Energy-Efficient Cluster based Routing Schemes for Static and Mobile Sensor Networks

The theme of the thesis is centred around one important aspect of wireless sensor networks; the energy-efficiency.The limited energy source of the sensor nodes calls for design of energy-efficient routing protocols. The schemes for protocol design should try to minimize the number of communications among the nodes to save energy. Cluster based techniques were found energy-efficient. In this method clusters are formed and data from different nodes are collected under a cluster head belonging to each clusters and then forwarded it to the base station.Appropriate cluster head selection process and generation of desirable distribution of the clusters can reduce energy consumption of the network and prolong the network lifetime. In this work two such schemes were developed for static wireless sensor networks.In the first scheme, the energy wastage due to cluster rebuilding incorporating all the nodes were addressed. A tree based scheme is presented to alleviate this problem by rebuilding only sub clusters of the network. An analytical model of energy consumption of proposed scheme is developed and the scheme is compared with existing cluster based scheme. The simulation study proved the energy savings observed.The second scheme concentrated to build load-balanced energy efficient clusters to prolong the lifetime of the network. A voting based approach to utilise the neighbor node information in the cluster head selection process is proposed. The number of nodes joining a cluster is restricted to have equal sized optimum clusters. Multi-hop communication among the cluster heads is also introduced to reduce the energy consumption. The simulation study has shown that the scheme results in balanced clusters and the network achieves reduction in energy consumption.The main conclusion from the study was the routing scheme should pay attention on successful data delivery from node to base station in addition to the energy-efficiency. The cluster based protocols are extended from static scenario to mobile scenario by various authors. None of the proposals addresses cluster head election appropriately in view of mobility. An elegant scheme for electing cluster heads is presented to meet the challenge of handling cluster durability when all the nodes in the network are moving. The scheme has been simulated and compared with a similar approach.The proliferation of sensor networks enables users with large set of sensor information to utilise them in various applications. The sensor network programming is inherently difficult due to various reasons. There must be an elegant way to collect the data gathered by sensor networks with out worrying about the underlying structure of the network. The final work presented addresses a way to collect data from a sensor network and present it to the users in a flexible way.A service oriented architecture based application is built and data collection task is presented as a web service. This will enable composition of sensor data from different sensor networks to build interesting applications. The main objective of the thesis was to design energy-efficient routing schemes for both static as well as mobile sensor networks. A progressive approach was followed to achieve this goal.

Spectroscopic and photoluminescence studies on optically transparent magnetic nanocomposites based on sol–gel glass: Fe3O4

Sol–gel glasses with Fe3O4 nanoparticles having particle sizes laying in the range 10–20 nm were encapsulated in the porous network of silica resulting in nanocomposites having both optical and magnetic properties. Spectroscopic and photoluminescence studies indicated that Fe3O4 nanocrystals are embedded in the silica matrix with no strong Si–O–Fe bonding. The composites exhibited a blue luminescence. The optical absorption edge of the composites red shifted with increasing concentration of Fe3O4 in the silica matrix. There is no obvious shift in the position of the luminescence peak with the concentration of Fe3O4 except that the intensity of the peak is decreased. The unique combinations of magnetic and optical properties are appealing for magneto–optical applications.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Development of a Biometric Personal Authentication System Based on Fingerprint and Speech

Biometrics deals with the physiological and behavioral characteristics of an individual to establish identity. Fingerprint based authentication is the most advanced biometric authentication technology. The minutiae based fingerprint identification method offer reasonable identification rate. The feature minutiae map consists of about 70-100 minutia points and matching accuracy is dropping down while the size of database is growing up. Hence it is inevitable to make the size of the fingerprint feature code to be as smaller as possible so that identification may be much easier. In this research, a novel global singularity based fingerprint representation is proposed. Fingerprint baseline, which is the line between distal and intermediate phalangeal joint line in the fingerprint, is taken as the reference line. A polygon is formed with the singularities and the fingerprint baseline. The feature vectors are the polygonal angle, sides, area, type and the ridge counts in between the singularities. 100% recognition rate is achieved in this method. The method is compared with the conventional minutiae based recognition method in terms of computation time, receiver operator characteristics (ROC) and the feature vector length. Speech is a behavioural biometric modality and can be used for identification of a speaker. In this work, MFCC of text dependant speeches are computed and clustered using k-means algorithm. A backpropagation based Artificial Neural Network is trained to identify the clustered speech code. The performance of the neural network classifier is compared with the VQ based Euclidean minimum classifier. Biometric systems that use a single modality are usually affected by problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multifinger feature level fusion based fingerprint recognition is developed and the performances are measured in terms of the ROC curve. Score level fusion of fingerprint and speech based recognition system is done and 100% accuracy is achieved for a considerable range of matching threshold

