This document discusses statistical approaches for detecting anomalies in network traffic. It begins by describing the typical four-stage process for anomaly detection: data collection, data analysis/feature extraction, inference to classify traffic as normal or anomalous, and validation. It then discusses several specific statistical approaches that can be used:
(1) Extracting features using statistical models of the traffic distributions, such as α-stable distributions, which can properly model highly variable network traffic.
(2) Using techniques like the Kalman filter to analyze traffic volume changes at different time scales and detect both short-term and long-term anomalies.
(3) Applying the Holt-Winters forecasting technique to decompose traffic into a baseline, trend,
The mobile ad hoc network is an infrastructure less system of mobility appliance connected by wireless.
The system protection violate cannot be prohibited using access and information flow control. This violate may
be outcome system software and hardware failures interrelate system organizational actions or disappointment
of the system verification module. The required for generate the existing methods into more difficult is in
addition rising, because it result into fresh and other useful resolution. Intrusion detection is a significant part in
the detection system abuse in many cases in current research works. An intrusion detection system is the
capability to sense intruders and abuser actions in the system in a competent and sensible fashion. An Intruder
that collaborate a mobile node in MANET eliminates the communication between the nodes. By distribution
fake routing information, provided that false link status information, and plentiful other nodes with superfluous
routing traffic information. The dependency and decentralized of MANET facilitate a challenger to enlarge
innovative type of attacks that are measured to demolish the cooperative algorithms used in ad hoc networks.
MANET is mostly susceptible to several kinds of attacks like inactive eavesdropping, dynamic impersonation,
and denial of services. An Intruder that collaborate a mobile node in MANET obliterate the communication
between the nodes by dissemination fake routing information. If inaccurate link state information, and abundant
other nodes with superfluous routing traffic information. Therefore, successful implementation of MANET
based on user’s poise in its security. The security research in MANET has paying attention on key managing,
routing protocol and intrusion detection techniques. Assessment on intrusion detection and supportive layer in
MANET endow with resolution to extend their real world applications. In this paper, aspire to revision the
various intrusion detections and prevention systems that were anticipated for Mobile Ad hoc Networks
(MANETs). And then compare the latest techniques Intrusion Detection dependent on their architecture and
data gathering techniques
Anomaly detection by using CFS subset and neural network with WEKA tools Drjabez
This document summarizes a research paper that proposes a new approach for anomaly detection in computer networks using CFS subset selection and neural networks with WEKA tools. The proposed approach uses CFS to select important features and neural networks like MLP, logistic regression and ELM for classification. Experiments on datasets show the proposed approach has lower execution time, higher anomaly detection rates, and lower CPU utilization compared to other machine learning methods. The approach effectively detects different types of attacks in computer networks.
An intrusion detection system plays a major role in network security. We
propose a model “DB-OLS: An Approach for IDS” which is a Deviation Based-Outlier
approach for Intrusion detection using Self Organizing Maps. In this model “Self
Organizing Map” approach is to be used for behavior learning and “Outlier mining”
approach, for detecting an intruder by calculating deviation from known user profile.
This model aims to improve the capability of detecting intruders.
Real Time Intrusion Detection System Using Computational Intelligence and Neu...ijtsrd
Today, Intrusion detection system using neural network is interested and measurable area for the researchers. The computational intelligence describe based on following parameters such as computational speed, adaptation, error resilience and fault tolerance. A good intrusion detection system must be satisfied adaptable as requirements. The objective of this paper, provide an outline of the research progress via computational intelligence and neural network over the intrusion detection. In this paper focused, existing research challenges, review analysis, research suggestion regarding Intrusion detection system. Dr. Prabha Shreeraj Nair"Real Time Intrusion Detection System Using Computational Intelligence and Neural Network: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5781.pdf http://www.ijtsrd.com/engineering/computer-engineering/5781/real-time-intrusion-detection-system-using-computational-intelligence-and-neural-network-a-review/dr-prabha-shreeraj-nair
This document summarizes various papers on developing intrusion detection systems using neural networks. It discusses different algorithms researchers have used to train neural networks for intrusion detection, including feed-forward neural networks, self-organizing maps, test driven development neural networks, combinations of supervised and unsupervised learning techniques, differential evolution, and backpropagation neural networks. Each algorithm has advantages and disadvantages. The document concludes that neural networks provide a flexible approach to intrusion detection and can learn new intrusion patterns, and proposes developing an additional level of protection using self-organizing maps to better detect intrusions.
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.
Research Inventy : International Journal of Engineering and Science is publis...researchinventy
This document summarizes a research paper that proposes using principal component analysis and sequential hypothesis testing in a game theory framework to detect intrusions in a mobile ad hoc network. Specifically, it detects replica node attacks by tracking features like source/destination addresses and routing requests/replies to build profiles for each node. It then applies sequential probability ratio testing on routing requests and replies to test hypotheses about whether nodes are normal or abnormal. A two-player game model is also used to identify the optimal attack and defense strategies between an attacker and defender. Simulation results show that the approach can decrease the number of claims needed for detection while minimizing false positives and negatives.
IRJET- Anomaly Detection System in CCTV Derived VideosIRJET Journal
This document describes a proposed system for anomaly detection in CCTV videos using deep learning techniques. The system has two main components: 1) feature extraction using convolutional neural networks to learn representations of normal behavior from training videos, and 2) an anomaly detection classifier to identify abnormal events in new videos based on the learned features. Several related works incorporating techniques like k-means clustering, decision trees, and neural networks for video-based anomaly detection are also reviewed. The methodology section outlines the overall framework, including preprocessing steps and separate training and testing phases to extract normal features and then detect anomalies.
The mobile ad hoc network is an infrastructure less system of mobility appliance connected by wireless.
