Passive operating system fingerprinting based on multi-layered sub-signature matching scheme (MLSMS).

Rangkaian komputer merupakan dimensi yang penting dalam organisasi moden. Oleh itu, usaha memastikan rangkaian ini dapat berjalan pada prestasi puncak dianggap amat penting dalam organisasi ini. Computer networks become an important dimension of the modern organizations. Thus, keeping the computer networks running at the peak performance is considered as a crucial part for these organizations

Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living

Following the recent advances in technology and the growing use of mobile devices such as smartphones, several solutions may be developed to improve the quality of life of users in the context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g., accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver, which allow the acquisition of physical and physiological parameters for the recognition of different Activities of Daily Living (ADL) and the environments in which they are performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare tasks, based on the types of skills that people usually learn in early childhood, including feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping, watching TV, working, listening to music, cooking, eating and others. On the context of AAL, some individuals (henceforth called user or users) need particular assistance, either because the user has some sort of impairment, or because the user is old, or simply because users need/want to monitor their lifestyle. The research and development of systems that provide a particular assistance to people is increasing in many areas of application. In particular, in the future, the recognition of ADL will be an important element for the development of a personal digital life coach, providing assistance to different types of users. To support the recognition of ADL, the surrounding environments should be also recognized to increase the reliability of these systems. The main focus of this Thesis is the research on methods for the fusion and classification of the data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL in almost real-time, taking into account the large diversity of the capabilities and characteristics of the mobile devices available in the market. In order to achieve this objective, this Thesis started with the review of the existing methods and technologies to define the architecture and modules of the method for the identification of ADL. With this review and based on the knowledge acquired about the sensors available in off-the-shelf mobile devices, a set of tasks that may be reliably identified was defined as a basis for the remaining research and development to be carried out in this Thesis. This review also identified the main stages for the development of a new method for the identification of the ADL using the sensors available in off-the-shelf mobile devices; these stages are data acquisition, data processing, data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One of the challenges is related to the different types of data acquired from the different sensors, but other challenges were found, including the presence of environmental noise, the positioning of the mobile device during the daily activities, the limited capabilities of the mobile devices and others. Based on the acquired data, the processing was performed, implementing data cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of the research their implementation does not have influence in the results of the identification of the ADL and environments, as the features are extracted from a set of data acquired during a defined time interval and there are no missing values during this stage. The joint selection of the set of usable sensors and the identifiable set of tasks will then allow the development of a framework that, considering multi-sensor data fusion technologies and context awareness, in coordination with other information available from the user context, such as his/her agenda and the time of the day, will allow to establish a profile of the tasks that the user performs in a regular activity day. The classification method and the algorithm for the fusion of the features for the recognition of ADL and its environments needs to be deployed in a machine with some computational power, while the mobile device that will use the created framework, can perform the identification of the ADL using a much less computational power. Based on the results reported in the literature, the method chosen for the recognition of the ADL is composed by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron (MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural Networks (DNN). Data acquisition can be performed with standard methods. After the acquisition, the data must be processed at the data processing stage, which includes data cleaning and feature extraction methods. The data cleaning method used for motion and magnetic sensors is the low pass filter, in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform (FFT) was applied to extract the different frequencies. When the data is clean, several features are then extracted based on the types of sensors used, including the mean, standard deviation, variance, maximum value, minimum value and median of raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance and median of the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the five greatest distances between the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel- Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw data acquired from the microphone data; and the distance travelled calculated with the data acquired from the GPS receiver. After the extraction of the features, these will be grouped in different datasets for the application of the ANN methods and to discover the method and dataset that reports better results. The classification stage was incrementally developed, starting with the identification of the most common ADL (i.e., walking, running, going upstairs, going downstairs and standing activities) with motion and magnetic sensors. Next, the environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen, living room, hall, street and library. After the environments are recognized, and based on the different sets of sensors commonly available in the mobile devices, the data acquired from the motion and magnetic sensors were combined with the recognized environment in order to differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled, extracted from the GPS receiver data, allowing also to recognize the driving activity. After the implementation of the three classification methods with different numbers of iterations, datasets and remaining configurations in a machine with high processing capabilities, the reported results proved that the best method for the recognition of the most common ADL and activities without motion is the DNN method, but the best method for the recognition of environments is the FNN method with Backpropagation. Depending on the number of sensors used, this implementation reports a mean accuracy between 85.89% and 89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of environments, and equals to 100% for the recognition of activities without motion, reporting an overall accuracy between 85.89% and 92.00%. The last stage of this research work was the implementation of the structured framework for the mobile devices, verifying that the FNN method requires a high processing power for the recognition of environments and the results reported with the mobile application are lower than the results reported with the machine with