Hybrid framework would hence forth, will
lead to effective, adaptive and intelligent intrusion detection.
Machine learning are used for numerous functions like image processing, data mining, prediction analysis, online shopping, cybersecurity, digital forensics, network security etc. the aim of this research work is to explore on the research work that implement security system or provide a framework for system security using machine learning algorithms. Furthermore to explore other fields that applied machine learning algorithms to solve their problems. Stipulate the essential use of the technique, once an algorithm was trained on how to manipulate the provided data, the process of implementation remain automatic.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
In this presentation, Sheba introduces the topic of IoT and the associated trends. Sheba's interest areas lie in applications of IoT in healthcare and more particularly Physiotherapy.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
Energy Management with Disaster Intimation and Control using IoTIJEACS
In the area of digitization and automation, the life of
human being is getting simpler as almost everything is
automated. Nowadays humans have made internet an integral
part of their everyday life without which they are helpless.
Internet of things (IoT) gives a platform which allows different
devices to inter-connect, sense and control the things remotely
across a network infrastructure without any limitation to the
coverage area. In our proposed work, we stress on Wireless-
Home-Automation-System (WHAS) using IoT, it is a system uses
computers or smart phone to control basic home functions and
features automatically through internet from anywhere around
the world, an automated home is sometimes called a smart home.
The proposed system is able to monitor the entire things
connected to the internet and also to be maintaining the status of
individual devices for further action. We have built the home
automation with several devices and sensors, here sensor help to
monitor the device status and intimate the authorized person to
take particular action.
An Analysis of the Architecture of the Internet of Things.pdfCIOWomenMagazine
As we all know internet of things is a system of interrelated and inter-connected objects. These objects are able to collect and transfer data via a wireless network without any human intervention.
Implementing this concept is not an easy task by any measure for many reasons including the complex nature of the different components of the ecosystem of IoT. To understand the gravity of this task, we will explain all the five components of IoT Implementation
Our day-to-day lives are filled with situations
where we need to prove who we are; may it be for personal
reasons or as part of your profession. Locks are to be opened,
e-mail accounts are to be accessed and purchases are to be
made – but only by the person correctly authorized to do so.
In this era of Digitization and Automation, the life of human beings is getting simpler as almost everything is automatic, replacing the old manual systems. Nowadays humans have made internet an integral part of their everyday life without which they are helpless. Internet of things IOT provides a platform that allows devices to connect, sensed and controlled remotely across a network infrastructure. Our project basically focuses on Laboratory automation using smart phone and computer. The IOT devices controls and monitors the electronic electrical and the mechanical systems used in various types of buildings. The devices connected to the cloud server are controlled by a single admin which facilitate a number of users to which a number of sensor and control nodes are connected. The system designed is economical and can be expanded as it allows connection and controlling of a number of different devices. Deepak Adhav | Rahul Pagar | Ravi Sonawane | Sachin Tawade ""Smart Laboratory"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22840.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/22840/smart-laboratory/deepak-adhav
2021 python projects list
A BI-OBJECTIVE HYPER-HEURISTIC SUPPORT VECTOR MACHINES FOR BIG DATA CYBER-SECURITY
AN ARTIFICIAL INTELLIGENCE AND CLOUD BASED COLLABORATIVE PLATFORM FOR PLANT DISEASE IDENTIFICATION, TRACKING AND FORECASTING FOR FARMERS
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
9.data analysis for understanding the impact of covid–19 vaccinations on the ...Venkat Projects
9.data analysis for understanding the impact of covid–19 vaccinations on the society
In this paper author analysing vaccines dataset to forecast required vaccines compare to manufacturing or available vaccines and by using this forecasting manufacturers may increase and decrease their manufacturing quantity. This forecasting can impact society by taking decision on manufacturing vaccines and if in society more cases occurred then forecasting will be high and by seeing forecasting manufacturers may increase production.
Vaccines are manufacturing by multiple manufacturers such as JOHNSON AND JOHNSON, PFIZER and many more. In this forecasting will take all manufacturers and their production quantity as well as usage of vaccines and based on this Machine Learning algorithm called Decision Tree will forecast require vaccines for next 30 days
To implement this project we are using vaccines dataset to train decision tree algorithm and then this algorithm will predict require vaccines quantity for next 30 days. This dataset is saved inside ‘Dataset’ folder and below screen showing some records from dataset
6.iris recognition using machine learning techniqueVenkat Projects
In this project to recognize person from IRIS we are using CASIA IRIS dataset which contains images from 108 peoples and by using this dataset we are training CNN model and then we can use this CNN model to predict/recognize persons. To train CNN model we are extracting IRIS features by using HoughCircles algorithm which extract IRIS circle from eye images. Below screen shots showing dataset with person id and this dataset saved inside ‘CASIA1’ folder
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
An effecient spam detection technique for io t devices using machine learning
1. An Efficient Spam Detection Technique For IOT Devices Using Machine
Learning
Abstract:
The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked
over wired or wireless channel for data transmission. IoT has grown rapidly over the past decade
with more than 25 billion devices expected to be connected by 2020. The volume of data
released from these devices will increase many-fold in the years to come. In addition to an
increased volume, the IoT devices produces a large amount of data with a number of different
modalities having varying data quality defined by its speed in terms of time and position
dependency. In such an environment, machine learning (ML) algorithms can play an important
role in ensuring security and authorization based on biotechnology, anomalous detection to
improve the usability, and security of IoT systems. On the other hand, attackers often view
learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from
these, in this article, we propose the security of the IoT devices by detecting spam using ML. To
achieve this objective, Spam Detection in IoT using Machine Learning framework is proposed.
