The Experts below are selected from a list of 1845444 Experts worldwide ranked by ideXlab platform
L. Guerra Rosa - One of the best experts on this subject based on the ideXlab platform.
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Weibull Statistical Analysis of granite bending strength
Rock Mechanics and Rock Engineering, 2008Co-Authors: P. M. Amaral, J. Cruz Fernandes, L. Guerra RosaAbstract:This paper describes and discusses the adequacy of Weibull Statistical Analysis to analyse the bending strength of granite. The experimental results show that strength variability is related with a specific origin of failure. This conclusion is based on analysing the influence of the surface condition (extrinsic defects) on the bending strength results treated by the Weibull statistics. The conclusions drawn from this study have been validated by analysing the results of the critical flaw dimension estimated by applying the linear elastic fracture mechanics (LEFM) formulae. Results obtained from fractographic examination also have been used to describe the location of the origin of the fracture and understand the distribution of defects; i.e., there is a unimodal distribution of defects (intrinsic defects), despite the fact that some outlier values are normally observed in the fractured surfaces.
Amir Hussain - One of the best experts on this subject based on the ideXlab platform.
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a novel Statistical Analysis and autoencoder driven intelligent intrusion detection approach
Neurocomputing, 2020Co-Authors: Cosimo Ieracitano, Ahsan Adeel, Francesco Carlo Morabito, Amir HussainAbstract:Abstract In the current digital era, one of the most critical and challenging issues is ensuring cybersecurity in information technology (IT) infrastructures. With significant improvements in technology, hackers have been developing ever more complex and dangerous malware attacks that make intrusion recognition a very difficult task. In this context, traditional analytical tools are facing severe challenges to detect and mitigate these threats. In this work, we introduce a novel Statistical Analysis and autoencoder (AE) driven intelligent intrusion detection system (IDS). Specifically, the proposed IDS combines data analytics and Statistical techniques with recent advances in machine learning theory to extract more optimized, strongly correlated features. The proposed IDS is evaluated using the benchmark NSL-KDD database. Comparative experimental results show that the designed Statistical Analysis and AE based IDS achieves better classification performance compared to conventional deep and shallow machine learning and other recently proposed state-of-the-art techniques.
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Statistical Analysis driven optimized deep learning system for intrusion detection
arXiv: Cryptography and Security, 2018Co-Authors: Cosimo Ieracitano, Ahsan Adeel, Francesco Carlo Morabito, Mandar Gogate, Kia Dashtipour, Hadi Larijani, Ali Raza, Amir HussainAbstract:Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative Statistical Analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and Statistical Analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.
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Statistical Analysis driven optimized deep learning system for intrusion detection
Brain Inspired Cognitive Systems, 2018Co-Authors: Cosimo Ieracitano, Ahsan Adeel, Francesco Carlo Morabito, Mandar Gogate, Kia Dashtipour, Hadi Larijani, Ali Raza, Amir HussainAbstract:Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology (ICT) systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks have made the recognition task even more complicated and challenging. In this work, we propose an innovative Statistical Analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and Statistical Analysis methods, followed by a deep autoencoder (AE) for potential threat detection. Specifically, a preprocessing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discards features with null values grater than 80% and selects the most significant features as input to the deep autoencoder model trained in a greedy-wise manner. The NSL-KDD dataset (an improved version of the original KDD dataset) from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed IDS system for improving intrusion detection as compared to existing state-of-the-art methods.
Christou Michalis - One of the best experts on this subject based on the ideXlab platform.
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Statistical Analysis of major accidents in petrochemical industry notified to the major accident reporting system mars
Journal of Hazardous Materials, 2006Co-Authors: Zoe Nivolianitou, Myrto Konstandinidou, Christou MichalisAbstract:The European major accident reporting system (MARS) was created within the framework of European Union (EU) directive 82/501, the so-called "SEVESO" directive, and in order to register all the major industrial accidents notified to the European Union authorities from the member states. Statistical Analysis of these accidents offers significant data to the understanding and prevention of industrial accidents. This paper makes an Analysis of some characteristics of major accidents in the petrochemical sector included in MARS. The Statistical Analysis focused on the main categorization fields of the MARS short reports and additionally a refinement of the immediate causes of major accidents with focus on the organizational factors was attempted through the details provided in the full reports of the database.
P. M. Amaral - One of the best experts on this subject based on the ideXlab platform.
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Weibull Statistical Analysis of granite bending strength
Rock Mechanics and Rock Engineering, 2008Co-Authors: P. M. Amaral, J. Cruz Fernandes, L. Guerra RosaAbstract:This paper describes and discusses the adequacy of Weibull Statistical Analysis to analyse the bending strength of granite. The experimental results show that strength variability is related with a specific origin of failure. This conclusion is based on analysing the influence of the surface condition (extrinsic defects) on the bending strength results treated by the Weibull statistics. The conclusions drawn from this study have been validated by analysing the results of the critical flaw dimension estimated by applying the linear elastic fracture mechanics (LEFM) formulae. Results obtained from fractographic examination also have been used to describe the location of the origin of the fracture and understand the distribution of defects; i.e., there is a unimodal distribution of defects (intrinsic defects), despite the fact that some outlier values are normally observed in the fractured surfaces.
Alain Iost - One of the best experts on this subject based on the ideXlab platform.
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Statistical Analysis of the vickers hardness
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 1999Co-Authors: Jeanmarc Schneider, Maxence Bigerelle, Alain IostAbstract:The Statistical dispersion in Vickers hardness measurements is discussed in order to check the reliability of hardness measurements. Several indentations were made under the same nominal conditions on a hardness standard. This was repeated for different loads. The distribution of diagonal lengths of indentation prints is found to be of Gaussian type. From the distribution function of the indentation lengths, the probability density function of the hardness is derived for the general case of several indentation measurements. The mean value and variance of hardness are compared with results based on Gaussian statistics. From this comparison, it follows that Statistical Analysis relying upon Gaussian distributions can be carried out within a given confidence level which depends on the number of indentations. A method is presented to calculate the number of indentations needed to achieve a certain level of accuracy.