high processing capabilities used. Thus, the DNN method was also implemented for the recognition of the environments with the mobile devices. Finally, the results reported with the mobile devices show an accuracy between 86.39% and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition of environments, and equal to 100% for the recognition of activities without motion, reporting an overall accuracy between 58.02% and 89.15%. Compared with the literature, the results returned by the implemented framework show only a residual improvement. However, the results reported in this research work comprehend the identification of more ADL than the ones described in other studies. The improvement in the recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum number of ADL and environments previously recognized was 13, while the number of ADL and environments recognized with the framework resulting from this research is 16. In conclusion, the framework developed has a mean improvement of 2.93% in the accuracy of the recognition for a larger number of ADL and environments than previously reported. In the future, the achievements reported by this PhD research may be considered as a start point of the development of a personal digital life coach, but the number of ADL and environments recognized by the framework should be increased and the experiments should be performed with different types of devices (i.e., smartphones and smartwatches), and the data imputation and other machine learning methods should be explored in order to attempt to increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro, giroscópio, magnetómetro, microfone e recetor de Sistema de Posicionamento Global (GPS), que permitem a aquisição de vários parâmetros físicos e fisiológicos para o reconhecimento de diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir escadas, dormir, ver televisão, trabalhar, ouvir música, cozinhar, comer, entre outras. No contexto de AVA, alguns indivíduos (comumente chamados de utilizadores) precisam de assistência particular, seja porque o utilizador tem algum tipo de deficiência, seja porque é idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o reconhecimento das AVD é uma parte importante para o desenvolvimento de um assistente pessoal digital, fornecendo uma assistência pessoal de baixo custo aos diferentes tipos de pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas. O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos dados adquiridos a partir dos sensores disponíveis nos dispositivos móveis, para o reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade das características dos dispositivos móveis disponíveis no mercado. Para atingir este objetivo, esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da literatura e com base no conhecimento adquirido sobre os sensores disponíveis nos dispositivos móveis disponíveis no mercado, um conjunto de tarefas que podem ser identificadas foi definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os principais conceitos para o desenvolvimento do novo método de identificação das AVD, utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de dados, imputação de dados, extração de características, fusão de dados e extração de resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram encontrados, sendo os mais relevantes o ruído ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos móveis. As diferentes características das pessoas podem igualmente influenciar a criação dos métodos, escolhendo pessoas com diferentes estilos de vida e características físicas para a aquisição e identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos, realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a extração de características, para iniciar a criação do novo método para o reconhecimento das AVD. Os métodos de imputação de dados foram excluídos da implementação, pois não iriam influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são utilizadas as características extraídas de um conjunto de dados adquiridos durante um intervalo de tempo definido. A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados adquiridos com múltiplos sensores em coordenação com outras informações relativas ao contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, após a criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para testes adicionais. Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de processamento de dados, que inclui os métodos de correção de dados e de extração de características. O método de correção de dados utilizado para sensores de movimento e magnéticos é o filtro passa-baixo de modo a reduzir o ruído, mas para os dados acústicos, a Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências. Após a correção dos dados, as diferentes características foram extraídas com base nos tipos de sensores usados, sendo a média, desvio padrão, variância, valor máximo, valor mínimo e mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os dados adquiridos pelo recetor de GPS. Após a extração das características, as mesmas são agrupadas em diferentes conjuntos de dados para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de características que reporta melhores resultados. O módulo de classificação de dados foi incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em seguida, os ambientes são identificados com dados de sensores acústicos, i.e., quarto, bar, sala de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes reconhecidos e os restantes sensores disponíveis nos dispositivos móveis, os dados adquiridos dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para diferenciar algumas atividades sem movimento (i.e., dormir e ver televisão), onde o número de atividades reconhecidas nesta fase aumenta com a fusão da distância percorrida, extraída a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir. Após a implementação dos três métodos de classificação com diferentes números de iterações, conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os resultados relatados provaram que o melhor método para o reconhecimento das atividades comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51% para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão global entre 85,89% e 92,00%. A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando que o método FNN requer um alto poder de processamento para o reconhecimento de ambientes e os resultados reportados com estes dispositivos são inferiores aos resultados reportados com a máquina com alta capacidade de processamento utilizada no desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o reconhecimento dos ambientes com os dispositivos móveis. Finalmente, os resultados relatados com os dispositivos móveis reportam uma exatidão entre 86,39% e 89,15% para o reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão geral entre 58,02% e 89,15%. Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais estudos disponíveis na literatura. A melhoria no reconhecimento das AVD com base na média das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos estudos disponíveis na literatura é 13, enquanto o número de AVD e ambientes reconhecidos com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93% na exatidão do reconhecimento num maior número de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto de partida para o desenvolvimento de um assistente digital pessoal, mas o número de ADL e ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser repetidas com diferentes tipos de dispositivos móveis (i.e., smartphones e smartwatches), e os métodos de imputação e outros métodos de classificação de dados devem ser explorados de modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e ambientes

