Malware Propagation on Social Time Varying Networks: A Comparative Study of Machine Learning Frameworks

Ojugo, Arnold Adimabua; Eseoghene, Ben-Iwhiwhu,; Kekeje, O.; Yerokun, Mo.; Iyawa, I.J.B.

doi:10.5815/ijmecs.2014.08.04

Cited by 18 publications

(30 citation statements)

References 23 publications

(13 reference statements)

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“…Undergraduates in the selected tertiary institutions were invited via email to participate in a short-web survey about student message usage and info on their future plan to pursue a graduate studies. Webpage was designed and used to collect the data [33][34][35][36][37][38][39]. We provided a link to webpage with misspelt URL (Uniform Resource Locator) to the targets.…”

Section: Procedures For Data Collectionmentioning

confidence: 99%

Empirical Evidence of Socially-Engineered Attack Menace Among Undergraduate Smartphone Users in Selected Universities in Nigeria

2021

IJATCSE

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Smartphone proliferation today, have up-scaled user adoption of Internet-based processing activities. These include (not limited to) e-learning, e-commerce, mobile-banking and others –all aimed at better performance, service delivery at improved execution speed, greater portability, flexibility and accessibility ease. Thus, necessitating growth expansion in mobile-app development across varying platforms that are poised to help users accomplish processing tasks while harnessing the benefits of the technology. Conversely, these techs have also allowed users to be constantly besieged by threats and security breaches. In lieu of residence, sensitive and proprietary –users have become bothered with smartphone exposure to possible data loss and theft –if they are to further adopt these new paradigms. With doubt in their minds, trust levels in user adoption continues to reduce. We seek to investigate social-engineered threats and attacks amongst undergraduates in Nigerian Universities. Result shows that phishing hits a higher successrate against smartphone users in Nigeria, which has decreased client’s trust level in Internet-based services

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Section: Procedures For Data Collectionmentioning

confidence: 99%

Empirical Evidence of Socially-Engineered Attack Menace Among Undergraduate Smartphone Users in Selected Universities in Nigeria

2021

IJATCSE

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“…Their model can reproduce the range of topologies observed across known robust and fragile biological networks, as well as several additional transport, communication, and social networks. Ojugo [13] studied the logarithmic analysis of complex networks yields solution to help minimize virus spread and propagation over networks. Sanatkar [17] derived an epidemic threshold, considering the susceptible-infected-susceptible epidemic model.…”

Section: Related Workmentioning

confidence: 99%

Using SIR Model to Simulate Emotion Contagion in Dynamic Crowd Aggregation Process

Xiang¹

2018

IJPE

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Emotion contagion is an indispensable behavior in a dynamic crowd, especially in an evacuation situation. As a consequence, generating emotion contagion results is very useful in the crowd simulation field. However, because the topology of the crowd usually keeps changing dynamically, computing the contagion process is a challenge. In this paper, we represented our research about the emotion contagion effects on the virtual pedestrian dynamic aggregation process. First of all, we calculated individuals' moving parameters based on their prefixed expectations according to the social force theory. After this, we made an adjacent test for each individual to generate nearer neighbors for further emotional contagions computing between neighbors. We then treated the emotional contagions between individuals and their neighbors as the information spreading process so that we can adopt the emotional information spreading model SIR (Susceptible Infective Removal) to calculate emotional influences, which are represented as their changing moving velocities during aggregation. Social force for computing low level moving parameters and SIR model for generating emotional influences were integrated by our method to simulate the dynamic pedestrian aggregation. Experimental results showed that the SIR model can effectively improve the fidelity of the emotional interaction process and crowd aggregation.

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“…On the basis of this time, each MN is classified in a category. We use heuristics and get intuition from [27][28][29][30][31][32] in the field of ML and data mining. Table 3 shows the time interval between two consecutive incoming sessions on an MN.…”

Section: Discretizing Interval Between Two Consecutive Incoming Sementioning

confidence: 99%

Intelligent Reduction in Signaling Load of Location Management in Mobile Data Networks

Munir¹,

Zahoor²,

Shahzad³

et al. 2016

IJCNIS

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Massive increase in the mobile data traffic volume has recently resulted in a big interest towards the distributed mobility management solutions that aim to address the limitations and drawbacks of centralized mobility management. Location management is an important requirement in a distributed mobility management environment. To provide seamless Internet data services to a mobile node, the location of a mobile node is stored and periodically updated on a location server through a location update message that is sent by the mobile node. In this paper, we propose an intelligent approach of setting the period of sending location update messages on the basis of a mobile node's patterns of data sessions and IP handovers. We use a machine learning approach on the location server. The results show that our approach significantly reduces the signaling load of the location management and the overall reduction is more than 50%.

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Malware Propagation on Social Time Varying Networks: A Comparative Study of Machine Learning Frameworks

Cited by 18 publications

References 23 publications

Empirical Evidence of Socially-Engineered Attack Menace Among Undergraduate Smartphone Users in Selected Universities in Nigeria

Empirical Evidence of Socially-Engineered Attack Menace Among Undergraduate Smartphone Users in Selected Universities in Nigeria

Using SIR Model to Simulate Emotion Contagion in Dynamic Crowd Aggregation Process

Intelligent Reduction in Signaling Load of Location Management in Mobile Data Networks

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