The system protection violate cannot be prohibited using access and information flow control. This violate may
be outcome system software and hardware failures interrelate system organizational actions or disappointment
of the system verification module. The required for generate the existing methods into more difficult is in
addition rising, because it result into fresh and other useful resolution. Intrusion detection is a significant part in
the detection system abuse in many cases in current research works. An intrusion detection system is the
capability to sense intruders and abuser actions in the system in a competent and sensible fashion. An Intruder
that collaborate a mobile node in MANET eliminates the communication between the nodes. By distribution
fake routing information, provided that false link status information, and plentiful other nodes with superfluous
routing traffic information. The dependency and decentralized of MANET facilitate a challenger to enlarge
innovative type of attacks that are measured to demolish the cooperative algorithms used in ad hoc networks.
MANET is mostly susceptible to several kinds of attacks like inactive eavesdropping, dynamic impersonation,
and denial of services. An Intruder that collaborate a mobile node in MANET obliterate the communication
between the nodes by dissemination fake routing information. If inaccurate link state information, and abundant
other nodes with superfluous routing traffic information. Therefore, successful implementation of MANET
based on user’s poise in its security. The security research in MANET has paying attention on key managing,
routing protocol and intrusion detection techniques. Assessment on intrusion detection and supportive layer in
MANET endow with resolution to extend their real world applications. In this paper, aspire to revision the
various intrusion detections and prevention systems that were anticipated for Mobile Ad hoc Networks
(MANETs). And then compare the latest techniques Intrusion Detection dependent on their architecture and
data gathering techniques
Anomaly detection by using CFS subset and neural network with WEKA tools Drjabez
This document summarizes a research paper that proposes a new approach for anomaly detection in computer networks using CFS subset selection and neural networks with WEKA tools. The proposed approach uses CFS to select important features and neural networks like MLP, logistic regression and ELM for classification. Experiments on datasets show the proposed approach has lower execution time, higher anomaly detection rates, and lower CPU utilization compared to other machine learning methods. The approach effectively detects different types of attacks in computer networks.
An intrusion detection system plays a major role in network security. We
propose a model “DB-OLS: An Approach for IDS” which is a Deviation Based-Outlier
approach for Intrusion detection using Self Organizing Maps. In this model “Self
Organizing Map” approach is to be used for behavior learning and “Outlier mining”
approach, for detecting an intruder by calculating deviation from known user profile.
This model aims to improve the capability of detecting intruders.
Real Time Intrusion Detection System Using Computational Intelligence and Neu...ijtsrd
Today, Intrusion detection system using neural network is interested and measurable area for the researchers. The computational intelligence describe based on following parameters such as computational speed, adaptation, error resilience and fault tolerance. A good intrusion detection system must be satisfied adaptable as requirements. The objective of this paper, provide an outline of the research progress via computational intelligence and neural network over the intrusion detection. In this paper focused, existing research challenges, review analysis, research suggestion regarding Intrusion detection system. Dr. Prabha Shreeraj Nair"Real Time Intrusion Detection System Using Computational Intelligence and Neural Network: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5781.pdf http://www.ijtsrd.com/engineering/computer-engineering/5781/real-time-intrusion-detection-system-using-computational-intelligence-and-neural-network-a-review/dr-prabha-shreeraj-nair
This document summarizes various papers on developing intrusion detection systems using neural networks. It discusses different algorithms researchers have used to train neural networks for intrusion detection, including feed-forward neural networks, self-organizing maps, test driven development neural networks, combinations of supervised and unsupervised learning techniques, differential evolution, and backpropagation neural networks. Each algorithm has advantages and disadvantages. The document concludes that neural networks provide a flexible approach to intrusion detection and can learn new intrusion patterns, and proposes developing an additional level of protection using self-organizing maps to better detect intrusions.
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.
Research Inventy : International Journal of Engineering and Science is publis...researchinventy
This document summarizes a research paper that proposes using principal component analysis and sequential hypothesis testing in a game theory framework to detect intrusions in a mobile ad hoc network. Specifically, it detects replica node attacks by tracking features like source/destination addresses and routing requests/replies to build profiles for each node. It then applies sequential probability ratio testing on routing requests and replies to test hypotheses about whether nodes are normal or abnormal. A two-player game model is also used to identify the optimal attack and defense strategies between an attacker and defender. Simulation results show that the approach can decrease the number of claims needed for detection while minimizing false positives and negatives.
IRJET- Anomaly Detection System in CCTV Derived VideosIRJET Journal
This document describes a proposed system for anomaly detection in CCTV videos using deep learning techniques. The system has two main components: 1) feature extraction using convolutional neural networks to learn representations of normal behavior from training videos, and 2) an anomaly detection classifier to identify abnormal events in new videos based on the learned features. Several related works incorporating techniques like k-means clustering, decision trees, and neural networks for video-based anomaly detection are also reviewed. The methodology section outlines the overall framework, including preprocessing steps and separate training and testing phases to extract normal features and then detect anomalies.
Computer Worms Based on Monitoring Replication and Damage: Experiment and Eva...IOSRjournaljce
This document describes experiments conducted to evaluate a proposed worm detection system (WDS) and its ability to detect computer worms. The experiments involved networking three machines and transferring files between them that may contain worms. Five known worms that cause specific types of damage were tested to evaluate if the WDS could detect the worms through their damage or replication behavior. Additional experiments tested the WDS's ability to detect unknown worms and known worms through signature matching. The results showed that the WDS successfully detected the worms and met all evaluation criteria.
This document summarizes research on intrusion detection systems using data mining techniques. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using models, a data warehouse to store data and models, and a model generator to develop and distribute new models. It then discusses supervised and unsupervised learning approaches for intrusion detection. The document concludes by summarizing several papers on intrusion detection using techniques like neural networks, decision trees, clustering, and ensemble methods.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
This document summarizes a research paper that analyzes machine learning algorithms for intrusion detection using the UNSW-NB15 dataset. It compares the performance of classifiers like KNN, SGD, Random Forest, Logistic Regression, and Naive Bayes, both with and without feature selection. Chi-Square feature selection is applied to reduce irrelevant features before training the classifiers. The classifiers' performance is evaluated based on metrics like accuracy, precision, recall, F1-score, true positive rate and false positive rate. The paper finds that feature selection can improve classifiers' performance for intrusion detection.
