Online social networks, such as Facebook, Twitter, LinkedIn, etc., have grown exponentially in recent times with a large amount of information. These social networks have huge volumes of data especially in structured, textual, and unstructured forms which have often led to cyber-crimes like cyber terrorism, cyber bullying, etc., and extracting information from these data has now become a serious challenge in order to ensure the data safety. In this work, we propose a new, supervised approach for Information Extraction (IE) from Web resources based on remote dynamic editing, called EIDED. Our approach is part of the family of IE approaches based on masks extraction and is articulated around three algorithms: (i) a labeling algorithm, (ii) a learning and inference algorithm, and (iii) an extended edit distance algorithm. Our proposed approach is able to work even in the presence of anomalies in the tuples such as missing attributes, multivalued attributes, permutation of attributes, and in the structure of web pages. The experimental study, which we conducted, on a standard database of web pages, shows the performance of our EIDED approach compared to approaches based on the classic edit distance, and this with respect to the standard metrics recall coefficient, precision, and F1-measure.

The current generation (5G) mobile communication system promises to accommodate a wide range of new applications and use scenarios, resulting in more flexible and unified connection. To satisfy the required criteria, the current waveform was replaced with new UF-OFDM, which combines the advantages of OFDM with enhanced spectral characteristics and greater resilience against time-frequency misalignments. However, its biggest disadvantage is the transmitter's computational complexity, which may be up to two hundred times that of OFDM if there is no reduction in complexity. The majority of current research on unique waveforms has focused on filter modification or performance enhancement strategies. UFMC with the use of adaptive filter (UFMC -FSK) is offered as a revolutionary technique in this study. The filter designed and used to transport information through the index modulation technique. As a result, each UF-OFDM sub band's used filter is chosen, so the data rate is enhanced according to a filter configured depending on original input data bits. The combined Maximum-likelihood (ML) decision metric for each sub band that is calculated at the receiver. Each sub band has a filter as well as data symbols that provide the minimal metric for making decisions are discovered. Furthermore, the bit error rate and power spectrum density are enhanced over the UF-OFDM technique, however there is some trade-off. Overall, the proposed system outperform typical UF-OFDM. Matlab simulations are used to assess the performance of the Adaptive UFMC system.

Scaling high performance computer systems needs increasing the fault tolerance at the design stage of a topology. There are several approaches of designing simple fast routing with fault tolerance. One of effective approach is to ensure fault tolerance at the topology level. This article discusses two methods for optimizing topologies synthesized using Dragonfly and Excess De Brujin. Methods of topology saturation are discusses, which allow to increase the dimension of the system without deterioration of topological characteristics due to the optimization of the synthesis method. Three scaling constraint methods are also proposed to reduce the topology dimension to the desired performance.

Many techniques have been proposed to detect and prevent spam over Internet telephony. Human spam calls can be detected more accurately with these techniques. However, robocalls, a type of voice spammer whose calling patterns are similar to those of legitimate users, cannot be detected as effectively. This paper proposes a model for robocall detection using a machine learning approach. Voice data recordings were collected and the relevant features for study were selected. The selected features were then used to formulate six (6) detection models. The formulated models were simulated and evaluated using some performance metrics to ascertain the model with the best performance. The C4.5 decision tree algorithm gave the best evaluation result with an accuracy of 99.15%, a sensitivity of 0.991%, a false alarm rate of 0.009%, and a precision of 0.992%. As a result, it was concluded that this approach can be used to detect and filter both machine-initiated and human-initiated spam calls.

Mobile Ad-Hoc Networks (MANETs for short) are gaining the importance in the field of wireless communication. The promising feature of MANET is that it can be deployed immediately in demanding situations as they do not require the infrastructure or any centralized structures as compared to traditional wired and wireless networks.An intelligent system has been designed to select an optimum route for various contexts.An efficient protocol is designed to overcome the limits of route finding and link formation in MANET’s. This can be done by making use of the application of soft-computing techniques such as artificial neural networks, fuzzy logic and genetic algorithms. Traditional techniques are based on statistical techniques such as regression models and probabilistic methods.It can be seen from the simulation outcomes that the route finding time using the HYPER-NF-NET simulator which use soft computingtechniques is 20% to the routing finding time using NS-2 simulator. It is also seen from the simulation results that the HYPER-NF-NET protocol performance is better compared to AODV, DSR and OLSR routing protocols for different node population and various degree of congestion. The simulation results showcase a superiority of HYPER-NF-NET simulator over NS-2 and associated HFNET protocol over other existing protocols.

Cloud computing technologies comprise various kinds of significant desirable constrains such as security, liability, government surveillance, telecommunications capacity, anonymity and privacy. The usage of cipher text technology is considered as a desired technique for performing the process of encryption in order to solve the issue of granting security to the data that are shared in the cloud. Similarly, the architecture of multi-tenant in the cloud computing system grants benefits to both the service providers and end-users which shares a common cloud platform to multiple tenants (i.e.) users and suitable resources are also computed by implementing proposed architecture. Therefore in this research work, the concept of cipher text multi-tenant are integrated for providing enhanced security to the data shared in the cloud environment. Hence a Modified Elliptic Curve Cryptography (MECC) based on Diffee Hellman algorithm is proposed in this research paper which provides enhanced security using alternate key generation. The encryption, decryption, upload and download time are calculated and it is concluded that the algorithm that is proposed in this research paper consumes less time for all these measures when comparing with other existing algorithms. Characteristics like less memory, high operational performance, small sized keys and rapid key generation process, and effective resource savings enable the modified ECC to obtain high efficiency. The encryption time of the proposed MECC system was 51ms with the key length 4096 bits, whereas existing method had 92ms as encryption time. Likewise With the key length of 4096 bits, the decryption time for the proposed model was 159ms. Accordingly the proposed system has reduced cipher text key size of 836KB when compared with the existing AES, Blow Fish, and Two Fish. Additionally, the key generation time (35 s) also seems to be considerably reduced when compared with the existing methods. These statements reveal that the proposed system outshine the state of art methods in terms of key generation time, encryption and decryption time and computational complexity.

The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial of Service (DDoS) is a type of cyber attack that prevents legitimate users from accessing network system resources. To minimize major damage, quick and accurate DDoS attack detection techniques are essential. To classify target classes, machine learning classification algorithms are faster and more accurate than traditional classification methods. This is a quantitative research applies Logistic Regression, Decision Tree, Random Forest, Ada Boost, Gradient Boost, KNN, and Naive Bayes classification algorithms to detect DDoS attacks on the CIC-DDoS2019 data set, which contains eleven different DDoS attacks each containing 87 features. In addition, evaluated classifiers’ performances in terms of evaluation metrics. Experimental results show that AdaBoost and Gradient Boost algorithms give the best classification results, Logistic Regression, KNN, and Naive Bayes give good classification results, Decision Tree and Random Forest produce poor classification results.