The exponential growth and development of the internet has created many problems on network security. Current intrusion detection system has failed to fully protect system against sophisticated attacks. This research work explores some dedicated methodologies such as Artificial Neural Network (ANN), Fuzzy Logic, and Genetic Algorithms applied to Intrusion Detection Systems but attacks against networks and information systems are still successful. We proposed Neurofuzzy Genetic Intrusion Detection System which is a fusion of the three Artificial Intelligence techniques. We foresee they would stand a fighting chance against any sophisticated attack, improve accuracy, precision rate and reduce the false positive rate and would protect data integrity, confidentiality and availability. We also discuss the dataset for evaluating the system. In this work we have identified a new research direction in the related field.

The proliferation of cloud computing and internet of things has led to the connectivity of states and nations (developed and developing countries) worldwide in which global network provide platform for the connection. Digital forensics is a field of computer security that uses software applications and standard guidelines which support the extraction of evidences from any computer appliances which are perfectly enough for the court of law to use and make a judgment based on the comprehensiveness, authenticity and objectivity of the information obtained. Cybersecurity is of major concerned to the internet users worldwide due to the recent form of attacks, threat, viruses, intrusion among others going on every day among internet of things. The aim of this work is make a systematic review on the application of machine learning algorithms to cybersecurity and cyber forensics, systematic survey method was used on recent application of machine learning algorithms on cyber forensics and cyber security based on this findings it is observed that cybersecurity is based on confidentiality, integrity and validity of data, it is also noted that there are ten steps to cybersecurity; network security, user education and awareness, malware prevention, removable media control, secure configuration, managing user privileges, incident management, monitoring and home and mobile working and pave away for further research directions on the application of deep learning, computational intelligence, soft computing to cybersecurity and cyber forensics.

ABSTRACT

Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin. The main aim of this research work was to determine the blood glucose level of diabetic patient using adaptive Neuro-fuzzy. Data of 80 diabetic patients were collected from Federal Medical Centre Jalingo. It was used for training and testing the system, Gaussian Membership function was used, hybrid training algorithm was used for training and testing, the error obtain is 0.0008333 at epoch 4 which shows that the training performance is exactly 99.99% and testing performance of the system are 99.99% at epoch 4. This shows that adaptive Neuro-fuzzy system can be applied to medical diagnosis because of the error obtained.

With the advancement of technology things are becoming Simpler and easier in every aspect of life. Automation is the use of control systems and information technologies to reduce the need for human work. Security is an essential part of our life at this crucial moment, the problems of security in our home, industry, banks, schools, hotels and even offices cannot be tackle manually. The main aim of this research work is to design and implement an intelligent and reliable security system using Passive Infrared (PIR) and Global System for mobile communication (GSM) Module embedded on Arduino Uno board. C programme is uploaded on the board using Arduino IDE. The system have surveillance over the movement of people around the house premises 7m to approach the door the security system send an SMS to a GSM phone (HTC) numbers included in the C programming codes.