NSL KDD Cup 99 dataset Anomaly Detection using Machine Learning Technique Sujeet Suryawanshi
This document summarizes a presentation given on using decision trees and machine learning techniques for anomaly detection on the NSL KDD Cup 99 dataset. It discusses anomaly detection, machine learning, different machine learning algorithms like decision trees, SVM, Naive Bayes etc. and their application for intrusion detection. It then describes an experiment conducted using the decision tree algorithm on the NSL KDD Cup 99 dataset to classify network traffic as normal or anomalous. The results showed the decision tree model achieved over 98% accuracy on both the full dataset and a reduced feature set.
Comparison of Data Mining Techniques used in Anomaly Based IDS IRJET Journal
This document discusses anomaly-based intrusion detection systems and compares various data mining techniques used in these systems. It begins by defining intrusion detection systems and the two main categories of misuse detection and anomaly detection. Anomaly detection involves learning normal patterns from data and detecting deviations from these patterns as potential anomalies or intrusions.
The document then examines several data mining techniques used for anomaly detection, including statistical-based approaches like chi-square statistics, and clustering algorithms like k-means, k-medoids, and EM clustering. It notes that these techniques can be applied to intrusion detection to analyze data and detect anomalies representing potential malicious activity. The methodology of anomaly detection is also summarized as involving parameterization of data,
Misuse detection approaches aim to detect known intrusion patterns by encoding them as signatures. Signatures precisely define the patterns of events that characterize an intrusion. This allows misuse detection to be fully effective against known attacks but provides no detection for unknown attacks. Common misuse detection techniques include pattern matching, rule-based systems, and state-based analysis. Pattern matching searches for encoded signatures in audit data, while rule-based systems apply rules to detect intrusion scenarios. State-based systems represent intrusions as state transitions to identify compromised system states.
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
This document describes a proposed hybrid intrusion detection model that uses feature selection and machine learning algorithms with misuse detection. The model first selects important features from the NSL-KDD dataset and generates rules based on the behaviors of those features using J48 and CART algorithms. These rules are then used to build an intrusion detection framework that is tested on the NSL-KDD dataset, achieving an accuracy of 88.23%, outperforming other models that require prior learning of attacks. The proposed model works on the concept of misuse detection and can detect intrusions based on feature behaviors without any previous training.
11.a genetic algorithm based elucidation for improving intrusion detection th...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions. The genetic algorithm evolves rules over generations to maximize fitness. Experiments show this approach can improve detection rates and reduce false alarms compared to existing intrusion detection systems.
1.[1 9]a genetic algorithm based elucidation for improving intrusion detectio...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions with a condensed feature set. The genetic algorithm is used to evolve rules from the reduced training data, with a fitness function evaluating rule quality. Experiments and evaluations are conducted on the KDD Cup 99 dataset to test the proposed method.
AN EFFICIENT INTRUSION DETECTION SYSTEM WITH CUSTOM FEATURES USING FPA-GRADIE...IJCNCJournal
An efficient Intrusion Detection System has to be given high priority while connecting systems with a network to prevent the system before an attack happens. It is a big challenge to the network security group to prevent the system from a variable types of new attacks as technology is growing in parallel. In this paper, an efficient model to detect Intrusion is proposed to predict attacks with high accuracy and less false-negative rate by deriving custom features UNSW-CF by using the benchmark intrusion dataset UNSW-NB15. To reduce the learning complexity, Custom Features are derived and then Significant Features are constructed by applying meta-heuristic FPA (Flower Pollination algorithm) and MRMR (Minimal Redundancy and Maximum Redundancy) which reduces learning time and also increases prediction accuracy. ENC (ElasicNet Classifier), KRRC (Kernel Ridge Regression Classifier), IGBC (Improved Gradient Boosting Classifier) is employed to classify the attacks in the datasets UNSW-CF, UNSW and recorded that UNSW-CF with derived custom features using IGBC integrated with FPA provided high accuracy of 97.38% and a low error rate of 2.16%. Also, the sensitivity and specificity rate for IGB attains a high rate of 97.32% and 97.50% respectively.
Design and Implementation of Artificial Immune System for Detecting Flooding ...Kent State University
Academic Paper: N. B. I. Al-Dabagh and I. A. Ali, "Design and implementation of artificial immune system for detecting flooding attacks," in High Performance Computing and Simulation (HPCS), 2011 International Conference on, 2011, pp. 381-390.
EFFECTIVENESS AND WEAKNESS OF QUANTIFIED/AUTOMATED ANOMALY BASED IDSIJNSA Journal
We shall discuss new problems of quantification/automation of anomaly-based Intrusion Detection System(IDS). We shall analyze effectiveness and weakness using our proposal method as an example, and derive new attack scenario. Development of anomaly-based IDS is necessary for correspondence to a high network attack, however, we shall show that it makes new different problems at the same time. In this paper, we shall discuss some attack scenario which makes invalidate our detection. As the result, we conclude that it is difficult to prevent such attacks technically, and security requirements for operation side become serious
This document summarizes and compares different clustering algorithms that can be used for network anomaly detection. It proposes a method that first applies clustering algorithms like k-means, hierarchical, and expectation maximization clustering to partition network traffic data into clusters. It then applies the ID3 decision tree algorithm on each cluster to classify instances as normal or anomalous. The performance of this combined method is compared to using just the clustering or ID3 algorithms individually. Real network data sets are used to evaluate performance based on various metrics. The combined method is found to outperform the individual algorithms. The document also reviews several other related works applying clustering and decision trees for network anomaly detection and privacy-preserving data mining.