In this framework, five ML models are evaluated using various metrics with a large collection of
inputs features sets. Each model computes a spam score by considering the refined input
features. This score depicts the trustworthiness of IoT device under various parameters. REFIT
Smart Home data set is used for the validation of proposed technique. The results obtained
proves the effectiveness of the proposed scheme in comparison to the other existing schemes.
Existing System:
The safety measures of IoT devices depends upon the size and type of organization in which it is
imposed. The behavior of users forces the security gateways to cooperate. In other words, we can
say that the location, nature, application of IoT devices decides the security measures. For
instance, the smart IoT security cameras in the smart organization can capture the different
parameters for analysis and intelligent decision making. The maximum care to be taken is with
web based devices as maximum number of IoT devices are web dependent. It is common at the
workplace that the IoT devices installed in an organization can be used to implement security
and privacy features efficiently. For example, wearable devices collect and send user’s health
data to a connected smartphone should prevent leakage of information to ensure privacy. It has
been found in the market that 25-30% of working employees connect their personal IoT devices
with the organizational network. The expanding nature of IoT attracts both the audience, i.e., the
users and the attackers.
However, with the emergence of ML in various attacks scenarios, IoT devices choose a
defensive strategy and decide the key parameters in the security protocols for trade-off between
2. security, privacy and computation. This job is challenging as it is usually difficult for an IoT
system with limited resources to estimate the current network and timely attack status.
Proposed System:
1) The proposed scheme of spam detection is validated using five different machine learning
models.
2) An algorithm is proposed to compute the spamicity score of each model which is then used for
detection and intelligent decision making.
3) Based upon the spamicity score computed in previous step, the reliability of IoT devices is
analyzed using different evaluation metrics.
1) Feature Engineering: The machine learning algorithms works accurately with the
appropriate instances and their attributes. We all know that the instances are the real data
world value, gathered from the real world smart objects deployed across the globe.
Feature extraction and feature selection are the core of feature engineering process.
Feature reduction: This methods is used to reduce the dimension of data. In other words,
feature reduction is the procedure to reduce the complexity of features. This technique
reduces the issues like, over-fitting, large memory requirement, computation power. There
are various feature extraction techniques. Among these, principal component analysis (PCA)
is the most popular . But, the method used in this proposal is PCA along with following IoT
parameters.
– Analysis time: The dataset used in the experiments, contains the data recorded for the span
of eighteen months. For better results and accuracy, we have considered the data of one
month. Considering the fact, the climate is the important parameter for the working of IoT
device, the month with maximum variations has been taken into the consideration
– Web based appliances: Only those appliances are included, which stay connected with web
for their working. The data collection includes the appliances: Television, Set top box, DVD
player/recorder, HiFi, Electric heater, Fridge, Dishwasher, Toaster, Coffee maker, Kettle,
Freezer, Washing machine, Tumble dryer, Electric heater, DAB radio, Desktop PC, PC
monitor, Printer, Router, Electric heater, Electric heater, Shredder, Freezer, Lamp, Alarm
radio, Lava lamp, CD player, Television, Video player, Set top box, Hub (network). Feature
selection: It is the process of computing the most important subset of features. It works by
computing the importance of each feature [16].
3. Entropy based filter is used as a feature selection technique in this proposal. – Entropy-based
filter: This algorithm uses the correlation among the discrete attributes with continuous
attributes to find out the weights of discrete attributes .
There are three functions using this entropy based filter namely, information.gain, gain.ratio,
symmetrical.uncertainty. The syntax for these functions are:
information.gain(formula, data, unit)
gain.ratio(formula, data, unit)
symmetrical.uncertainty(formula, data, unit)
The arguments used in the function definition are described here.
a) formula: It is the description of the working behind the algorithm.
b) data: It is the set of training data with the defined attributes for which the selection is to be
made.
c) unit: It is the unit which is used for entropy computing. By default it takes the value “log”.