Road conditions monitoring using semantic segmentation of smartphone motion sensor data

Many studies and publications have been written about the use of moving object analysis to locate a specific item or replace a lost object in video sequences. Using semantic analysis, it could be challenging to pinpoint each meaning and follow the movement of moving objects. Some machine learning algorithms have turned to the right interpretation of photos or video recordings to communicate coherently. The technique converts visual patterns and features into visual language using dense and sparse optical flow algorithms. To semantically partition smartphone motion sensor data for any video categorization, using integrated bidirectional Long Short-Term Memory layers, this paper proposes a redesigned U-Net architecture. Experiments show that the proposed technique outperforms several existing semantic segmentation algorithms using z-axis accelerometer and z-axis gyroscope properties. The video sequence's numerous moving elements are synchronised with one another to follow the scenario. Also, the objective of this work is to assess the proposed model on roadways and other moving objects using five datasets (self-made dataset and the pothole600 dataset). After looking at the map or tracking an object, the results should be given together with the diagnosis of the moving object and its synchronization with video clips. The suggested model's goals were developed using a machine learning method that combines the validity of the results with the precision of finding the necessary moving parts. Python 3.7 platforms were used to complete the project since they are user-friendly and highly efficient platforms

The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach

The increase in the number of mobile devices that use the Android operating system has attracted the attention of cybercriminals who want to disrupt or gain unauthorized access to them through malware infections. To prevent such malware, cybersecurity experts and researchers require datasets of malware samples that most available antivirus software programs cannot detect. However, researchers have infrequently discussed how to identify evolving Android malware characteristics from different sources. In this paper, we analyze a wide variety of Android malware datasets to determine more discriminative features such as permissions and intents. We then apply machine-learning techniques on collected samples of different datasets based on the acquired features’ similarity. We perform random sampling on each cluster of collected datasets to check the antivirus software’s capability to detect the sample. We also discuss some common pitfalls in selecting datasets. Our findings benefit firms by acting as an exhaustive source of information about leading Android malware datasets

Development of a classification algorithm for vehicle impacts: an anomaly detection approach

Dissertação de mestrado em Engenharia InformáticaIn the past decade, Machine Learning has been heavily applied to automobile industry solutions, the most promising being development of autonomous vehicles. New mobility services are available today as alternatives to owning a car, like ride hailing and carsharing. High costs associated with the maintenance of the vehicle and the reduced rate of vehicle use throughout the day are some of the factors for the popularity of these services. Car-sharing is self-service mode of transport that provides its members with access to a fleet of vehicles parked in various locations throughout a city. Damages are expected to happen when vehicles are used and the required repair implies costs to fleet operators. Systems able to detect these damages will promote a better use of these vehicles by vehicle users. Vehicle damages result from impacts with other objects, for instance, other vehicles or structures of any kind and these impacts inflict deformations to the vehicle exterior structure. Most of these impacts can be perceived or detected by the forces involved as result of the impact. Anomaly Detection is a technique applicable in a variety of domains, such as intrusion detection, fraud detection, event detection in sensor network or detection ecosystem disturbances. The objective of this thesis is the study and development of a semi-supervised intelligent system for detection and classification of vehicle impacts with an Anomaly Detection approach, using the accelerometer data, and following a strategy that would allow exploring a Machine Learning cycle. This thesis was developed under an internship in the company Bosch Car Multimedia S.A, located in Braga.Na última década, Machine Learning tem sido extensamente utilizado em soluções na indústria automóvel, o mais promissor sendo o desenvolvimento de veículos com condução autônoma. Novos serviços de mobilidade estão disponíveis hoje como alternativas à posse de um carro, como ride hailing ou car-sharing. Os elevados custos associados à manutenção do veículo e a sua reduzida taxa de utilização ao longo do dia são alguns dos fatores que contribuem para a popularidade destes serviços. Car-sharing é um modo de transporte self-service que fornece aos seus membros acesso a uma frota de veículos estacionados em vários locais duma cidade. Danos são espectáveis de ocorrer quando os veículos são usados e a reparação necessária implica custos para os operadores da frota. Sistemas capazes de detectar esses danos irão promover um melhor aproveitamento desses veículos pelos utilizadores dos veículos. Os danos de veículos resultam de impactos com outros objetos como, por exemplo, outros veículos ou estruturas e esses impactos provocam deformações na estrutura externa do veículo. A maioria desses impactos podem ser compreendidos ou detetados pelas forças envolvidas do resultado do impacto. Anomaly Detection é uma técnica aplicável em uma variedade de domínios, como deteção de intrusões, deteção de fraude, deteção de eventos numa rede de sensores ou deteção de distúrbios no ecossistema. O objetivo desta dissertação foi o estudo e desenvolvimento de um sistema inteligente semi-supervisionado para detecção e classificação de impactos de veículos a partir de uma abordagem de Anomaly Detection, utilizando os dados de acelerómetro, e seguindo uma estratégia que permitisse explorar um ciclo de Machine Learning. Esta dissertação foi desenvolvida no âmbito de um estágio na empresa Bosch Car Multimedia S.A, situada em Braga

The Feasibility of Using Behavioural Profiling Technique for Mitigating Insider Threats: Review

Insider threat has become a serious issue to the many organizations. Various companies are increasingly deploying many information technologies to prevent unauthorized access to getting inside their system. Biometrics approaches have some techniques that contribute towards controlling the point of entry. However, these methods mainly are not able to continuously validate the users reliability. In contrast behavioral profiling is one of the biometrics technologies but it focusing on the activities of the users during using the system and comparing that with a previous history. This paper presents a comprehensive analysis, literature review and limitations on behavioral profiling approach and to what extent that can be used for mitigating insider misuse