Cooperative Localization in Mines Using Fingerprinting and Neural Networks

This work is a special investigation in the localization of users in underground and confined areas such as gold mines. It sheds light on the basic approaches that are used nowadays to estimate the position and track users using wireless technology. Localization or Geo-location in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery suffer severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting the channel impulse response fingerprints with reference to one wireless receiver and using an artificial neural network as the matching algorithm. In this work we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints allows us to create artificial neural networks that work separately or cooperatively using the same localization technique. In this approach, the received signals by the mobile at various distances are analysed and several components of each signal are extracted accordingly. The channel impulse response found at each position is unique to the position of the receiver. The parameters extracted from the CIR are the received signal strength, mean excess delay, root mean square, maximum excess delay, the number of multipath components, the total power of the received signal, the power of the first arrival and the delay of the first arrival. The use of multiple fingerprints from multiple references not only adds diversity to the set of inputs fed to the neural network but it also enhances the overall concept and makes it applicable in a multi-access environment. Localization is analyzed in the presence of two receivers using several position estimation procedures. The results showed that using two CIRs in a cooperative localization technique gives a position accuracy less than or equal to 1m for 90% of both trained and untrained neural networks. Another way of using cooperative intelligence is by using the time domain including tracking, probabilities and previous positions to the localization system. Estimating new positions based on previous positions recorded in history has a great improvement factor on the accuracy of the localization system where it showed an estimation error of less than 50cm for 90% of training data and 65cm for testing data. The details of those techniques and the estimation errors and graphs are fully presented and they show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points as well as using tracking improves significantly the accuracy, precision, scalability and the overall performance of the localization system

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Multimodal Approach for Malware Detection

Although malware detection is a very active area of research, few works were focused on using physical properties (e.g., power consumption) and multimodal features for malware detection. We designed an experimental testbed that allowed us to run samples of malware and non-malicious software applications and to collect power consumption, network traffic, and system logs data, and subsequently to extract dynamic behavioral-based features. We also extracted code-based static features of both malware and non-malicious software applications. These features were used for malware detection based on: feature level fusion using power consumption and network traffic data, feature level fusion using network traffic data and system logs, and multimodal feature level and decision level fusion. The contributions when using feature level fusion of power consumption and network traffic data are: (1) We focused on detecting real malware using the extracted dynamic behavioral features (both power-based and network traffic-based) and supervised machine learning algorithms, which has not been done by any of the prior works. (2) We ran a large number of machine learning experiments, which allowed us to identify the best performing learner, DC voltage rails that led to the best malware detection performance, and the subset of features that are the best predictors for malware detection. (3) The comparison of malware detection performance was done using a comprehensive set of metrics that reflect different aspects of the quality of malware detection. In the case of the feature level fusion using network traffic data and system logs, the contributions are: (1) Most of the previous works that have used network flows-based features have done classification of the network traffic, while our focus was on classifying the software running in a machine as malware and non-malicious software using the extracted dynamic behavioral features. (2) We experimented with different sizes of the training set (i.e., 90%, 75%, 50%, and 25% of the data) and found that smaller training sets produced very good classification results. This aspect of our work has a practical value because the manual labeling of the training set is a tedious and time consuming process. In this dissertation we present a multimodal deep learning neural network that integrates different modalities (i.e., power consumption, system logs, network traffic, and code-based static data) using decision level fusion. We evaluated the performance of each modality individually, when using feature level fusion, and when using decision level fusion. The contributions of our multimodal approach are as follow: (1) Collecting data from different modalities allowed us to develop a multimodal approach to malware detection, which has not been widely explored by prior works. Even more, none of the previous works compared the performance of feature level fusion with decision level fusion, which is explored in this dissertation. (2) We proposed a multimodal decision level fusion malware detection approach using a deep neural network and compared its performance with the performance of feature level fusion approaches based on deep neural network and standard supervised machine learning algorithms (i.e., Random Forest, J48, JRip, PART, Naive Bayes, and SMO)

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Program analysis for android security and reliability

The recent, widespread growth and adoption of mobile devices have revolutionized the way users interact with technology. As mobile apps have become increasingly prevalent, concerns regarding their security and reliability have gained significant attention. The ever-expanding mobile app ecosystem presents unique challenges in ensuring the protection of user data and maintaining app robustness. This dissertation expands the field of program analysis with techniques and abstractions tailored explicitly to enhancing Android security and reliability. This research introduces approaches for addressing critical issues related to sensitive information leakage, device and user fingerprinting, mobile medical score calculators, as well as termination-induced data loss. Through a series of comprehensive studies and employing novel approaches that combine static and dynamic analysis, this work provides valuable insights and practical solutions to the aforementioned challenges. In summary, this dissertation makes the following contributions: (1) precise identifier leak tracking via a novel algebraic representation of leak signatures, (2) identifier processing graphs (IPGs), an abstraction for extracting and subverting user-based and device-based fingerprinting schemes, (3) interval-based verification of medical score calculator correctness, and (4) identifying potential data losses caused by app termination