A virtual backbone based approach for cooperative caching in mobile ad hoc networks

Cache look up is an integral part of cooperative caching in ad hoc networks. In this paper, we discuss a cooperative caching architecture with a distributed cache look up protocol which relies on a virtual backbone for locating and accessing data within a cooperate cache. Our proposal consists of two phases: (i) formation of a virtual backbone and (ii) the cache look up phase. The nodes in a Connected Dominating Set (CDS) form the virtual backbone. The cache look up protocol makes use of the nodes in the virtual backbone for effective data dissemination and discovery. The idea in this scheme is to reduce the number of nodes involved in cache look up process, by constructing a CDS that contains a small number of nodes, still having full coverage of the network. We evaluated the effect of various parameter settings on the performance metrics such as message overhead, cache hit ratio and average query delay. Compared to the previous schemes the proposed scheme not only reduces message overhead, but also improves the cache hit ratio and reduces the average delay

RMAC-M: Extending the R-MAC Protocol for an Energy Efficient, Delay Tolerant Underwater Acoustic Sensor Network application with a mobile data mule node

One of the major applications of underwater acoustic sensor networks (UWASN) is ocean environment monitoring. Employing data mules is an energy efficient way of data collection from the underwater sensor nodes in such a network. A data mule node such as an autonomous underwater vehicle (AUV) periodically visits the stationary nodes to download data. By conserving the power required for data transmission over long distances to a remote data sink, this approach extends the network life time. In this paper we propose a new MAC protocol to support a single mobile data mule node to collect the data sensed by the sensor nodes in periodic runs through the network. In this approach, the nodes need to perform only short distance, single hop transmission to the data mule. The protocol design discussed in this paper is motivated to support such an application. The proposed protocol is a hybrid protocol, which employs a combination of schedule based access among the stationary nodes along with handshake based access to support mobile data mules. The new protocol, RMAC-M is developed as an extension to the energy efficient MAC protocol R-MAC by extending the slot time of R-MAC to include a contention part for a hand shake based data transfer. The mobile node makes use of a beacon to signal its presence to all the nearby nodes, which can then hand-shake with the mobile node for data transfer. Simulation results show that the new protocol provides efficient support for a mobile data mule node while preserving the advantages of R-MAC such as energy efficiency and fairness.

Energy Aware Cluster-based Multihop Routing Protocol for Sensor Networks

Clustering combined with multihop communication is a promising solution to cope with the energy requirements of large scale Wireless Sensor Networks. In this work, a new cluster based routing protocol referred to as Energy Aware Cluster-based Multihop (EACM) Routing Protocol is introduced, with multihop communication between cluster heads for transmitting messages to the base station and direct communication within clusters. We propose EACM with both static and dynamic clustering. The network is partitioned into near optimal load balanced clusters by using a voting technique, which ensures that the suitability of a node to become a cluster head is determined by all its neighbors. Results show that the new protocol performs better than LEACH on network lifetime and energy dissipation

An efficient scheme for the evaluation of coding performance of high speed links incorporating joint error behaviour based on Monte Carlo Method

While channel coding is a standard method of improving a system’s energy efficiency in digital communications, its practice does not extend to high-speed links. Increasing demands in network speeds are placing a large burden on the energy efficiency of high-speed links and render the benefit of channel coding for these systems a timely subject. The low error rates of interest and the presence of residual intersymbol interference (ISI) caused by hardware constraints impede the analysis and simulation of coded high-speed links. Focusing on the residual ISI and combined noise as the dominant error mechanisms, this paper analyses error correlation through concepts of error region, channel signature, and correlation distance. This framework provides a deeper insight into joint error behaviours in high-speed links, extends the range of statistical simulation for coded high-speed links, and provides a case against the use of biased Monte Carlo methods in this setting

Offline Handwritten Malayalam Character Recognition Based on Chain Code Histogram

Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Spectroscopic and photoluminescence studies on optically transparent magnetic nanocomposites based on sol–gel glass: Fe3O4

Sol–gel glasses with Fe3O4 nanoparticles having particle sizes laying in the range 10–20 nm were encapsulated in the porous network of silica resulting in nanocomposites having both optical and magnetic properties. Spectroscopic and photoluminescence studies indicated that Fe3O4 nanocrystals are embedded in the silica matrix with no strong Si–O–Fe bonding. The composites exhibited a blue luminescence. The optical absorption edge of the composites red shifted with increasing concentration of Fe3O4 in the silica matrix. There is no obvious shift in the position of the luminescence peak with the concentration of Fe3O4 except that the intensity of the peak is decreased. The unique combinations of magnetic and optical properties are appealing for magneto–optical applications.

