Experimental Saltwater Intrusion in Coastal aquifers Using Automated Image Analysis: Applications to Homogeneous Aquifers.

This paper presents the applications of a novel methodology to quantify saltwater intrusion parameters in laboratory-scale experiments. The methodology uses an automated image analysis procedure, minimizing manual inputs and the subsequent systematic errors that can be introduced. This allowed the quantification of the width of the mixing zone which is difficult to measure in experimental methods that are based on visual observations. Glass beads of different grain sizes were tested for both steady-state and transient conditions. The transient results showed good correlation between experimental and numerical intrusion rates. The experimental intrusion rates revealed that the saltwater wedge reached a steady state condition sooner while receding than advancing. The hydrodynamics of the experimental mixing zone exhibited similar
traits; a greater increase in the width of the mixing zone was observed in the receding saltwater wedge, which indicates faster fluid velocities and higher dispersion. The angle of intrusion analysis revealed the formation of a volume of diluted saltwater at the toe position when the saltwater wedge is prompted to recede. In addition, results of different physical repeats of the experiment produced an average coefficient of variation less than 0.18 of the measured toe length and width of the mixing zone.

Contextual Intrusion Alerts for SCADA Networks

The complexity of modern SCADA networks and their associated cyber-attacks requires an expressive but flexible manner for representing both domain knowledge and collected intrusion alerts with the ability to integrate them for enhanced analytical capabilities and better understanding of attacks. This paper proposes an ontology-based approach for contextualized intrusion alerts in SCADA networks. In this approach, three security ontologies were developed to represent and store information on intrusion alerts, Modbus communications, and Modbus attack descriptions. This information is correlated into enriched intrusion alerts using simple ontology logic rules written in Semantic Query-Enhanced Web Rules (SQWRL). The contextualized alerts give analysts the means to better understand evolving attacks and to uncover the semantic relationships between sequences of individual attack events. The proposed system is illustrated by two use case scenarios.

Modelling the Minho river plume intrusion into the rias baixas

The Minho River, situated 30 km south of the Rias Baixas is the most important freshwater source flowing into the Western Galician Coast (NW of the Iberian Peninsula). This discharge is important to determine the hydrological patterns adjacent to its mouth, particularly close to the Galician coastal region. The buoyancy generated by the Minho plume can flood the Rias Baixas for long periods, reversing the normal estuarine density gradients. Thus, it becomes important to analyse its dynamics as well as the thermohaline patterns of the areas affected by the freshwater spreading. Thus, the main aim of this work was to study the propagation of the Minho estuarine plume to the Rias Baixas, establishing the conditions in which this plume affects the circulation and hydrographic features of these coastal systems, through the development and application of the numerical model MOHID. For this purpose, the hydrographic features of the Rias Baixas mouths were studied. It was observed that at the northern mouths, due to their shallowness, the heat fluxes between the atmosphere and ocean are the major forcing, influencing the water temperature, while at the southern mouths the influence of the upwelling events and the Minho River discharge were more frequent. The salinity increases from south to north, revealing that the observed low values may be caused by the Minho River freshwater discharge. An assessment of wind data along the Galician coast was carried out, in order to evaluate the applicability of the study to the dispersal of the Minho estuarine plume. Firstly, a comparative analysis between winds obtained from land meteorological stations and offshore QuikSCAT satellite were performed. This comparison revealed that satellite data constitute a good approach to study wind induced coastal phenomena. However, since the numerical model MOHID requires wind data with high spatial and temporal resolution close to the coast, results of the forecasted model WRF were added to the previous study. The analyses revealed that the WRF model data is a consistent tool to obtain representative wind data near the coast, showing good results when comparing with in situ wind observations from oceanographic buoys. To study the influence of the Minho buoyant discharge influence on the Rias Baixas, a set of three one-way nested models was developed and implemented, using the numerical model MOHID. The first model domain is a barotropic model and includes the whole Iberian Peninsula coast. The second and third domains are baroclinic models, where the second domain is a coarse representation of the Rias Baixas and adjacent coastal area, while the third includes the same area with a higher resolution. A bi-dimensional model was also implemented in the Minho estuary, in order to quantify the flow (and its properties) that the estuary injects into the ocean. The chosen period for the Minho estuarine plume propagation validation was the spring of 1998, since a high Minho River discharge was reported, as well as favourable wind patterns to advect the estuarine plume towards the Rias Baixas, and there was field data available to compare with the model predictions. The obtained results show that the adopted nesting methodology was successful implemented. Model predictions reproduce accurately the hydrodynamics and thermohaline patterns on the Minho estuary and Rias Baixas. The importance of the Minho river discharge and the wind forcing in the event of May 1998 was also studied. The model results showed that a continuous moderate Minho River discharge combined with southerly winds is enough to reverse the Rias Baixas circulation pattern, reducing the importance of the occurrence of specific events of high runoff values. The conditions in which the estuarine plume Minho affects circulation and hydrography of the Rias Baixas were evaluated. The numerical results revealed that the Minho estuarine plume responds rapidly to wind variations and is also influenced by the bathymetry and morphology of the coastline. Without wind forcing, the plume expands offshore, creating a bulge in front of the river mouth. When the wind blows southwards, the main feature is the offshore extension of the plume. Otherwise, northward wind spreads the river plume towards the Rias Baixas. The plume is confined close to the coast, reaching the Rias Baixas after 1.5 days. However, for Minho River discharges higher than 800 m3 s-1, the Minho estuarine plume reverses the circulation patterns in the Rias Baixas. It was also observed that the wind stress and Minho River discharge are the most important factors influencing the size and shape of the Minho estuarine plume. Under the same conditions, the water exchange between Rias Baixas was analysed following the trajectories particles released close to the Minho River mouth. Over 5 days, under Minho River discharges higher than 2100 m3 s-1 combined with southerly winds of 6 m s-1, an intense water exchange between Rias was observed. However, only 20% of the particles found in Ria de Pontevedra come directly from the Minho River. In summary, the model application developed in this study contributed to the characterization and understanding of the influence of the Minho River on the Rias Baixas circulation and hydrography, highlighting that this methodology can be replicated to other coastal systems.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

