A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy

Big data frequently contains huge amounts of personal identifiable information and therefore the protection of user2019;s privacy becomes a challenge. Lots of researches had been administered on securing big data, but still limited in efficient privacy management and data sensitivity. This study designed a big data framework named Big Data-ARpM that is secured and enforces privacy and access restriction level. The internal components of Big Data-ARpM consists of six modules. Data Pre-processor which contains a data cleaning component that checks each entity of the data for conformity

Employing differential privacy for big data security

Big data frequently contains huge amounts of personal identifiable information and therefore the protection of user’s privacy becomes a challenge. Lots of researches had been carried out on securing big data, but still limited in efficient privacy management and data sensitivity. This paper designs a mechanism that employs Differential Privacy for protection of personal data, which enforces privacy and access restriction level. The Differential Privacy technique acts on the request by introducing a minimum distortion to the information provided by the database system. The mechanism, DP-Data was implemented with Python scripting and Java Programming languages, Mysql and VmWare on Apache Hadoop platform. To test the effectiveness of DP-Data, a medical dataset with 1,048,576 instances and 12 attributes was employed. It was evaluated based on its utility, scalability, accuracy, sensitivity, specificity and processing time. The results indicated accuracy of 95.80 %, sensitivity of 93.60 %, specificity of 98.00 % and 0.40 ms processing time with high utility and good scalability which shows that the time it takes to preserve a data of 5000 tuples or less are almost similar. From these results, the application of differential privacy in solving privacy issue proved a high level of efficiency. Hence, the deployment of a secure big data framework that is based on access restriction and preserved level of privacy poses a higher level of protection of user’s privacy in comparison with other techniques.Keywords: Big Data, Privacy Preservation, Differential Privac

Adaptive neuro-fuzzy system for malware detection

Malware, which are computer programs designed to infiltrate and disrupt computing operations, is one of the security challenges faced by Internet users. Most malware detection techniques such as signature-based, specification-based and  static-based are faced with high false positive, low accuracy and inability to detect both zero day and polymorphic malware. In this research work, an Adaptive Neuro Fuzzy System for Malware Detection (ANFSMD) was proposed to address these problems. ANFSMD utilizes both the Application Programming Interface (API) calls and operation codes to study the behaviour of Portable Executable (PE) files. The PE files were disassembled into low-level codes and the identified features were grouped for efficient detection. Five features, selected using weighted average, were used for the fuzzification. Using a bell membership function, 243 rules were generated for predicting the behaviours of the PE files. A normalization technique was used to combine the various fuzzy sets into one. Back propagation algorithm was used for the training and the resulting errors from outputs were used to dynamically modify inputs for improved outcomes. The implementation of ANFSMD was carried out using Java Programming Language, Interactive Disassembler and Matlab because of their supports for implementation of micro-programs. A total of 20,750 malware programs from VX Heaven public dataset and 15,000 clean files from Filehippo were used for the evaluation. The result showed that Adaptive Neuro-Fuzzy Inference System (ANFIS) has a detection rate of 97.96%, Naïve Bayes has detection rate of 93.88%, Random Forest has 84.78% and Support Vector Machine has 92.87. The proposed method was also compared with a Control Flow Graph (CFG), which is one of the best existing techniques that adopted the use of API calls. The evaluation showed that the detection rate, false positive rate and overall accuracy for CFG were 93.9%, 9.3% and 92.4%, while the proposed method achieved 98%, 3.9% and 97% respectively. These results showed that ANFSMD can be deployed for efficient detection of all categories of malware.Keywords: Malware, API, N-grams, ANFIS, Features extraction

MONRATE, a descriptive tool for calculation and prediction of re-infection of Ascaris lumbricoides

The objective of the study was to develop an interactive and systematic descriptive tool, MONRATE for calculating and predicting reinfection rates and time of Ascaris lumbricoides   following mass chemotherapy using levamisole. Each pupil previously treated was retreated 6 or 7 months after the initial treatment in Ogun State, Nigeria. The implementation was based on the theoretical equation for time-prevalence: Y = G [1 -(1-X)N-R]. Using the Psuedo-Code of the MONRATE tool, the calculated monthly reinfection rates (X) for the LGAs were 1.6% in Ewekoro, 2.3% in Odeda, 2.3% in Ado-odo/Otta, 3.8% in Ogun Waterside and 4.2% in Obafemi/Owode. The mathematical mean of 'X' values in the study areas for Ogun State was 2.84. The calculated reinfection time (N months) for the LGAs varied such as Ado-odo/Otta (12.7), Ogun Waterside (21.8), Obafemi/Owode (22.92), Odeda (25.45), and Ewekoro (25.9). The mean value for N in Ogun State was 21.75. The results obtained from MONRATE were compared with those obtained using the mathematical equation and were found to be the same but MONRATE was faster in computation and more accurate. It is concluded that MONRATE is a veritable tool that can be used in the execution of control programme involving mass treatment against A. lumbricoides

MONRATE: A descriptive tool for calculation and prediction of re-infection of Ascaris lumbricoides (Ascaridida: Ascarididae)

The study presents an interactive descriptive tool (MONRATE) for calculating and predicting reinfection rates and time of Ascaris lumbricoides following mass chemotherapy. The implementation was based on the theoretical equation published by Hayashi in 1977, for time-prevalence: Y=G [1-(1-X)N-R] as modified by Jong-Yil in 1983. Using the Psuedo-Code of the MONRATE tool, the calculated monthly reinfection rates (X) for the LGAs are (names are locations in Nigeria in a region predominately populated by the Yoruba speaking tribes of Nigeria whose traditional occupations are agriculture and commerce): Ewekoro (1.6 %), Odeda (2.3 %), Ado-odo/Otta (2.3 %), Ogun Waterside (3.8 %) and Obafemi/Owode (4.2 %). The mathematical mean of ‘X’ values in the study areas for Ogun State was 2.84. The calculated reinfection time (N months) for the LGAs are varied such as Ado-odo/Otta (12.7), Ogun Waterside (21.8), Obafemi/Owode (22.92), Odeda (25.45), and Ewekoro (25.9). The mean value for N in Ogun State was 21.75. The results obtained from MONRATE were compared with those obtained using the mathematical equation and found to be the same. Rev. Biol. Trop. 55 (3-4): 755-760. Epub 2007 December, 28

Towards detecting credit card frauds using Hidden Markov Model

E-commerce systems have become increasingly popular due to the widespread of internet shopping and banking. Credit card is one of the mostly used forms of payment on e-commerce platforms. However, there has been a tremendous rise in fraudulent credit card transactions, resulting to huge financial losses. In this work, a Hidden Markov Model (HMM) is proposed to design a credit card fraud detection system. Each HMM specifies the likelihood of a transaction given its sequence of previous transactions. This model is driven by a combination of K-Means and Baum Welch algorithms. A clustering process, obtained by the K-Means algorithm groups each transaction based on users’ spending profiles, where each cluster is used for different hidden states of the model. Subsequently, the Baum Welch algorithm generates a trained set of observations and calculates the probability of acceptance, which is used to detect if a current transaction is fraudulent or legitimate. This approach was implemented using PHP and was tested with a simulated dataset. Four performance metrics were used on the model which includes a Fraud Detection Rate (FDR), False Alarm Rate (FAR), Accuracy (A) and Sensitivity (S). The experimental results gave a high level of FDR and a low level of FAR, indicating that the proposed Hidden Markov Model is an effective approach for detecting credit card frauds.Keywords: Credit card, Fraud, E-commerce, Hidden Markov Model, K-Means algorithm, Baum Welch algorith