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

OIL SPILL ALONG THE TURKISH STRAITS SEA AREA; ACCIDENTS, ENVIRONMENTAL POLLUTION, SOCIO-ECONOMIC IMPACTS AND PROTECTION

The Turkish Straits Sea Area (TSSA) is a long water passage that is consisted of the Sea of Marmara, an inland sea within Turkey's borders, and two narrow straits connected to neighboring seas. With a strategic location between the Balkans and Anatolia, the Black Sea and the Mediterranean, and dominated by the continental climate, the region hosted many civilizations throughout the centuries. This makes the region among the busiest routes in the world, with sea traffic three times higher than that in the Suez Canal. The straits are the most difficult waterways to navigate and witnessed many hazardous and important collisions and accidents throughout history. In addition, this area has vital roles as a biological corridor and barrier among three distinctive marine realms. Therefore, the region is rather sensitive to damages of national and international maritime activities, which may cause severe environmental problems. This book addresses several key questions on a chapter basis, including historical accidents, background information on main dynamic restrictions, oil pollution, oil spill detection, and clean-up recoveries, its impacts on biological communities, socioeconomic aspects, and subjects with international agreements. This book will help readers, public, local and governmental authorities gain a deeper understanding of the status of the oil spill, mostly due to shipping accidents, and their related impacts along the TSSA, which needs precautionary measures to be protected.CONTENTS INTRODUCTION CHAPTER I - HISTORY OF ACCIDENTS AND REGULATIONS Remarkable Accidents at the Istanbul Strait Hasan Bora USLUER and Saim OĞUZÜLGEN …………………………………...... 3 History of Regulations before Republican Era along the Turkish Straits Sea Area Ali Umut ÜNAL …………………………………………………………………….. 16 Transition Regime in the Turkish Straits during the Republican Era Osman ARSLAN ……….……………………………………………………….……26 26 The Montreux Convention and Effects at Turkish Straits Oktay ÇETİN ………………………………………………………………….…….. 33 Evaluation of the Montreux Convention in the Light of Recent Problems Ayşenur TÜTÜNCÜ ………………………………………………………………… 44 A Historical View on Technical Developments on Ships and Effects of Turkish Straits Murat YAPICI ………………………………………………………………………. 55 CHAPTER II - GEOGRAPHY, BATHYMETRY AND HYDRO-METEOROLOGICAL CONDITIONS Geographic and Bathymetric Restrictions along the Turkish Straits Sea Area Bedri ALPAR, Hasan Bora USLUER and Şenol AYDIN ……………………..…… 61 Hydrodynamics and Modeling of Turkish Straits Serdar BEJİ and Tarkan ERDİK ………………………………………………….… 79 Wave Climate in the Turkish Sea of Marmara Tarkan ERDİK and Serdar BEJİ …………………………………………………..… 91 CHAPTER III - OIL POLLUTION, DETECTION AND RECOVERY Oil Pollution at Sea and Coast Following Major Accidents Selma ÜNLÜ ……………………………………………………………………….101 Forensic Fingerprinting in Oil-spill Source Identification at the Turkish Straits Sea Area Özlem ATEŞ DURU ……………………………………………………………… 121 xi Oil Spill Detection Using Remote Sensing Technologies-Synthetic Aperture Radar (SAR) İbrahim PAPİLA, Elif SERTEL, Şinasi KAYA and Cem GAZİOĞLU ……..……. 140 The Role of SAR Remote Sensing to Detect Oil Pollution and Emergency Intervention Saygın ABDIKAN, Çağlar BAYIK and Füsun BALIK ŞANLI ……….….……….. 157 Oil Spill Recovery and Clean-Up Techniques Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………… 176 Turkish Strait Sea Area, Contingency Planning, Regulations and Case Studies Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………... 188 Dispersant Response Method to Incidental Oil Pollution Dilek EDİGER, Leyla TOLUN and Fatma TELLİ KARAKOÇ ………………….... 205 CHAPTER IV - THE EFFECTS / IMPACTS OF OIL SPILL ON BIOLOGICAL COMMUNITIES – INCLUDING SAMPLING AND MONITORING Marine Microorganisms and Oil Spill Sibel ZEKİ and Pelin S. ÇİFTÇİ TÜRETKEN …………...………………………… 219 Estimated Effects of Oil Spill on the Phytoplankton Following “Volgoneft-248” Accident (Sea of Marmara) Seyfettin TAŞ ………………………………..