Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack

Abstract

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.

Predictive Intelligent Decision Support Model in Forecasting of the Diabetes Pandemic Using a Reinforcement Deep Learning Approach

“…We extend the study [2] via frameworks modelled in [25][26][27], we use dataset as presented in table 1obtained via a survey of questionnaires, consisting of two phases: (a) demographic data, and (b) tele-medical data. A total of a hundred questionnaires were distributed to various medical (diabetic) professional across forty teaching hospitals in six ( 6) Geo-political zones in Nigeria.…”

“…The Modular Neural Network (MNN) as detailed in [25][26][27] is an improved deep learning neural network with learning that features an independent series of intermediary components and module operating under a certain architecture. It advances a model that receives individual network module output as input that helps compute final output, resolved via tangent activation function.…”

“…Re-usability of rules has been a tedious experienced with such large and complex networks. But, with appropriate data encoding and carefully selected featsnetwork experiences improved performance, improves flexibility of compartmentalization via removal of partitions of the interfaces and eliminates redundancy [27].…”

“…Also, similarities among patterns are mapped into relations on the competitive layer. After training, the pattern relations are observed from this layer which are used as the result determination [26][27].…”

“…b. The MNN receives optimal dataset from knowledgebase with successive labeled/unlabeled rules perceived learnt instances [25][26][27]. The optimized-rules are propagated as IF-THEN rules (enhanced data with predefined variables) classified into various diabetes classes.…”