IRJET- Improving Cyber Security using Artificial IntelligenceIRJET Journal
This document discusses using artificial intelligence techniques like machine learning algorithms to improve cyber security. It proposes a methodology that uses Splunk to extract relevant fields from cybersecurity data, feeds that into a K-means clustering algorithm to form attack clusters, then sends those clusters to individual artificial neural networks (ANNs). The aggregated ANN results are then fed into a support vector machine (SVM) which classifies attacks as malicious, non-malicious, or benign. Testing this approach on a dataset achieved a classification accuracy of over 92% when using Splunk, K-means, ANNs, and SVM together.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
We would send hard copy of Journal by speed post to the address of correspondence author after online publication of paper.
We will dispatched hard copy to the author within 7 days of date of publication
An efficient intrusion detection using relevance vector machineIAEME Publication
The document summarizes an efficient intrusion detection system using Relevance Vector Machine (RVM). It begins with an introduction to intrusion detection and types of attacks. Then it discusses related work using data mining techniques like SVM for intrusion detection. The proposed methodology preprocesses data from the KDD Cup 99 dataset, performs normalization, and classifies using RVM. RVM can provide sparse solutions and inferences with low computation. Experimental results on the KDD Cup 99 dataset show the technique achieves higher detection rates than regular SVM algorithms.
Hardware Trojan Identification and Detectionijcisjournal
The majority of techniques developed to detect hardware trojans are based on specific attributes. Further, the ad hoc approaches employed to design methods for trojan detection are largely ineffective. Hardware trojans have a number of attributes which can be used to systematically develop detection techniques.
Based on this concept, a detailed examination of current trojan detection techniques and the characteristics of existing hardware trojans is presented. This is used to develop a new approach to hardware trojan identification and classification. This identification can be used to compare trojan risk or severity and trojan detection effectiveness. Identification vectors are generated for each hardware trojan and trojan detection technique based on the corresponding attributes. Vectors are also defined which represent trojan risk or severity and trojan detection effectiveness.
IDS IN TELECOMMUNICATION NETWORK USING PCAIJCNCJournal
This document summarizes a research paper that proposes using principal component analysis (PCA) as a dimension reduction technique for intrusion detection systems (IDS). The paper applies PCA to reduce the number of features from 41 to either 6 or 10 features for the NSL-KDD dataset. One reduced feature set is used to develop a network IDS with high detection success and rate, while the other is used for a host IDS also with good detection success and very high detection rate. The paper outlines the process of applying PCA for IDS, including performing PCA on training data to identify principal components, then using those components to map new online data and detect intrusions based on deviation thresholds.
ADRISYA: A FLOW BASED ANOMALY DETECTION SYSTEM FOR SLOW AND FAST SCANIJNSA Journal
Attackers perform port scan to find reachability, liveness and running services in a system or network. Current day scanning tools provide different scanning options and capable of evading various security tools like firewall, IDS and IPS. So in order to detect and prevent attacks in the early stages, an accurate detection of scanning activity in real time is very much essential. In this paper we present a flow based protocol behaviour analysis system to detect TCP based slow and fast scan. This system provides scalable, accurate and generic solution to TCP based scanning by means of automatic behaviour analysis of the network traffic. Detection capability of proposed system is compared with SNORT and result proves the high detection rate of the system over SNORT.
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.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Web server load prediction and anomaly detection from hypertext transfer prot...IJECEIAES
As network traffic increases and new intrusions occur, anomaly detection solutions based on machine learning are necessary to detect previously unknown intrusion patterns. Most of the developed models require a labelled dataset, which can be challenging owing to a shortage of publicly available datasets. These datasets are often too small to effectively train machine learning models, which further motivates the use of real unlabeled traffic. By using real traffic, it is possible to more accurately simulate the types of anomalies that might occur in a real-world network and improve the performance of the detection model. We present a method able to predict and categorize anomalies without the aid of a labelled dataset, demonstrating the model’s usability while also gathering a dataset from real noisy network traffic. The proposed long short-term memory (LTSM) based intrusion detection system was tested in a real-world setting of an antivirus company and was successful in detecting various intrusions using 5-minute windowing over both the predicted and real update curves thereby demonstrating its usefulness. Our contribution was the development of a robust model generally applicable to any hypertext transfer protocol (HTTP) traffic with almost real-time anomaly detection, while also outperforming earlier studies in terms of prediction accuracy.
Computer Worms Based on Monitoring Replication and Damage: Experiment and Eva...IOSRjournaljce
This document describes experiments conducted to evaluate a proposed worm detection system (WDS) and its ability to detect computer worms. The experiments involved networking three machines and transferring files between them that may contain worms. Five known worms that cause specific types of damage were tested to evaluate if the WDS could detect the worms through their damage or replication behavior. Additional experiments tested the WDS's ability to detect unknown worms and known worms through signature matching. The results showed that the WDS successfully detected the worms and met all evaluation criteria.
This document summarizes research on intrusion detection systems using data mining techniques. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using models, a data warehouse to store data and models, and a model generator to develop and distribute new models. It then discusses supervised and unsupervised learning approaches for intrusion detection. The document concludes by summarizing several papers on intrusion detection using techniques like neural networks, decision trees, clustering, and ensemble methods.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
This document summarizes a research paper that analyzes machine learning algorithms for intrusion detection using the UNSW-NB15 dataset. It compares the performance of classifiers like KNN, SGD, Random Forest, Logistic Regression, and Naive Bayes, both with and without feature selection. Chi-Square feature selection is applied to reduce irrelevant features before training the classifiers. The classifiers' performance is evaluated based on metrics like accuracy, precision, recall, F1-score, true positive rate and false positive rate. The paper finds that feature selection can improve classifiers' performance for intrusion detection.
