Computer Science, Information Technology and Telecommunications

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    Hybrid peak to average power ratio reduction in orthogonal frequency division multiplexing system
    (Universiti Teknologi Malaysia, 2017) Jaber, Ali Yasir
    Multi-carrier systems based on Orthogonal Frequency Division Multiplexing (OFDM) is a widely-used modulation in wireless communication systems because it enables high throughput data transfer and is robust against frequency selective fading caused by the multipath wireless channel. Nevertheless, OFDM suffers from disadvantages such as high Peak-to-Average Power Ratio (PAPR) and high sensitivity to Carrier Frequency Offset (CFO) which leads to a loss of subcarrier orthogonality and severe system degradation. Thus, a suitable reduction technique should be used in OFDM system to mitigate these drawbacks. Mitigation of the impacts of PAPR and Inter-Carrier Interference (ICI) due to CFO at the OFDM transmitter is the main target of this work. In this work, PAPR and ICI reduction methods are proposed at the OFDM transmitter. Clipping Peaks Amplifying Bottoms (CPAB) method is developed to reduce PAPR, where the negative peaks of the clipped OFDM signal are amplified. However, to reduce further PAPR level, a combination of Partial Transmit Sequence (PTS) with Cascade CPAB (PTS-CCPAB) is proposed. To improve BER performance, a Carrier Frequency Offset (CFO) compensation method is added to the hybrid PTS-CCPAB. The proposed work was conducted in MATLAB simulator using the parameters of Wireless Access Vehicular Environment (WAVE) IEEE 802.11p standard. The hybrid PTS-CCPAB/CFO introduced a PAPR Reduction Gain (RG) of 39% compared to the conventional system. Also, system performance at BER =10-4 improved by 12% and 5% over Additive White Gaussian Channel (AWGN) and Rayleigh channels respectively compared to the conventional system. Overall results show that the proposed work is a suitable solution to mitigate the loss of subcarrier orthogonality and system degradation by improving both PAPR and BER performances. The proposed work can be used in most multicarrier wireless communication system.
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    Enhancing efficiency and robustness against collision attack using mersenne number transform for hash function
    (Universiti Teknologi Malaysia, 2022) Omar Maetouq, Ali Aboulqasim
    The most popular hash algorithms functions are Message Digest 5 (MD5), Secure Hash Function1 (SHA-1) and Secure Hash Function 2 (SHA-2). These algorithms utilise the Merkle-Damgard structure. The structure's weaknesses have been discovered, especially against a collision attacks. In this attack, a colluder tries to find two different input messages that produce the same hash result with an effort of less than 2n/2 (where n = length of hash value). That required at most 239 MD5 operations to find a collision, much lower than the intended 264. For SHA-1 with n=160, the collision can be found with effort less than 269, much lower than the intended 280. The fact that collisions are now easily generated, means that these algorithms can no longer be reliably used. Therefore, this study proposed a hash function that is more efficient and robust against collision attacks using the New Mersenne Number Transform (NMNT) and a secret key to achieve the required robustness and maintain efficiency. NMNT is utilised due to its characteristics that are analogous to cryptographic requirements, such as sensitivity, diffusion, and parameterisation, while the secret key is used to increase the scheme’s security. The proposed hash scheme in this study is called Hash Mersenne Transform (HMT). It takes an arbitrary length as input to generate a hash value with variable lengths (128, 256 and 512-bits). HMT consisted of four main stages, namely: Pre-Processing, applying new Mersenne number transform, generating a secret key, and Hash value. Security and performance analyses are conducted to validate the robustness and efficiency of the proposed scheme HMT. To investigate the collision resistance capability of the HMT, collision tests are performed which focus on the possibility of collision between every two hash pairs. From the results, it found that the number of hits of HMT does not exceed 1 for 128-bits hash values, 2 for 256-bits hash values and 3 for 512- bit hash values. This indicates that the HMT has good collision resistance which provides high robustness against collision attacks. In terms of efficiency analysis, the proposed scheme HMT is considered more efficient than existing schemes due to its reduction in execution time. In particular, for long messages with a length equal to 10 MB, the time cost of the HMT is two times less than for SHA-2. In addition, statistical analyses are also employed, which involved the hash value distribution, confusion and diffusion, and sensitivity of hash value to message, secret key and image. Statistics related to mean changed bit number, mean changed probability and their standard deviations are considered the diffusion and confusion quality of the proposed scheme HMT. The results show that the standard deviations are very small for the proposed scheme HMT, the mean changed probability is very close to 50%, and the mean changed bit number is likewise close to half of the hash value lengths. This suggests that the proposed scheme HMT has a stable diffusion and confusion capability. Text messages and images are used to measure the sensitivity of the HMT. The results demonstrates that even a small alteration in the input message or image, such as 1-bit, can cause a significant change in the final hash value. These findings prove that HMT has high hash sensitivity. Comparing the security and performance analysis to other hash functions, it can be concluded that the proposed scheme HMT is suitable for data integrity, message authentication, and blockchain applications.