Classification of Mammograms and DW T based Detection of Microcalcification

Cancer treatment is most effective when it is detected early and the progress in treatment will be closely related to the ability to reduce the proportion of misses in the cancer detection task. The effectiveness of algorithms for detecting cancers can be greatly increased if these algorithms work synergistically with those for characterizing normal mammograms. This research work combines computerized image analysis techniques and neural networks to separate out some fraction of the normal mammograms with extremely high reliability, based on normal tissue identification and removal. The presence of clustered microcalcifications is one of the most important and sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously abnormal, to detect possible microcalcifications. The goal of this work is to improve the detection performance and throughput of screening-mammography, thus providing a ‘second opinion ‘ to the radiologists. The state-of- the- art DWT computation algorithms are not suitable for practical applications with memory and delay constraints, as it is not a block transfonn. Hence in this work, the development of a Block DWT (BDWT) computational structure having low processing memory requirement has also been taken up.

Air-gap based vertical cavity micro-opto-electro-mechanical Fabry-Perot filters

Mikrooptische Filter sind heutzutage in vielen Bereichen in der Telekommunikation unersetzlich. Wichtige Einsatzgebiete sind aber auch spektroskopische Systeme in der Medizin-, Prozess- und Umwelttechnik. Diese Arbeit befasst sich mit der Technologieentwicklung und Herstellung von luftspaltbasierenden, vertikal auf einem Substrat angeordneten, oberflächenmikromechanisch hergestellten Fabry-Perot-Filtern. Es werden zwei verschiedene Filtervarianten, basierend auf zwei verschiedenen Materialsystemen, ausführlich untersucht. Zum einen handelt es sich dabei um die Weiterentwicklung von kontinuierlich mikromechanisch durchstimmbaren InP / Luftspaltfiltern; zum anderen werden neuartige, kostengünstige Siliziumnitrid / Luftspaltfilter wissenschaftlich behandelt. Der Inhalt der Arbeit ist so gegliedert, dass nach einer Einleitung mit Vergleichen zu Arbeiten und Ergebnissen anderer Forschergruppen weltweit, zunächst einige theoretische Grundlagen zur Berechnung der spektralen Reflektivität und Transmission von beliebigen optischen Schichtanordnungen aufgezeigt werden. Auß erdem wird ein kurzer theoretischer Ü berblick zu wichtigen Eigenschaften von Fabry-Perot-Filtern sowie der Möglichkeit einer mikromechanischen Durchstimmbarkeit gegeben. Daran anschließ end folgt ein Kapitel, welches sich den grundlegenden technologischen Aspekten der Herstellung von luftspaltbasierenden Filtern widmet. Es wird ein Zusammenhang zu wichtigen Referenzarbeiten hergestellt, auf denen diverse Weiterentwicklungen dieser Arbeit basieren. Die beiden folgenden Kapitel erläutern dann ausführlich das Design, die Herstellung und die Charakterisierung der beiden oben erwähnten Filtervarianten. Abgesehen von der vorangehenden Epitaxie von InP / GaInAs Schichten, ist die Herstellung der InP / Luftspaltfilter komplett im Institut durchgeführt worden. Die Herstellungsschritte sind ausführlich in der Arbeit erläutert, wobei ein Schwerpunktthema das trockenchemische Ä tzen von InP sowie GaInAs, welches als Opferschichtmaterial für die Herstellung der Luftspalte genutzt wurde, behandelt. Im Verlauf der wissenschaftlichen Arbeit konnten sehr wichtige technische Verbesserungen entwickelt und eingesetzt werden, welche zu einer effizienteren technologischen Herstellung der Filter führten und in der vorliegenden Niederschrift ausführlich dokumentiert sind. Die hergestellten, für einen Einsatz in der optischen Telekommunikation entworfenen, elektrostatisch aktuierbaren Filter sind aus zwei luftspaltbasierenden Braggspiegeln aufgebaut, welche wiederum jeweils 3 InP-Schichten von (je nach Design) 357nm bzw. 367nm Dicke aufweisen. Die Filter bestehen aus im definierten Abstand