NSL KDD Cup 99 dataset Anomaly Detection using Machine Learning Technique Sujeet Suryawanshi
This document summarizes a presentation given on using decision trees and machine learning techniques for anomaly detection on the NSL KDD Cup 99 dataset. It discusses anomaly detection, machine learning, different machine learning algorithms like decision trees, SVM, Naive Bayes etc. and their application for intrusion detection. It then describes an experiment conducted using the decision tree algorithm on the NSL KDD Cup 99 dataset to classify network traffic as normal or anomalous. The results showed the decision tree model achieved over 98% accuracy on both the full dataset and a reduced feature set.
Comparison of Data Mining Techniques used in Anomaly Based IDS IRJET Journal
This document discusses anomaly-based intrusion detection systems and compares various data mining techniques used in these systems. It begins by defining intrusion detection systems and the two main categories of misuse detection and anomaly detection. Anomaly detection involves learning normal patterns from data and detecting deviations from these patterns as potential anomalies or intrusions.
The document then examines several data mining techniques used for anomaly detection, including statistical-based approaches like chi-square statistics, and clustering algorithms like k-means, k-medoids, and EM clustering. It notes that these techniques can be applied to intrusion detection to analyze data and detect anomalies representing potential malicious activity. The methodology of anomaly detection is also summarized as involving parameterization of data,
Misuse detection approaches aim to detect known intrusion patterns by encoding them as signatures. Signatures precisely define the patterns of events that characterize an intrusion. This allows misuse detection to be fully effective against known attacks but provides no detection for unknown attacks. Common misuse detection techniques include pattern matching, rule-based systems, and state-based analysis. Pattern matching searches for encoded signatures in audit data, while rule-based systems apply rules to detect intrusion scenarios. State-based systems represent intrusions as state transitions to identify compromised system states.
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
This document describes a proposed hybrid intrusion detection model that uses feature selection and machine learning algorithms with misuse detection. The model first selects important features from the NSL-KDD dataset and generates rules based on the behaviors of those features using J48 and CART algorithms. These rules are then used to build an intrusion detection framework that is tested on the NSL-KDD dataset, achieving an accuracy of 88.23%, outperforming other models that require prior learning of attacks. The proposed model works on the concept of misuse detection and can detect intrusions based on feature behaviors without any previous training.
11.a genetic algorithm based elucidation for improving intrusion detection th...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions. The genetic algorithm evolves rules over generations to maximize fitness. Experiments show this approach can improve detection rates and reduce false alarms compared to existing intrusion detection systems.
1.[1 9]a genetic algorithm based elucidation for improving intrusion detectio...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection. The paper aims to reduce features from the KDD Cup 99 dataset and generate a rule set using genetic algorithms to detect intrusions with a condensed feature set. The genetic algorithm is used to evolve rules from the reduced training data, with a fitness function evaluating rule quality. Experiments and evaluations are conducted on the KDD Cup 99 dataset to test the proposed method.
AN EFFICIENT INTRUSION DETECTION SYSTEM WITH CUSTOM FEATURES USING FPA-GRADIE...IJCNCJournal
An efficient Intrusion Detection System has to be given high priority while connecting systems with a network to prevent the system before an attack happens. It is a big challenge to the network security group to prevent the system from a variable types of new attacks as technology is growing in parallel. In this paper, an efficient model to detect Intrusion is proposed to predict attacks with high accuracy and less false-negative rate by deriving custom features UNSW-CF by using the benchmark intrusion dataset UNSW-NB15. To reduce the learning complexity, Custom Features are derived and then Significant Features are constructed by applying meta-heuristic FPA (Flower Pollination algorithm) and MRMR (Minimal Redundancy and Maximum Redundancy) which reduces learning time and also increases prediction accuracy. ENC (ElasicNet Classifier), KRRC (Kernel Ridge Regression Classifier), IGBC (Improved Gradient Boosting Classifier) is employed to classify the attacks in the datasets UNSW-CF, UNSW and recorded that UNSW-CF with derived custom features using IGBC integrated with FPA provided high accuracy of 97.38% and a low error rate of 2.16%. Also, the sensitivity and specificity rate for IGB attains a high rate of 97.32% and 97.50% respectively.
Design and Implementation of Artificial Immune System for Detecting Flooding ...Kent State University
Academic Paper: N. B. I. Al-Dabagh and I. A. Ali, "Design and implementation of artificial immune system for detecting flooding attacks," in High Performance Computing and Simulation (HPCS), 2011 International Conference on, 2011, pp. 381-390.
EFFECTIVENESS AND WEAKNESS OF QUANTIFIED/AUTOMATED ANOMALY BASED IDSIJNSA Journal
We shall discuss new problems of quantification/automation of anomaly-based Intrusion Detection System(IDS). We shall analyze effectiveness and weakness using our proposal method as an example, and derive new attack scenario. Development of anomaly-based IDS is necessary for correspondence to a high network attack, however, we shall show that it makes new different problems at the same time. In this paper, we shall discuss some attack scenario which makes invalidate our detection. As the result, we conclude that it is difficult to prevent such attacks technically, and security requirements for operation side become serious
This document summarizes and compares different clustering algorithms that can be used for network anomaly detection. It proposes a method that first applies clustering algorithms like k-means, hierarchical, and expectation maximization clustering to partition network traffic data into clusters. It then applies the ID3 decision tree algorithm on each cluster to classify instances as normal or anomalous. The performance of this combined method is compared to using just the clustering or ID3 algorithms individually. Real network data sets are used to evaluate performance based on various metrics. The combined method is found to outperform the individual algorithms. The document also reviews several other related works applying clustering and decision trees for network anomaly detection and privacy-preserving data mining.
IRJET- Improving Cyber Security using Artificial IntelligenceIRJET Journal
This document discusses using artificial intelligence techniques like machine learning algorithms to improve cyber security. It proposes a methodology that uses Splunk to extract relevant fields from cybersecurity data, feeds that into a K-means clustering algorithm to form attack clusters, then sends those clusters to individual artificial neural networks (ANNs). The aggregated ANN results are then fed into a support vector machine (SVM) which classifies attacks as malicious, non-malicious, or benign. Testing this approach on a dataset achieved a classification accuracy of over 92% when using Splunk, K-means, ANNs, and SVM together.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
We would send hard copy of Journal by speed post to the address of correspondence author after online publication of paper.
