Today’s popularity of the internet has since proven an effective and efficient means of information sharing. However, this has consequently advanced the proliferation of adversaries who aim at unauthorized access to information being shared over the internet medium. These are achieved via various means one of which is the distributed denial of service attacks-which has become a major threat to the electronic society. These are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ the deep learning approach to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic.

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Abstract-Significant research into the logarithmic analysis of complex networks yields solution to help minimize virus spread and propagation over networks. This task of virus propagation is been a recurring subject, and design of complex models will yield modeling solutions used in a number of events not limited to and include propagation, dataflow, network immunization, resource management, service distribution, adoption of viral marketing etc. Stochastic models are successfully used to predict the virus propagation processes and its effects on networks. The study employs SI-models for independent cascade and the dynamic models with Enron dataset (of e-mail addresses) and presents comparative result using varied machine models. Study samples 25,000 emails of Enron dataset with Entropy and Information Gain computed to address issues of blocking targeting and extent of virus spread on graphs. Study addressed the problem of the expected spread immunization and the expected epidemic spread minimization; but not the epidemic threshold (for space constraint).

The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.

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Machine learning algorithm have become veritable tools for effective decision support towards the construction of systems that assist experts (individuals) in their field of exploits and endeavor with regards to problematic tasks.. They are best suited for tasks where data is explored and exploited; and cases where the dataset contains noise, partial truth, ambiguities and in cases where there is shortage of datasets. For this study, we employ the Bayesian network to construct a model trained towards a target system that can help predict best parameters used for classification of the novel coronavirus (covid-19). Data was collected from Federal Medical Center Epidemiology laboratory (a centralized databank for all cases of the covid-19 in Delta State). Data was split into training and investigation (test) dataset for the target system. Results show high predictive capability.

An effective systemic approach to task will lead to efficient communication and resource sharing within a network. This has become imperative as it aids alternative delivery. With communication properly etched into the fabrics of today’s society via effective integration of informatics and communication technology, the constant upgrades to existing network infrastructure are only a start to meeting with the ever-increasing challenges. There are various criteria responsible for network performance, scalability, and resilience. To ensure best practices, we analyze the network and select parameters required to improve performance irrespective of bottlenecks, potentials, and expansion capabilities of the network infrastructure. Study compute feats via Bayesian network design alongside upgrades implementation to result in a prototype design, capable of addressing users need(s). Thus, to ensure functionality, the experimental network uses known simulation kits such as riverbed modeler edition 17.5 and cisco packet tracer 6.0.1-to conduct standardized tests such as throughput test, application response-time test, and availability test.

Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier.