Journal of Advances in Computer Engineering and Technology
Volume:5 Issue: 3, Summer 2019

One of the serious threats to cyberspace is the Bot networks or Botnets. Bots are malicious software that acts as a network and allows hackers to remotely manage and control infected computer victims. Given the fact that DNS is one of the most common protocols in the network and is essential for the proper functioning of the network, it is very useful for monitoring, detecting and reducing the activity of the Botnets. DNS queries are sent in the early stages of the life cycle of each Botnet, so infected hosts are identified before any malicious activity is performed. Because the exchange of information in the network environment and the volume of information is very high, Storing and indexing this massive data requires a large database. By using the DNS traffic analysis, we try to identify the Botnets. We used the data generated from the network traffic and information of known Botnets with the Splunk platform to conduct data analysis to quickly identify attacks and predict potential dangers that could arise. The analysis results were used in tests conducted on real network environments to determine the types of attacks. Visual IP mapping was then used to determine actions that could be taken. The proposed method is capable of recognizing known and unknown Bots.

Big data analytics is one of the most important subjects in computer science. Today, due to the increasing expansion of Web technology, a large amount of data is available to researchers. Extracting information from these data is one of the requirements for many organizations and business centers. In recent years, the massive amount of Twitter's social networking data has become a platform for data mining research to discover facts, trends, events, and even predictions of some incidents. In this paper, a new framework for clustering and extraction of information is presented to analyze the sentiments from the big data. The proposed method is based on the keywords and the polarity determination which employs seven emotional signal groups. The dataset used is 2077610 tweets in both English and Persian. We utilize the Hive tool in the Hadoop environment to cluster the data, and the Wordnet and SentiWordnet 3.0 tools to analyze the sentiments of fans of Iranian athletes. The results of the 2016 Olympic and Paralympic events in a one-month period show a high degree of precision and recall of this approach compared to other keyword-based methods for sentiment analysis. Moreover, utilizing the big data processing tools such as Hive and Pig shows that these tools have a shorter response time than the traditional data processing methods for pre-processing, classifications and sentiment analysis of collected tweets.

This paper studies interference alignment scheme and minimum mean square error (MMSE) improvement in Internet of Things (IoT)-oriented cognitive systems, where IoT devices share the licensed spectrum by cognitive radio in spectrum underlay. Target to manage the inter-tier interference caused by cognitive spectrum sharing as well as ensure an MMSE at receivers, the interference alignment algorithms is proposed. In particular, we focus on the problem of designing the optimal linear pre-coding to minimize the total mean square error while satisfying transmit power constraints. Firstly, we introduce a system model of the downlink IoT-oriented cognitive multi-input multi-output (MIMO) system. Secondly, we propose an interference nulling based cognitive interference alignment scheme, and then, the pre-coding and post-coding matrix designs for the primary transceivers to minimum mean square error (MSE), as well as to eliminate the co-channel interference to the primary receivers. We also apply these interference alignment scheme and matrix designs for the secondary links. Finally, the numerical results are used to evaluate performance of the proposed algorithm.

Cloud computing technology is providing businesses, be it micro, small, medium, and large scale enterprises with the same level playing grounds. Small and Medium enterprises (SMEs) that have adopted the cloud are taking their businesses to greater heights with the competitive edge that cloud computing offers. The limitations faced by (SMEs) in procuring and maintaining IT infrastructures has been handled on the cloud platform for the SMEs that adopt it. In this research, the impact and challenges of cloud computing on SME’s that have adopted it in Nigeria has been investigated. The impacts identified ranges from provisioning IT infrastructures, reshaping and extending business values and outreach to giving competitive edge to businesses subscribed to it. Though Cloud computing has many benefits; however, it is not without some pitfalls. These pitfalls include data vulnerability, vendor lock-in, limited control over the infrastructure by the subscribers etc. To investigate the level of impacts and challenges being faced by SMEs in Nigeria on the cloud platform, questionnaires were administered to managers and employees of about fifty SMEs that have deployed cloud. The data collected were analyzed using Statistical Package for Social Sciences (SPSS), from which appropriate recommendations were made.

Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the field of hardware and software. Security is the top priority to prevent a cyber-attack that users should initially be detecting the type of attacks because virtual environments are not monitored. Today, email is the foundation of many internet attacks that have happened. The Hackers and penetrators are using email spam as a way to penetrate into computer systems junk. Email can contain viruses, malware, and malicious code. Therefore, the type of email should be detected by security tools and avoid opening suspicious emails. In this paper, a new model has been proposed based on the hybrid of Scatter Searching Algorithm (SSA) and K-Nearest Neighbors (KNN) to email spam detection. The Results of proposed model on Spambase dataset shows which our model has more accuracy with Feature Selection (FS) and in the best case, its percentage of accuracy is equal to 94.54% with 500 iterations and 57 features. Also, the comparison shows that the proposed model has better accuracy compared to the evolutionary algorithm (data mining and decision detection such as C4.5).

Water Quality plays an important role in attaining a sustainable aquaculture system, its cumulative effect can make or mar the entire system. The amount of dissolved oxygen (DO) alongside other parameters such as temperature, pH, alkalinity and conductivity are often used to estimate the water quality index (WQI) in aquaculture. There exist different approaches for the estimation of the quality index of the water in the aquatic environment. One of such approaches is the use of the Artificial Neural Network (ANN), however, its efficacy lies in the ability to select and use optimal parameters for the network. In this work, different WQI estimation models have been developed using the ANN. These models have been developed by varying the activation function in the hidden layer of the ANN. The performance of the ANN-based estimation models was compared with that of the multilinear regression (MLR) based model. The performance comparison depicts the ANN model case 3 with a tangent activation function as the most accurate and optimal model as compared with MLR model and other ANN models based on the mean square error (MSE), root mean square error (RMSE) and regression (R) metrics. The optimal model has a goodness of fit of 0.998, thereby outweighing other developed models in its capability to estimate the WQI in the aquaculture system.