Signature-Based Malware Detection Using Approximate Boyer Moore String Matching Algorithm

Ojugo, Arnold Adimabua; Eboka, A. O.

doi:10.5815/ijmsc.2019.03.05

Cited by 31 publications

(42 citation statements)

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“…Its connections are set as either explicit (apriori knowledge) and/or implicit (post-priori knowledge) to allow learning so that the net is trained to learn patterns that change its weight and bias based on a rule [40]. Its learning is grouped into either of: supervised, unsupervised and reinforcement [45,46].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Inventory prediction and management in Nigeria using market basket analysis associative rule mining: memetic algorithm based approach

Ojugo¹,

Eboka²

2019

IJ-ICT

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<p class="Text">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.</p>

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Inventory prediction and management in Nigeria using market basket analysis associative rule mining: memetic algorithm based approach

Ojugo¹,

Eboka²

2019

IJ-ICT

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“…In Nigeria, the scene is not left out as a relapse of the banking institution to meet with the client needs in relation to the adoption and adaptation of emerging technologies, will see such legacy banking institutions loosing revenue (Okonta et al, 2014;Ojugo and Eboka, 2019b;2020b). But, some merits of these technologies (inasmuch as they portend doom and lose of revenue for non-complaint banks)is that they also proffer benefits and opportunities that these banks can leverage upon such as Big Data, advanced analytics and other new techs to improve the customer experience, build further client trust, loyalty and increase revenue for the banking institutions.…”

Section: Introduction *mentioning

confidence: 99%

Intelligent Peer-To-Peer Banking Framework: Advancing The Frontiers of Agent Banking For Financial Inclusion In Nigeria Via Smartphones

Ojugo

Otakore

2020

Quant. Econ. Manag. Stud.

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The advent of the retail point of sale (POS) system as a critical component of the traditional retail infrastructure seeks to advance client payment-ease for goods and services rendered by vendors as well as the effective collection of funds by the vendor. It also aids the vendor to collect in advance monies that the client may wish to spend later on goods and services. Thus, the POS has since become a necessity in modern retail stores as its increased usage has seen a transformation from a single machine to a cloud and smart platforms. Our study seeks to model a conceptual framework for decentralized POS as adapted to smartphones. This will enhance cashless transaction irrespective of a customer’s location globally and locally. Built around the block-chain technology, it seeks to minimize challenge(s) of time, installation requirements incurred with the adoption of automatic teller machine (ATM), location and citing of agent-banking in a rural area with low tele- and tech-penetration.

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“…SMS spams have since become enormous challengecausing great loss of revenue to Internet Service Providers, Mobile Network Operators and users in general. On overall, spams grew by 300% from just 2011 to 2012 from millions of SMS received worldwide; And 33.3% attributed to spam-related messages [2,8]. In Nigeria alone, an estimated 334,857,685 SMS spams were received daily in 2015.…”

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confidence: 99%

“…The tremendous rise in the usage of SMS is attributed to [16,[1][2]: a) Trust in SMS channel: SMS is a private communication between two parties only has created some level of trust and acceptance all over the world such that financial institution has adopted its use in payment authorization. b) High open rate: Average time it takes to respond to SMS is faster than email and voice callmaking it a preferred choice.…”

mentioning

confidence: 99%

“…SMS has great benefit for both subscribers and operators in diverse ways centered on convenience, flexibility, seamless integration of messaging services and data access. Others may include [1][2]: (a) delivery of notifications, (b) guaranteed delivery, (c) reliable, low-cost for concise data, (d) ability to screen messages and return calls, (e) increases productivity, (f) more sophisticated functionality provides enhanced user benefits, (g) delivery to multiple users at same time, (h) ability to receive diverse information, (i) e-mail generation, (l) creation of user groups, (m) integration with other data and Internet-based application, and (n) increase in revenue for mobile network operators (MNO).…”

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confidence: 99%

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Memetic algorithm for short messaging service spam filter using text normalization and semantic approach

Ojugo

Eboka

2020

IJ-ICT

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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.

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Signature-Based Malware Detection Using Approximate Boyer Moore String Matching Algorithm

Cited by 31 publications

References 0 publications

Inventory prediction and management in Nigeria using market basket analysis associative rule mining: memetic algorithm based approach

Inventory prediction and management in Nigeria using market basket analysis associative rule mining: memetic algorithm based approach

Intelligent Peer-To-Peer Banking Framework: Advancing The Frontiers of Agent Banking For Financial Inclusion In Nigeria Via Smartphones

Memetic algorithm for short messaging service spam filter using text normalization and semantic approach

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