We will dispatched hard copy to the author within 7 days of date of publication
An efficient intrusion detection using relevance vector machineIAEME Publication
The document summarizes an efficient intrusion detection system using Relevance Vector Machine (RVM). It begins with an introduction to intrusion detection and types of attacks. Then it discusses related work using data mining techniques like SVM for intrusion detection. The proposed methodology preprocesses data from the KDD Cup 99 dataset, performs normalization, and classifies using RVM. RVM can provide sparse solutions and inferences with low computation. Experimental results on the KDD Cup 99 dataset show the technique achieves higher detection rates than regular SVM algorithms.
Hardware Trojan Identification and Detectionijcisjournal
The majority of techniques developed to detect hardware trojans are based on specific attributes. Further, the ad hoc approaches employed to design methods for trojan detection are largely ineffective. Hardware trojans have a number of attributes which can be used to systematically develop detection techniques.
Based on this concept, a detailed examination of current trojan detection techniques and the characteristics of existing hardware trojans is presented. This is used to develop a new approach to hardware trojan identification and classification. This identification can be used to compare trojan risk or severity and trojan detection effectiveness. Identification vectors are generated for each hardware trojan and trojan detection technique based on the corresponding attributes. Vectors are also defined which represent trojan risk or severity and trojan detection effectiveness.
IDS IN TELECOMMUNICATION NETWORK USING PCAIJCNCJournal
This document summarizes a research paper that proposes using principal component analysis (PCA) as a dimension reduction technique for intrusion detection systems (IDS). The paper applies PCA to reduce the number of features from 41 to either 6 or 10 features for the NSL-KDD dataset. One reduced feature set is used to develop a network IDS with high detection success and rate, while the other is used for a host IDS also with good detection success and very high detection rate. The paper outlines the process of applying PCA for IDS, including performing PCA on training data to identify principal components, then using those components to map new online data and detect intrusions based on deviation thresholds.
ADRISYA: A FLOW BASED ANOMALY DETECTION SYSTEM FOR SLOW AND FAST SCANIJNSA Journal
Attackers perform port scan to find reachability, liveness and running services in a system or network. Current day scanning tools provide different scanning options and capable of evading various security tools like firewall, IDS and IPS. So in order to detect and prevent attacks in the early stages, an accurate detection of scanning activity in real time is very much essential. In this paper we present a flow based protocol behaviour analysis system to detect TCP based slow and fast scan. This system provides scalable, accurate and generic solution to TCP based scanning by means of automatic behaviour analysis of the network traffic. Detection capability of proposed system is compared with SNORT and result proves the high detection rate of the system over SNORT.
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.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Web server load prediction and anomaly detection from hypertext transfer prot...IJECEIAES
As network traffic increases and new intrusions occur, anomaly detection solutions based on machine learning are necessary to detect previously unknown intrusion patterns. Most of the developed models require a labelled dataset, which can be challenging owing to a shortage of publicly available datasets. These datasets are often too small to effectively train machine learning models, which further motivates the use of real unlabeled traffic. By using real traffic, it is possible to more accurately simulate the types of anomalies that might occur in a real-world network and improve the performance of the detection model. We present a method able to predict and categorize anomalies without the aid of a labelled dataset, demonstrating the model’s usability while also gathering a dataset from real noisy network traffic. The proposed long short-term memory (LTSM) based intrusion detection system was tested in a real-world setting of an antivirus company and was successful in detecting various intrusions using 5-minute windowing over both the predicted and real update curves thereby demonstrating its usefulness. Our contribution was the development of a robust model generally applicable to any hypertext transfer protocol (HTTP) traffic with almost real-time anomaly detection, while also outperforming earlier studies in terms of prediction accuracy.
Network Traffic Anomaly Detection Through Bayes NetGyan Prakash
Traffic anomaly detection using high performance measurement systems offers the possibility of improving the speed of
detection and enabling detection of important, short lived anomalies. In this paper we investigate the problem of detecting anomalies
using traffic measurements with fine-grained time stamps. We develop a new detection algorithm (called KS3) that utilizes a Bayes
Net to efficiently consider multiple input signals and to explicitly define what is considered “anomalous”.
The input signals considered KS3 are traffic volumes and correlations between ingress egress packet and bit rates. These
complementary signals enable identification of expanded range of anomalies. Using a set of high precision traffic measurements
collected at our campus border router over a 10 month period and an annotated anomaly log supplied by our network operators, we
show that KS3 is highly accurate, identifying 86% of the anomalies listed in the log. Compared with well known time series-based
and wavelet-based detectors, this represents over a 20% improvement in accuracy. Investigation of events identified by KS3 that did
not appear in the operator log indicate many are, in fact, true positives. Deployment of Ks3 in an operational environment supports
this by showing zero false positives during initial tests.
A New Way of Identifying DOS Attack Using Multivariate Correlation Analysisijceronline
This document summarizes a research paper that proposes a new method for identifying denial of service (DoS) attacks using multivariate correlation analysis (MCA). The method involves three main steps: 1) generating basic features from network traffic, 2) using MCA to extract correlations between features and generate triangle area maps, and 3) using an anomaly-based detection mechanism to distinguish attacks from normal traffic based on differences from pre-generated normal profiles. The researchers evaluate their method on the KDD Cup 99 dataset and achieve moderate detection performance. However, they identify issues related to differences in feature scales that reduce detection of some attacks. They propose using statistical normalization to address this.