The tectonically controlled emplacement of a vertically sheeted gabbro-pyroxenite intrusion: Feeder-zone of an ocean-island volcano (Fuerteventura, Canary Islands)

The Miocene PX1 gabbro-pyroxenite pluton, Fuerteventura, Canary Islands, is a 3.5 x 5.5 km shallow-level intrusion (0.15-0.2 GPa and 1100-1120 degrees C), interpreted as the feeder-zone to an ocean-island volcano. It displays a vertical magmatic banding expressed in five 50 to 100 metre-wide NNE-SSW trending alkaline gabbro sequences alternating with pyroxenites. This emplacement geometry was controlled by brittle to ductile shear zones, generated by a regional E-W extensional tectonic setting that affected Fuerteventura during the Miocene. At a smaller scale, the PX1 gabbro and pyroxenite bands consist of metre-thick differentiation units, which suggest emplacement by periodic injection of magma pulses as vertical dykes that amalgamated, similarly to a sub-volcanic sheeted dyke complex. Individual dykes underwent internal differentiation following a solidification front parallel to the dyke edges. This solidification front may have been favoured by a significant lateral/horizontal thermal gradient, expressed by the vertical banding in the gabbros, the fractionation asymmetry within individual dykes and the migmatisation of the wall rocks. Pyroxenitic layers result from the fractionation and accumulation of clinopyroxene +/- olivine +/- plagioclase crystals from a mildly alkaline basaltic liquid. They are interpreted as truncated differentiation sequences, from which residual melts were extracted at various stages of their chemical evolution by subsequent dyke intrusions, either next to or within the crystallising unit. Compaction and squeezing of the crystal mush is ascribed to the incoming and inflating magma pulses. The expelled interstitial liquid was likely collected and erupted along with the magma flowing through the newly injected dykes. Clinopyroxene mineral orientation - as evidenced by EBSD and micro X-ray tomography investigations - displays a marked pure-shear component, supporting the interpretation of the role of compaction in the generation of the pyroxenites. Conversely, gabbro sequences underwent minor melt extraction and are believed to represent crystallised coalesced magma batches emplaced at lower rates at the end of eruptive cycles. Clinopyroxene orientations in gabbros record a simple shear component suggesting syn-magmatic deformation parallel to observed NNE-SSW trending shear zones induced by the regional tensional stress field. This emplacement model implies a crystallisation time of 1 to 5 years for individual dykes, consistent with PX1 emplacement over less than 0.5 My. A minimum amount of approximately 150 km(3) of magma is needed to generate the pluton, part of it having been erupted through the Central Volcanic Centre of Fuerteventura. If the regional extensional tectonic regime controls the PX1 feeder-zone initiation and overall geometry, rates and volumes of magma depend on other, source-related factors. High injection rates are likely to induce intrusion growth rates larger than could be accommodated by the regional extension. In this case, dyke intrusion by propagation of a weak tip, combined with the inability of magma to circulate through previously emplaced and crystallised dykes could result in an increase of non-lithostatic pressure on previously emplaced mushy dyke walls; thus generating strong pure-shear compaction within the pluton feeder-zone and interstitial melt expulsion. These compaction-dominated processes are recorded by the cumulitic pyroxenite bands. (C) 2010 Elsevier B.V. All rights reserved.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Petrography, geochemistry and structure of the Timmins area, Ontario

