Yazar "Alsaadi, Husam Ibrahiem" seçeneğine göre listele

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    Computational intelligence algorithms to handle dimensionality reduction for enhancing intrusion detection system
    (Inst Information Science, 2020) Alsaadi, Husam Ibrahiem; Almuttairi, Rafah M.; Bayat, Oğuz; Uçan, Osman Nuri
    In this paper, propose to use computational intelligence models to improve intrusion detection system, the computational intelligence algorithms are used as preprocessing steps for selecting most significant features from network data. Two computational intelligence algorithms, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are implemented to generate subset of relevant features. The computational intelligence approaches have been applied to optimize the classification of algorithms. The most significant features obtained from computational intelligence is fed into the classification algorithm. Novelty of this presents research of use computational intelligence algorithms namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for handling dimensionality reduction. The dimensionality reduction is obstructed time processing of classification algorithms. Three classification algorithms namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes (NB) are implemented for intrusion detection system. Benchmark datasets, namely, KDD cup and NSL-KDD datasets are used to demonstrate and validate the performance of the proposed model for intrusion detection. From the empirical results, it is observed that the classification algorithm has improved the intrusion detection system with using computational intelligence algorithms. A comparative result analysis between the proposed model and different existing models is presented. It is concluded that the proposed model has outperformed of conventional models.
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    Forecasting of Twitter hashtag temporal dynamics using locally weighted projection regression
    (Institute of Electrical and Electronics Engineers Inc., 2018) Alsaadi, Husam Ibrahiem; Almajmaie, Layth Kamil; Mahmood, Wisam Ali
    Popularity of social networks opens great opportunities for market such as advertisement. Using hashtags increasingly used in twits helps us to realize popular topics on the internet. Since most of new hashtags become popular and then fade away quickly, there is a limited time to predict the trend. Therefore, this paper proposes a fast incremental method to forecast the rate of the used hashtags in hour like time series. Two main parts for forecasting system are applied Preprocessing and Supervised Learning. Normalization is one of most popular preprocessing of dataset also proposed to have larger dataset. Moreover, the efficiency of the system under changing number of input (number of past hours from hashtag history) and output (number of next hours which is going to be predicted) are evaluated. Locally Weighted Projection Regression as one of the most powerful machine learning methods with no metaparameter are applied in this paper as real-Time learning method. The performance of the system is verified by implementation of 'Volume Time Series of Memetracker Phrases and Twitter Hashtags'. The results show that the errors of forecasting system are good enough to understand the trend of the hashtag. © 2017 IEEE.
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    Forecasting of Twitter Hashtahg Temporal Dynamics Using Locally Weighted Projection Regression
    (IEEE, 2017) Alsaadi, Husam Ibrahiem; Almajmaie, Layth Kamil; Mahmood, Wisam Ali
    Popularity of social networks opens great opportunities for market such as advertisement. Using hashtags increasingly used in twits helps us to realize popular topics on the internet. Since most of new hashtags become popular and then fade away quickly, there is a limited time to predict the trend. Therefore, this paper proposes a fast incremental method to forecast the rate of the used hashtags in hour like time series. Two main parts for forecasting system are applied Preprocessing and Supervised Learning. Normalization is one of most popular preprocessing of dataset also proposed to have larger dataset. Moreover, the efficiency of the system under changing number of input (number of past hours from hashtag history) and output (number of next hours which is going to be predicted) are evaluated. Locally Weighted Projection Regression as one of the most powerful machine learning methods with no meta-parameter are applied in this paper as real-time learning method. The performance of the system is verified by implementation of Volume Time Series of Memetracker Phrases and Twitter Hashtags. The results show that the errors of forecasting system are good enough to understand the trend of the hashtag.
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    Text steganography in font color of MS excel sheet
    (Assoc Computing Machinery, 2018) Alsaadi, Husam Ibrahiem; Al-Anni, Maad Kamal; Almuttairi, Rafah M.; Bayat, Oğuz; Uçan, Osman Nuri
    One of the ways to maintain the security of data transfer over the network is to encrypt the data before sending it, for the purpose of increasing data privacy and its importance. Steganography, is used to hide encrypted data in various documents, the art of concealment is embedded Secret Messages (SM) to be passed confidentially within a file without influence on the properties of the file and this can't be anyone's hand is to know that there is confidential data encapsulated by transport data. Previous research used the Excel file to hide data in different ways, but the data masking and packaging by changing the color content writing is what distinguishes our research and make it alone in this area. The Microsoft Excel file size is being used to hide data remains constant in spite of the file containing the confidential data. The secret for the survival of file size unchanged despite the addition of encrypted data, is that confidential data is embedding in the value of the color pallet of the font format used in the cells, leading to the font color change slightly. The main advantage for scathing the Code Message (CM) upon the Cover Excel File (CEF) is making the brute force more confusing and has no possible to visualize the hiding message in normal circumstance, it is somehow rather than embedding it into either MS Word or PPT while the latter done sequentially, in this approach: each Full cell can hide a character with coding process (from 4 bits to 8 bits) In this study, we have had advantages through applying a new approach of Text to Excel hiding techniques, likewise both the conductivity ratio, and the degree of disappearances are indicated perfectly.