Articles
<![CDATA[Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Rume Elizabeth Yoro]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v11i2.pp1498-1509
<![CDATA[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.]]>
<![CDATA[Empirical Bayesian network to improve service delivery and performance dependability on a campus network ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Andrew Okonji Eboka]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i3.pp623-635
<![CDATA[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.]]>
<![CDATA[Boyer Moore string-match framework for a hybrid short message service spam filtering technique ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[David Ademola Oyemade]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i3.pp519-527
<![CDATA[Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. Spams are unsolicited message or inappropriate contents. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure. Model achieves an accuracy of 97% with an error rate of 1.2%.]]>
<![CDATA[Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Debby Oghenevwede Otakore]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp497-506
<![CDATA[Implicit clusters are formed as a result of the many interactions between users and their contacts. Online social platforms today provide special link-types that allows effective communication. Thus, many users can hardly categorize their contacts into groups such as “family”, “friends” etc. However, such contact clusters are easily represented via implicit graphs. This has arisen the need to analyze users’ implicit social graph and enable the automatic add/delete of contacts from/to a group via a suggestion algorithm – making the group creation process dynamic (instead of static, where users are manually added or removed). The study implements the friend suggest algorithm, which analyzes a user’s implicit social graph to create custom contact group using an interaction-based metric to estimate a user’s affinity to his contacts and groups. The algorithm starts with a small seed set of contacts – already categorized by a user as friends/groups; And, then suggest other contacts to be added to a group. The result inherent demonstrates the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.]]>
<![CDATA[Inventory prediction and management in Nigeria using market basket analysis associative rule mining: memetic algorithm based approach ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Andrew Okonji Eboka]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 8, No 3: December 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v8i3.pp128-138
<![CDATA[A key challenge in businesses today is determining inventory level for each product (to be) sold to clients. A pre-knowledge will suppress inventory stock-up and help avert unnecessary demurrage. It will also avoid stock out and avert loss of clients to competition. Study aims to unveil customer’s behavior in purchasing goods and thus, predict a next time purchase as well as serve as decision support to determine the required amount of each goods inventory. Study is conducted for Delta Mall (Asaba and Warri branches) department store. We adapt the memetic algorithm on market basket dataset to examine buying behavior of customers, their preference and frequency at which goods are purchased in common (basket). Result shows some items placed in basket allow customers to purchase items of similar value, or best combined with the selected items due to shelf-placement via concept of feature drift. Model yields 21-rules for eight items obtained from data transaction mining dataset acquired from Delta Mall.]]>
<![CDATA[Memetic algorithm for short messaging service spam filter using text normalization and semantic approach ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Andrew Okonji Eboka]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 9, No 1: April 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v9i1.pp9-18
<![CDATA[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.]]>
<![CDATA[Computational solution of networks versus cluster grouping for social network contact recommender system ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Debby Oghenevwede Otakore]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 9, No 3: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v9i3.pp185-194
<![CDATA[Graphs have become the dominant life-form of many tasks as they advance a structural system to represent many tasks and their corresponding relationships. A powerful role of networks and graphs is to bridge local feats that exist in vertices or nodal agents as they blossom into patterns that helps explain how nodes and their corresponding edges impacts a complex effect that ripple via a graph. User cluster are formed as a result of interactions between entities – such that many users today, hardly categorize their contacts into groups such as “family”, “friends”, “colleagues”. The need to analyze such user social graph via implicit clusters, enables the dynamism in contact management. Study seeks to implement this dynamism via a comparative study of the deep neural network and friend suggest algorithm. We analyze a user’s implicit social graph and seek to automatically create custom contact groups using metrics that classify such contacts based on a user’s affinity to contacts. Experimental results demonstrate the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.]]>
<![CDATA[Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Obinna Nwankwo]]>
<![CDATA[ JINAV: Journal of Information and Visualization Vol. 2 No. 1 (2021) ]]>
Publisher : <![CDATA[PT Mattawang Mediatama Solution ]]>
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DOI: 10.35877/454RI.jinav274
<![CDATA[Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.]]>
<![CDATA[Multi-Agent Bayesian Framework For Parametric Selection In The Detection And Diagnosis of Tuberculosis Contagion In Nigeria ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Obinna Nwankwo]]>
<![CDATA[ JINAV: Journal of Information and Visualization Vol. 2 No. 2 (2021) ]]>
Publisher : <![CDATA[PT Mattawang Mediatama Solution ]]>
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DOI: 10.35877/454RI.jinav375
<![CDATA[Decision making has become quite a critical factor in our everyday living. The provision of data alongside its consequent processing has further sought to extend and expand our reasoning faculties as well as effectively aid proper decision making. But data is daily, produced at an exponential rapid rate and the volume in amount of data churned out to be processed even more so that we now require data storage optimization techniques to process such humongous volume of data. These have today, necessitated the need for advancement in data mining process. With the tremendous advances made in data mining, machine learning, storage virtualization and optimization – amongst other fields of computing – researchers now seek a new paradigm and platform called data science. This field today has become quite imperative as it seeks to provide beneficial support in constructing models and algorithms that can effectively assist domain experts and practitioners to make comprehensive and sound decisions regarding potential problematic cases. We focus on modeling social graph using implicit suggest algorithm in medical diagnosis to effectively respond to problematic cases of Tuberculosis (TB) in Nigeria. We introduce spectral clustering and Bayesian Network, construct algorithms cum models for predicting potential problematic cases in Tuberculosis as well as compare the algorithms based on data samples collected from an Epidemiology laboratory at the Federal Medical Center Asaba in Delta State of Nigeria. The volume of data was collated and divided into two data sets which are the training dataset and the investigation dataset. The model constructed by this study has shown a high predictive capability strength compared to other models presented on similar studies.]]>
<![CDATA[Migration Pattern As Threshold Parameter In The Propagation of The Covid-19 Epidemic Using An Actor-Based Model for SI-Social Graph ]]>
<![CDATA[Arnold Adimabua Ojugo]]>;
<![CDATA[Rume Elizabeth Yoro]]>
<![CDATA[ JINAV: Journal of Information and Visualization Vol. 2 No. 2 (2021) ]]>
Publisher : <![CDATA[PT Mattawang Mediatama Solution ]]>
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DOI: 10.35877/454RI.jinav379
<![CDATA[Despite the benefits inherent with social interactions, the case of epidemics cum pandemic outbreaks especially the case of the novel corona virus (covid-19) alongside its set protocols employed to contain the spread therein - has continually left the world puzzled as the disease itself has come to stay. The nature of its rapid propagation on exposure alongside its migration spread pattern of this contagion (with retrospect of other epidemics) on daily basis, has also left experts rethinking the set protocols. Our study involved modelling the covid-19 contagion on a social graph, so as to ascertain if its propagation using migration pattern as a threshold parameter can be minimized via the employment of set protocols. We also employed a design that sought to block or minimize targeted spread of the contagion with the introduction of seedset node(s) using the susceptible-infect framework on a time-varying social graph. Study results showed that migration or mobility pattern has become an imperative factors that must be added when modelling the propagation of contagion or epidemics.]]>