Regional structural analysis of the Timmins area indicates four major periods of tectonic deformation. The DI deformation is characterized by a series of isoclinal FI folds which are outlined in the study area by bedding, pillow tops and variolitic flows. The D2 deformation developed the Porcupine Syncline and refolded the Fl folds about a NE. axis. A pervasive S2 foliation developed during low grade (greenschist) regional metamorphism associated with the D2 deformation. The S2 foliation developed south of the Destor-Porcupine Break. The third phase of tectonic D3 deformation is recognized by the development of a S3 sub-horizontal crenulation cleavage which developed on the plane of the S2 foliation. No meso scopic folds are associated with this deformation. The 8 3 crenulation cleavage is observed south of the Destor-Porcupine Break. The D4 tectonic deformation is recorded as a subvertical S4 crenulation cleavage which developed on the plane of the S2 foliation and also offsets the S3 crenulation cleavage. Macroscopic F4 folds have refolded the F2 axial plane. No metamorphic recrystallization is associated with this deformation. The S4 crenulation cleavage is observed south of the Destor-Porcupine Break. Petrographic evidence indicates that the Timmins area has been subjected to pervasive regional low grade (greenschist) metamorphism which has recrystallized the original mineralogy. South of the study are~ the Donut Lake ultramafic lavas have been subjected to contact medium grade (amphibolite facies) metamorphism associated with the intrusion of the Peterlong Lake Complex. The Archean volcanic rocks of the Timmins area have been subdivided into komatiitic, tholeiitic and calcalkaline suites based on Zr, Ti0 2 and Ni. The three elements were used because of their r e lative immobility during subsequent metamorphic events. Geochemical observations in the Timmins area indicates that the composition of the Goose Lake and Donut Lake Formations are a series of peridotitic, pyroxenitic and basaltic komatiites. The Lower Schumacher Formation is a sequence of basaltic komatiites while the upper part of the Lower Schumacher Formation is an intercalated sequence of basaltic komatiites and low Ti0 2 tholeiites. The variolitic flows are felsic tholeiites in composition and geochemical evidenc e sugg ests that they developed as a n immiscible splitting of a tholeiitic magma. The Upper Schumacher Formation is a sequence of tholeiitic rocks dis p laying a mild iron enrichment. The Krist and Boomerang Formations are the felsic calc-alkaline rocks of the study area which are characteristically pyroclastic. The Redstone Fo rmation is dominantly a calc-alkali ne sequence of volcani c rocks whose minor mafic end me mbers exposed in 1t.he study hav e basaltic komatiitic compositions. Geochemical evidence sugges ts that the Keewatin-type se dimentary rocks have a composition similar to a quartz diorite or a granodiorite. Fi e l d obs ervations and petrographic evidence suggests that they were derived fr om a distal source and now repr esent i n part a turbidite sequence. The Timiskaming-type sedimentary rocks approach the c omp osi t ion of the felsic calc-alkaline rocks of the study area . The basal conglomerate in the study are a sugge s ts that th e uni t was derived fr om a proximal source. Petrographic and ge ochemical evidence suggests that the peridotitic and pyroxenitic komatiites originated as a 35-55% partial melt within the mantle, in excess of 100 Km. depth. The melt ros e as a diapir with the subsequent effusion of the ultramafic lavas, The basaltic komatiites and tholeiitic rocks originated in the mantle from lesser degrees of partial melting and fractionated in low pressure chambers. Geochemical evidence suggests a "genetic link" between the basaltic komatiites and tholeiites, The calc-alkaline rocks developed as a result of the increa.se In PO in the magma chamber. The felsic calcalkaline rocks are a late stage effusion possibly the last major volcanic eruptions in the area.

Salinity Intrusion and Seasonal Water Quality Variations in the Tidal Canals of Cochin

The study conducted on the salinity intrusion and seasonal water quality variations in the tidal canals of cochin. The main objectives are, salinity intrusion profile, water quality variation of the surface water of the canals,hierarchical utility of the water bodies and to understand the non-conservative components in the water body. The parameters monitored werepH,temperature,alkalinity,conductivity,DO(dissolvedoxygen),COD(chemical oxygen demand),BOD(biochemical oxygen demand0,chloride, total hardness, calcium hardness, dissolved phosphate, nitrate, total iron, sulphate, turbidity, total coliform and SUVA at 254nm. The tidal canals of GCDA were found to be creeks extending to the interior, canals inter connecting parts of the estuary or canals with seasonally broken segments. Based on utility the canals could be classified as: canals heavely polluted and very saline,canals polluted by urban waste , canals having fresh water for most part of the year and not much polluted, fresh water bodies heavily polluted. During the rainy months carbon fixation by plankton is nonexistent,and during the dry months Chitrapuzha becomes a sink of phosphate. The study indicated abiotic subrouts for dissolved phosphate and revealed the potential pitfalls in LOICZ modeling exercise on sewage ladentidal canals. It was also found that all canals except for the canals of West cochin and chittoorpuzha have fresh water for some part of the year. The water quality index in the durable fresh water stretches was found to be of below average category.