Passive monitoring to build Situational AwarenessDavid Sweigert
Passive network monitoring techniques can provide valuable situational awareness for network security professionals. The document describes techniques for passively discovering information about nodes on a network, including operating systems, roles, services, and configurations. This contextual information helps analysts by reducing false positives and focusing resources. The passive approach does not disrupt networks and can operate continuously, in contrast to active scanning tools. A network monitoring prototype is being developed to test these passive discovery techniques.
This document summarizes 5 references related to machine learning and data mining for computer security and anomaly detection. Reference 1 discusses using decision trees to classify server traffic based on a set of designed features. Reference 2 argues that analyzing distributions of packet features can detect and identify a diverse set of anomalies. Reference 3 examines machine learning issues in anomaly detection for computer security. Reference 4 provides an overview of using machine learning and data mining for problems in computer security. Reference 5 covers basic statistical techniques for computer intrusion detection and network monitoring.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
This document discusses using a random forest classifier with feature selection to improve intrusion detection. It begins with background on intrusion detection systems and challenges. It then proposes using genetic algorithms for feature selection to identify the most important features from a dataset. A random forest classifier is used for classification, which combines decision trees to improve accuracy. The methodology involves feature selection, classification with random forest, and detection. Feature weights are calculated and cross-validation is used to analyze detection rates for individual attacks. The goal is to improve accuracy, reduce training time, and better detect minority attacks through this approach.
Attack Detection Availing Feature Discretion using Random Forest ClassifierCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
This document compares the k-means data mining and outlier detection approaches for network-based intrusion detection. It analyzes four datasets capturing network traffic using both approaches. The k-means approach clusters traffic into normal and abnormal flows, while outlier detection calculates an outlier score for each flow. The document finds that k-means was more accurate and precise, with a better classification rate than outlier detection. It requires less computer resources than outlier detection. This comparison of the approaches can help network administrators choose the best intrusion detection method.
Defending Reactive Jammers in WSN using a Trigger Identification Service.ijsrd.com
In the last decade, the greatest threat to the wireless sensor network has been Reactive Jamming Attack because it is difficult to be disclosed and defend as well as due to its mass destruction to legitimate sensor communications. As discussed above about the Reactive Jammers Nodes, a new scheme to deactivate them efficiently is by identifying all trigger nodes, where transmissions invoke the jammer nodes, which has been proposed and developed. Due to this identification mechanism, many existing reactive jamming defending schemes can be benefited. This Trigger Identification can also work as an application layer .In this paper, on one side we provide the several optimization problems to provide complete trigger identification service framework for unreliable wireless sensor networks and on the other side we also provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios.
A Survey on Data Intrusion schemes used in MANETIRJET Journal
The document discusses data intrusion schemes used in mobile ad hoc networks (MANETs). It reviews common problems with data intrusion in MANETs due to their dynamic architecture and limited resources. Several proposed intrusion detection schemes are described, including distributed and cooperative schemes, specification-based schemes, and the proposed Random Walker Detection method. The proposed method aims to efficiently detect intrusions by deploying detection engines at each node and excluding detection engines from random walkers to reduce detection latency. It is described as working on three network layers and using advanced encryption standards to securely detect and route around malicious nodes.
A COMBINATION OF TEMPORAL SEQUENCE LEARNING AND DATA DESCRIPTION FOR ANOMALYB...IJNSA Journal
Through continuous observation and modelling of normal behavior in networks, Anomaly-based Network Intrusion Detection System (A-NIDS) offers a way to find possible threats via deviation from the normal model. The analysis of network traffic based on time series model has the advantage of exploiting the relationship between packages within network traffic and observing trends of behaviors over a period of time. It will generate new sequences with good features that support anomaly detection in network traffic and provide the ability to detect new attacks. Besides, an anomaly detection technique, which focuses on the normal data and aims to build a description of it, will be an effective technique for anomaly detection in imbalanced data. In this paper, we propose a combination model of Long Short Term Memory (LSTM) architecture for processing time series and a data description Support Vector Data Description (SVDD) for anomaly detection in A-NIDS to obtain the advantages of them. This model helps parameters in LSTM and SVDD are jointly trained with joint optimization method. Our experimental results with KDD99 dataset show that the proposed combined model obtains high performance in intrusion detection, especially DoS and Probe attacks with 98.0% and 99.8%, respectively.
A COMBINATION OF TEMPORAL SEQUENCE LEARNING AND DATA DESCRIPTION FOR ANOMALYB...IJNSA Journal
This document summarizes a research paper that proposes combining long short-term memory (LSTM) and support vector data description (SVDD) for anomaly detection in anomaly-based network intrusion detection systems (A-NIDS). The paper argues that analyzing network traffic as a time series using LSTM can capture relationships between packets, but LSTM alone does not directly optimize for anomaly detection. It also notes that A-NIDS often only have normal data available for training. Therefore, the paper proposes combining LSTM to learn temporal features from network traffic with SVDD, an anomaly detection technique that builds a description of normal data. The combined model trains LSTM and SVDD parameters jointly using a joint optimization method. An evaluation on the KDD99 dataset
Reliable and Efficient Data Acquisition in Wireless Sensor NetworkIJMTST Journal
The sensors in the WSN sense the surrounding, collects the data and transfers the data to the sink node. It
has been observed that the sensor nodes are deactivated or damaged when exposed to certain radiations or
due to energy problems. This damage leads to the temporary isolation of the nodes from the network which
results in the formation of the holes. These holes are dynamic in nature and can grow and shrink depending
upon the factors causing the damage to the sensor nodes. So a solution has been presented in the base paper
where the dual mode i.e. Radio frequency and the Acoustic mode are considered so that the data can be
transferred easily. Based on this a survey has been done where several factors are studied so that the
performance of the system can be increased.
This document describes a preprocessing expert system for mining association rules from alarm data in telecommunication networks. The system addresses several issues with directly mining the original alarm data, including time non-synchronization of alarms and the need to assign different weights to alarm attributes. The proposed system uses a time window technique to convert original alarms into transactions and a neural network to classify alarms into different levels according to their characteristics, in order to mine weighted association rules. Simulation results demonstrate the effectiveness of the preprocessing expert system in analyzing alarm correlation for fault diagnosis.
This document describes a preprocessing expert system for mining association rules from alarm data in telecommunication networks. The system addresses several issues with directly mining the original alarm data, including time non-synchronization of alarms and the need to assign different weights to different alarm attributes. The proposed system uses a time window technique to convert original alarms into transactions and a neural network technique to classify alarms into different levels based on their characteristics, in order to mine weighted association rules. Simulation results and a real-world application demonstrate the effectiveness of the preprocessing expert system.
Internet ttraffic monitering anomalous behiviour detectionGyan Prakash
This document discusses a methodology for monitoring internet traffic and detecting anomalous behavior. It begins by noting the challenges of understanding vast quantities of internet traffic data due to the diversity of applications and services. Recent cyber attacks have made it important to develop techniques to analyze communication patterns in traffic data for network security purposes.
The proposed methodology uses data mining and entropy-based techniques to build behavior profiles of internet backbone traffic. It involves clustering traffic based on communication patterns, automatically classifying behaviors, and modeling structures for analysis. The methodology is validated using data sets from internet core links. It aims to automatically discover significant behaviors, provide interpretations, and quickly identify anomalous events like scanning or denial of service attacks.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
This document describes a vehicle theft detection system that uses radio frequency identification (RFID) technology. The system involves embedding an RFID chip in each vehicle that continuously transmits a unique identification signal. When a vehicle is stolen, the owner reports it to the police, who upload the vehicle's information to a central database. Police vehicles are equipped with RFID receivers. If a stolen vehicle passes within range of a receiver, the receiver detects the vehicle's ID signal and displays its details on a tablet. This allows police to quickly identify and recover stolen vehicles. The system aims to make it difficult for thieves to hide a vehicle's identity and allows vehicles to be tracked globally wherever the detection system is implemented.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document compares the k-means and grid density clustering algorithms. It summarizes that grid density clustering determines dense grids based on the densities of neighboring grids, and is able to handle different shaped clusters in multi-density environments. The grid density algorithm does not require distance computation and is not dependent on the number of clusters being known in advance like k-means. The document concludes that grid density clustering is better than k-means clustering as it can handle noise and outliers, find arbitrary shaped clusters, and has lower time complexity.
This document proposes a method for detecting, localizing, and extracting text from videos with complex backgrounds. It involves three main steps:
1. Text detection uses corner metric and Laplacian filtering techniques independently to detect text regions. Corner metric identifies regions with high curvature, while Laplacian filtering highlights intensity discontinuities. The results are combined through multiplication to reduce noise.
2. Text localization then determines the accurate boundaries of detected text strings.
3. Text binarization filters background pixels to extract text pixels for recognition. Thresholding techniques are used to convert localized text regions to binary images.
The method exploits different text properties to detect text using corner metric and Laplacian filtering. Combining the results improves
This document describes the design and implementation of a low power 16-bit arithmetic logic unit (ALU) using clock gating techniques. A variable block length carry skip adder is used in the arithmetic unit to reduce power consumption and improve performance. The ALU uses a clock gating circuit to selectively clock only the active arithmetic or logic unit, reducing dynamic power dissipation from unnecessary clock charging/discharging. The ALU was simulated in VHDL and synthesized for a Xilinx Spartan 3E FPGA, achieving a maximum frequency of 65.19MHz at 1.98mW power dissipation, demonstrating improved performance over a conventional ALU design.
This document describes using particle swarm optimization (PSO) and genetic algorithms (GA) to tune the parameters of a proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. PSO and GA are used to minimize the objective function by adjusting the PID parameters to achieve optimal step response with minimal overshoot, settling time, and rise time. The results show that PSO provides high-quality solutions within a shorter calculation time than other stochastic methods.
This document discusses implementing trust negotiations in multisession transactions. It proposes a framework that supports voluntary and unexpected interruptions, allowing negotiating parties to complete negotiations despite temporary unavailability of resources. The Trust-x protocol addresses issues related to validity, temporary loss of data, and extended unavailability of one negotiator. It allows a peer to suspend an ongoing negotiation and resume it with another authenticated peer. Negotiation portions and intermediate states can be safely and privately passed among peers to guarantee stability for continued suspended negotiations. An ontology is also proposed to provide formal specification of concepts and relationships, which is essential in complex web service environments for sharing credential information needed to establish trust.
This document discusses and compares various nature-inspired optimization algorithms for resolving the mixed pixel problem in remote sensing imagery, including Biogeography-Based Optimization (BBO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). It provides an overview of each algorithm, explaining key concepts like migration and mutation in BBO. The document aims to prove that BBO is the best algorithm for resolving the mixed pixel problem by comparing it to other evolutionary algorithms. It also includes figures illustrating concepts like the species model and habitat in BBO.
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
This document summarizes research on using wireless sensor networks to detect mobile targets. It discusses two optimization problems: 1) maximizing the exposure of the least exposed path within a sensor budget, and 2) minimizing sensor installation costs while ensuring all paths have exposure above a threshold. It proposes using tabu search heuristics to provide near-optimal solutions. The research also addresses extending the models to consider wireless connectivity, heterogeneous sensors, and intrusion detection using a game theory approach. Experimental results show the proposed mobile replica detection scheme can rapidly detect replicas with no false positives or negatives.
This document compares several propagation path loss models - Okumura, Hata, ECC 33, Cost-231, and SUI - by estimating path losses and signal strengths at 950 MHz in urban, suburban, and rural areas. Path losses are estimated using each model and compared to measured practical data from those environments. The results show that the Hata model most closely matches the practical data across all three environments. Therefore, the Hata model is concluded to be the most suitable for predicting signal strength in urban, suburban, and rural areas.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.