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Cryptosystems are commonly deployed to secure data transmission over an insecure line of communication. To provide confusion in the data over insecure networks, substitution boxes are the solitary components for delivering a nonlinear mapping between inputs and outputs. A confusion component of a block cipher with high nonlinearity and low differential and linear approximation probabilities is considered secure against cryptanalysis. This study aims to design a highly nonlinear substitution-permutation network using the blotch symmetry of quantum spin states on the Galois field GF (28). To observe the efficiency of the proposed methodology, some common and advanced measures were evaluated for performance, randomness, and cryptanalytics. The outcomes of these analyses validate that the generated nonlinear confusion components are effective for block ciphers and attain better cryptographic strength with a high signal-to-noise ratio in comparison to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Most Intrusion Detection Systems (IDS) nowadays are signature-based. They are very useful and accurate for detecting known attacks. However, they are inefficient with unknown attacks. Moreover, most of cyber attacks start with malicious URLs. Attackers try to trick users into clicking on malicious URLs. This gives attackers an easy way to launch attacks. To defend against this, companies and organizations use IDS/IPS to detect malicous URLs using blacklist of URLs. This is very efficient with known malicious URLs, but useless with unknown malicious URLs. To overcome this problem, a number of malicious Web site detection systems have been proposed. One of the most promising methods is to apply machine learning detection techniques. In this paper, we present a new lexical approach to classify URLs by using machine learning techniques which patternize the URLs. Our approach is based on natural language processing features which use word vector representation and ngram models on the blacklist word as the main features. Using this technique can help classifier distinguish benign URLs from malicious ones. Our experimentation shows that our approach can achieve a high degree of accuracy at 97.1% in the case of SVM. Moreover, it can maintain a high level of robustness with 0.97 precision and 0.93 recall scores. [ABSTRACT FROM AUTHOR]

Reliable video delivery is one of the most important requirements to satisfy end user needs. With the proliferation of multimedia technologies there is a growing demand for video multicast, particularly in challenging wireless environments. Effective video multicast services need to meet the conflicting goals of assured data rate and reliability. The most used IEEE standards (802.11a/b/g/n) do not ensure reliable multicasts since throughput remained primary focus of research in recent past. The IEEE 802.11aa remains the only standard that attempts to handle the unreliability issue with multicasting. However, the block acknowledgement scheme of the IEEE 802.11aa standard suffers from scalability issue. As the number of receivers increases, the number of acknowledgement packets increases proportionally. This increasing acknowledgement storm eventually causes a decrease in the average peak signal-to-noise ratio (PSNR) of the video frames. Therefore, in order to overwhelm the scalability limitation of the IEEE 802.11aa block acknowledgment scheme, this paper proposes a modified block acknowledgment scheme for video multicast. Our scheme considers the impact of the loss of different frames on video quality under Moving Picture Expert Group 4 (MPEG-4) and H.264 video coding. We provide a Markov chain model and numerical analysis of our proposed protocol. Simulation results indicate that proposed scheme performs well in terms of PSNR. [ABSTRACT FROM AUTHOR]

Existing smartphones have many built-in sensors which allow various indoor positioning techniques (IPS) to be developed without the need of additional hardware. Among the techniques, Pedestrian Dead Reckoning (PDR) is one of the more suitable ones for continouos positioning tracking. Through the accelerometer, magnetometer and gyroscope sensors, the number of steps and the direction of those taken steps can be captured and the position of the user can be inferred from a known starting point. Despite the PDR technique is simple, the implementation is not so straight forward as there are some challenges involved. Some of the notable challenges include i) the positioning error accumulates over time; ii) sensors from different phones models produce inconsistent measurement data; iii) walking gaits and step lengths vary greatly for different users. To overcome the error accumulation of PDR, many existing works use beacons to correct a user's position estimation when the user is close to a beacon. Instead of using beacons, this work introduces the concept of correction points, which are logical points where their locations are known. Besides inferring the user's position, the proposed solution also use the correction points to recalibrate user's PDR step length and improves the overall positioning accuracy. From the experimental results, the proposed solution with correction points is able to perform continuous position tracking on three different smartphone models with more than 50% of accuracy improvement compared to IPS that only uses PDR alone. On top of that, we also show how the proposed system can be utilized to perform Wi-Fi fingerprint mapping, which can reduce the effort for system maintenance. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent noise prediction system using pattern analysis and deep learning method. The proposed system is designed to analyze an interlayer noise information from noise and vibration sensors in order to provide a noise warning signal to the noise generator. This scheme is based on both a pattern analysis and a predictive model at that time when the threshold is exceeded. The analyzing process of noise information is performed on Arduino board and noise data is collected from multiple sensors with Bluetooth transmission technology. The Firebase cloud server allows users to access the collected data easily. In addition, a large amount of stored data is analyzed systematically using both R program and a predictive model according to a noise pattern. Performance analysis is carried out to evaluate the excellent performance of the proposed system by showing the accurate predictive results. [ABSTRACT FROM AUTHOR]

Improved Spectral Efficiency (SE) is a prominent feature of Massive Multiple-Input and Multiple-Output systems. These systems are prepared with antenna clusters at receiver (R x) and transmitter (T x) . In this paper, we examined a massive MIMO system to increase SE in each cell that ultimately improves the area throughput of the system. We are aiming to find appropriate values of average cell-density (D), available bandwidth (B), and SE to maximize area throughput because it is the function of these parameters. Likewise, a SE augmentation model was developed to attain an increased transmit power and antenna array gain. The proposed model also considers the inter-user interference from neighboring cells along with incident angles of desired and interfering users. Moreover, simulation results validate the proposed model that is implementable in real-time scenarios by realizing maximum SE of 12.79 bits/s/Hz in Line of Sight (LoS) and 12.69 bits/s/Hz in Non-Line of Sight (NLoS) scenarios, respectively. The proposed results also substantiate the SE augmentation because it is a linear function of transmit power and array gain while using the Uniform Linear Array (ULA) configuration. The findings of this work ensure the efficient transmission of information in future networks. [ABSTRACT FROM AUTHOR]