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    A malicious URL detection framework using priority coefficient and feature evaluation
    (Universiti Teknologi Malaysia, 2023) Rafsanjani, Ahmad Sahban
    Malicious Uniform Resource Locators (URLs) are one of the major threats in cybersecurity. Cyber attackers spread malicious URLs to carry out attacks such as phishing and malware, which lead unsuspecting visitors into scams, resulting in monetary loss, information theft, and other threats to website users. At present, malicious URLs are detected using blacklist and heuristic methods, but these methods lack the ability to detect new and obfuscated URLs. Machine learning and deep learning methods have been seen as popular methods for improving the previous method to detect malicious URLs. However, these methods are entirely datadependent, and a large, updated dataset is necessary for the training to create an effective detection method. Besides, accuracy and detection mostly depend on the quality of training data. This research developed a framework to detect malicious URL based on predefined static feature classification by allocating priority coefficients and feature evaluation methods. The feature classification employed 39 classes of blacklist, lexical, host- based, and content-based features. A dataset containing 2000 real-world URLs was gathered from two popular phishing and malware websites, URLhaus and PhishTank. In the experiment, the proposed framework was evaluated with three supervised machine learning methods: Support Vector Machine (SVM), Random Forest (RF), and Bayesian Network (BN). The result showed that the proposed framework outperformed these methods. In addition, the proposed framework was benchmarked with three comprehensive malicious URL detection methods, which were Precise Phishing Detection with Recurrent Convolutional Neural Networks, Li, and URLNet in terms of accuracy and precision. The results showed that the proposed framework achieved a detection accuracy of 98.95% and a precision value of 98.60%. In sum, the developed malicious URL framework significantly improves the detection in terms of accuracy.
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    Multistage artificial neural network in structural damage detection
    (Universiti Teknologi Malaysia, 2015) Goh, Lyn Dee
    This study addressed two main current issues in the area of vibration-based damage detection. The first issue was the development of a pragmatic method for damage detection through the use of a limited number of measurements. A full set of measurements was required to establish the reliable result, especially when mode shape and frequency were used as indicators for damage detection. However, this condition is usually difficult to achieve in real-life applications. Hence, in this study, a multistage artificial neural network (ANN) was employed to predict the unmeasured data at all the unmeasured point locations to obtain full measurement before proceeding to damage detection. The accuracy and efficiency of the proposed method for damage detection was investigated. Furthermore, the sensitivity of the number of measurement points in the proposed method was also investigated through a parametric study. The second issue was the integration of the uncertainties into the proposed multistage ANN. The existence of uncertainties is inevitable in practical applications because of modelling and measurement errors. These uncertainties were incorporated into the multistage ANN through a probabilistic approach. The results were in the means of the probability of damage existence, which were computed using the Rosenblueth’s point-estimate method. The results of this study evidenced that the multistage ANN was capable of predicting the unmeasured data at the unmeasured point locations, and subsequently, was successful in predicting the damage locations and severities. The incorporation of uncertainties into the multistage ANN further improved the proposed method. The results were supported through the demonstration of numerical examples and an experimental example of a prestressed concrete panel. It is concluded that the proposed method has great potential to overcome the issue of using a limited number of sensors in the vibrationbased damaged detection field.
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    Prediction of fracture dip using artificial neural networks
    (Universiti Teknologi Malaysia, 2017) Alizadeh, Mostafa
    Fracture characterization and fracture dip prediction can provide the desirable information about the fractured reservoirs. Fractured reservoirs are complicated and recent technology sometimes takes time and cost to provide all the desired information about these types of reservoirs. Core recovery has hardly been well in a highly fractured zone, hence, fracture dip measured from core sample is often not specific. Data prediction technology using Artificial Neural Networks (ANNs) can be very useful in these cases. The data related to undrilled depth can be predicted in order to achieve a better drilling operation, or maybe sometimes a group of data is missed then the missed data can be predicted using the other data. Consequently, this study was conducted to introduce the application of ANNs for fracture dip data prediction in fracture characterization technology. ANNs are among the best available tools to generate linear and nonlinear models and they are computational devices consisting of groups of highly interconnected processing elements called neurons, inspired by the scientists' interpretation of the architecture and functioning of the human brain. A feed forward Back Propagation Neural Network was run to predict the fractures dip angle for the third well using the image logs data of other two wells nearby. The predicted fracture dip data was compared with the fracture dip data from image logs of the third well to verify the usefulness of the ANNs. According to the obtained results, it is concluded that the ANN can be used successfully for modeling fracture dip data of the three studied wells. High correlation coefficients and low prediction errors obtained confirm the good predictive ability of ANN model, which the correlation coefficients of training and test sets for the ANN model were 0.95 and 0.91, respectively. Significantly, a non-linear approach based on ANNs allows to improve the performance of the fracture characterization technology