parallel übereinander angeordneten Membranen, die über Verbindungsbrücken unterschiedlicher Anzahl und Länge an Haltepfosten befestigt sind. Da die mit 357nm bzw. 367nm vergleichsweise sehr dünnen Schichten freitragende Konstrukte mit bis zu 140 nm Länge bilden, aber trotzdem Positionsgenauigkeiten im nm-Bereich einhalten müssen, handelt es sich hierbei um sehr anspruchsvolle mikromechanische Bauelemente. Um den Einfluss der zahlreichen geometrischen Strukturparameter studieren zu können, wurden verschiedene laterale Filterdesigns implementiert. Mit den realisierten Filter konnte ein enorm weiter spektraler Abstimmbereich erzielt werden. Je nach lateralem Design wurden internationale Bestwerte für durchstimmbare Fabry-Perot-Filter von mehr als 140nm erreicht. Die Abstimmung konnte dabei kontinuierlich mit einer angelegten Spannung von nur wenigen Volt durchgeführt werden. Im Vergleich zu früher berichteten Ergebnissen konnten damit sowohl die Wellenlängenabstimmung als auch die dafür benötigte Abstimmungsspannung signifikant verbessert werden. Durch den hohen Brechungsindexkontrast und die geringe Schichtdicke zeigen die Filter ein vorteilhaftes, extrem weites Stopband in der Größ enordnung um 550nm. Die gewählten, sehr kurzen Kavitätslängen ermöglichen einen freien Spektralbereich des Filters welcher ebenfalls in diesen Größ enordnungen liegt, so dass ein weiter spektraler Einsatzbereich ermöglicht wird. Während der Arbeit zeigte sich, dass Verspannungen in den freitragenden InPSchichten die Funktionsweise der mikrooptischen Filter stark beeinflussen bzw. behindern. Insbesondere eine Unterätzung der Haltepfosten und die daraus resultierende Verbiegung der Ecken an denen sich die Verbindungsbrücken befinden, führte zu enormen vertikalen Membranverschiebungen, welche die Filtereigenschaften verändern. Um optimale Ergebnisse zu erreichen, muss eine weitere Verbesserung der Epitaxie erfolgen. Jedoch konnten durch den zusätzlichen Einsatz einer speziellen Schutzmaske die Unterätzung der Haltepfosten und damit starke vertikale Verformungen reduziert werden. Die aus der Verspannung resultierenden Verformungen und die Reaktion einzelner freistehender InP Schichten auf eine angelegte Gleich- oder Wechselspannung wurde detailliert untersucht. Mittels Weisslichtinterferometrie wurden lateral identische Strukturen verglichen, die aus unterschiedlich dicken InP-Schichten (357nm bzw. 1065nm) bestehen. Einen weiteren Hauptteil der Arbeit stellen Siliziumnitrid / Luftspaltfilter dar, welche auf einem neuen, im Rahmen dieser Dissertation entwickelten, technologischen Ansatz basieren. Die Filter bestehen aus zwei Braggspiegeln, die jeweils aus fünf 590nm dicken, freistehenden Siliziumnitridschichten aufgebaut sind und einem Abstand von 390nm untereinander aufweisen. Die Filter wurden auf Glassubstraten hergestellt. Der Herstellungsprozess ist jedoch auch mit vielen anderen Materialien oder Prozessen kompatibel, so dass z.B. eine Integration mit anderen Bauelemente relativ leicht möglich ist. Die Prozesse dieser ebenfalls oberflächenmikromechanisch hergestellten Filter wurden konsequent auf niedrige Herstellungskosten optimiert. Als Opferschichtmaterial wurde hier amorph abgeschiedenes Silizium verwendet. Der Herstellungsprozess beinhaltet die Abscheidung verspannungsoptimierter Schichten (Silizium und Siliziumnitrid) mittels PECVD, die laterale Strukturierung per reaktiven Ionenätzen mit den Gasen SF6 / CHF3 / Ar sowie Fotolack als Maske, die nasschemische Unterätzung der Opferschichten mittels KOH und das Kritisch-Punkt-Trocken der Proben. Die Ergebnisse der optischen Charakterisierung der Filter zeigen eine hohe Ü bereinstimmung zwischen den experimentell ermittelten Daten und den korrespondierenden theoretischen Modellrechnungen. Weisslichtinterferometermessungen der freigeätzten Strukturen zeigen ebene Filterschichten und bestätigen die hohe vertikale Positioniergenauigkeit, die mit diesem technologischen Ansatz erreicht werden kann.

Semantic network analysis of ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.