Numerical Feasibility Study for Treated Wastewater Recharge as a Tool to Impede Saltwater Intrusion in the Coastal Aquifer of Gaza – Palestine

The ongoing depletion of the coastal aquifer in the Gaza strip due to groundwater overexploitation has led to the process of seawater intrusion, which is continually becoming a serious problem in Gaza, as the seawater has further invaded into many sections along the coastal shoreline. As a first step to get a hold on the problem, the artificial neural network (ANN)-model has been applied as a new approach and an attractive tool to study and predict groundwater levels without applying physically based hydrologic parameters, and also for the purpose to improve the understanding of complex groundwater systems and which is able to show the effects of hydrologic, meteorological and anthropogenic impacts on the groundwater conditions. Prediction of the future behaviour of the seawater intrusion process in the Gaza aquifer is thus of crucial importance to safeguard the already scarce groundwater resources in the region. In this study the coupled three-dimensional groundwater flow and density-dependent solute transport model SEAWAT, as implemented in Visual MODFLOW, is applied to the Gaza coastal aquifer system to simulate the location and the dynamics of the saltwater–freshwater interface in the aquifer in the time period 2000-2010. A very good agreement between simulated and observed TDS salinities with a correlation coefficient of 0.902 and 0.883 for both steady-state and transient calibration is obtained. After successful calibration of the solute transport model, simulation of future management scenarios for the Gaza aquifer have been carried out, in order to get a more comprehensive view of the effects of the artificial recharge planned in the Gaza strip for some time on forestall, or even to remedy, the presently existing adverse aquifer conditions, namely, low groundwater heads and high salinity by the end of the target simulation period, year 2040. To that avail, numerous management scenarios schemes are examined to maintain the ground water system and to control the salinity distributions within the target period 2011-2040. In the first, pessimistic scenario, it is assumed that pumping from the aquifer continues to increase in the near future to meet the rising water demand, and that there is not further recharge to the aquifer than what is provided by natural precipitation. The second, optimistic scenario assumes that treated surficial wastewater can be used as a source of additional artificial recharge to the aquifer which, in principle, should not only lead to an increased sustainable yield of the latter, but could, in the best of all cases, revert even some of the adverse present-day conditions in the aquifer, i.e., seawater intrusion. This scenario has been done with three different cases which differ by the locations and the extensions of the injection-fields for the treated wastewater. The results obtained with the first (do-nothing) scenario indicate that there will be ongoing negative impacts on the aquifer, such as a higher propensity for strong seawater intrusion into the Gaza aquifer. This scenario illustrates that, compared with 2010 situation of the baseline model, at the end of simulation period, year 2040, the amount of saltwater intrusion into the coastal aquifer will be increased by about 35 %, whereas the salinity will be increased by 34 %. In contrast, all three cases of the second (artificial recharge) scenario group can partly revert the present seawater intrusion. From the water budget point of view, compared with the first (do nothing) scenario, for year 2040, the water added to the aquifer by artificial recharge will reduces the amount of water entering the aquifer by seawater intrusion by 81, 77and 72 %, for the three recharge cases, respectively. Meanwhile, the salinity in the Gaza aquifer will be decreased by 15, 32 and 26% for the three cases, respectively.

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.

Unwanted memories of assault: what intrusion characteristics are associated with PTSD?

Intrusive memories are common in the immediate aftermath of traumatic events, but neither their presence or frequency are good predictors of the persistence of posttraumatic stress disorder (PTSD). Two studies of assault survivors, a cross-sectional study (N = 81) and a 6-month prospective longitudinal study (N = 73), explored whether characteristics of the intrusive memories improve the prediction. Intrusion characteristics were assessed with an Intrusion Interview and an Intrusion Provocation Task. The distress caused by the intrusions, their "here and now" quality, and their lack of a context predicted PTSD severity. The presence of intrusive memories only explained 9% of the variance of PTSD severity at 6 months after assault. Among survivors with intrusions, intrusion frequency only explained 8% of the variance of PTSD symptom severity at 6 months. Nowness, distress and lack of context explained an additional 43% of the variance. These intrusion characteristics also predicted PTSD severity at 6 months over and above what could be predicted from PTSD diagnostic status at initial assessment. Further predictors of PTSD severity were rumination about. the intrusive memories, and the ease and persistence with which intrusive memories could be triggered by photographs depicting assaults. The results have implications for the early identification of trauma survivors at risk of chronic PTSD. (c) 2004 Elsevier Ltd. All rights reserved.

Unsupervised genetic algorithm deployed for intrusion detection

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machine-learning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.