…………………………………….... 229 Interactions between Zooplankton and Oil Spills: Lessons Learned from Global Accidents and a Proposal for Zooplankton Monitoring İ. Noyan YILMAZ and Melek İŞİNİBİLİR ……………………………………..….. 238 The Effects of Oil Spill on the Macrophytobenthic Communities Ergün TAŞKIN and Barış AKÇALI …………………………….…………….……. 244 Potential Impacts of Oil Spills on Macrozoobenthos in the Turkish Straits System Güley KURT-ŞAHİN …………………………………………………………….… 253 The Anticipated Effects of Oil Spill on Fish Populations in Case of an Accident along the Turkish Straits System – A review of Studies after Several Incidents from the World M. İdil ÖZ and Nazlı DEMİREL …………………………………………………….261 Estimated Impacts of an Oil Spill on Bird Populations along the Turkish Straits System Itri Levent ERKOL …………………………………………………………….…… 272 The Effect of Oil Spills on Cetaceans in the Turkish Straits System (TSS) Ayaka Amaha ÖZTÜRK ………………………………………………………….. 277 Changes in the Ichthyoplankton and Benthos Assemblages following Volgoneft-248 Oil Spill: Case Study Ahsen YÜKSEK and Yaprak GÜRKAN …………………………………….……. 280 Assessing the Initial and Temporal Effects of a Heavy Fuel Oil Spill on Benthic Fauna Yaprak GÜRKAN, Ahsen YÜKSEK ………………………………………..…….. 287 CHAPTER V - SOCIO-ECONOMIC ASPECTS Socio-economic Aspects of Oil Spill Özlem ATEŞ DURU and Serap İNCAZ ……………………………………….…… 301 Effects of Oil Spill on Human Health Türkan YURDUN ………………………………………………………………..…. 313 Crisis Management of Oil Spill, A Case Study: BP Gulf Mexico Oil Disaster Serap İNCAZ and Özlem ATEŞ DURU …………………………….………….……324 CHAPTER VI - CONVENTIONS RELATING TO PREVENTION OF OIL SPILL International Convention for the Prevention of Pollution of the Sea by Oil (OILPOL), 1954 and its Situation Related with Turkey Emre AKYÜZ, Metin ÇELİK and Ömer SÖNER …………………………...……... 334 International Convention for the Prevention of Pollution from Ships, 1973, as Modified by the Protocol of 1978 Relating Thereto and by the Protocol of 1997 (MARPOL) Özcan ARSLAN, Esma UFLAZ and Serap İNCAZ ………………………….……. 342 Applications of MARPOL Related with Oil Spill in Turkey Emre AKYÜZ, Özcan ASLAN and Serap İNCAZ ………………………………… 356 Ship Born Oil Pollution at the Turkish Straits Sea Area and MARPOL 73/78 Duygu ÜLKER and Sencer BALTAOĞLU………………………….…………….. 363 International Convention Relating to Intervention on the High Seas in Cases of Oil Pollution Casualties (INTERVENTION 1969) and its Applications Related with Oil Spill in Turkey Şebnem ERKEBAY ……………………………….……………………………….. 371 International Convention on Oil Pollution Preparedness, Response and Co-operation (OPRC) 1990 and its Applications Related with Oil Spill in Turkey Kadir ÇİÇEK ………………………………………………………………………. 381 Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances, 2000 (OPRC-HNS Protocol) and its Effects in Turkey Aydın ŞIHMANTEPE and Cihat AŞAN ……………….…………………………. 392 The International Convention on Salvage (SALVAGE) 1989 Related with Oil Spill in Turkey İrşad BAYIRHAN ……………………………………….………………..……….. 408 CHAPTER VII - CONVENTIONS COVERING LIABILITY AND COMPENSATION RELATED WITH OIL SPILL International Convention on Civil Liability for Oil Pollution Damage (CLC), 1969 and its Applications Serap İNCAZ and Pınar ÖZDEMİR ……………………………………..………… 416 1992 Protocol to the International Convention on the Establishment of an International Fund for Compensation for Oil Pollution Damage (FUND 1992) and its Applications Related with Oil Spill in Turkey Ali Umut ÜNAL and Hasan Bora USLUER …………………………….………… 424 International Convention on Liability and Compensation for Damage in Connection with the Carriage of Hazardous and Noxious Substances by Sea (HNS), 1996 (and its 2010 Protocol) and its Applications Related with Oil Spill in Turkey Bilun ELMACIOĞLU ……………………………………………………………… 437 Bunkering Incidents and Safety Practices in Turkey Fırat BOLAT, Pelin BOLAT and Serap İNCAZ …………………………………... 447 "Nairobi International Convention on the Removal of Wrecks 2007" and its Effects on Turkey Şafak Ümit DENİZ and Serap İNCAZ ……………………….……………………. 457