Design and Analysis of Anomaly Detection and Mitigation Schemes for Distributed Denial of Service Attacks in Software Defined Network. An Investigation into the Security Vulnerabilities of Software Defined Network and the Design of Efficient Detection and Mitigation Techniques for DDoS Attack using Machine Learning Techniques

Software Defined Networks (SDN) has created great potential and hope to overcome the need for secure, reliable and well managed next generation networks to drive effective service delivery on the go and meet the demand for high data rate and seamless connectivity expected by users. Thus, it is a network technology that is set to enhance our day-to-day activities. As network usage and reliance on computer technology are increasing and popular, users with bad intentions exploit the inherent weakness of this technology to render targeted services unavailable to legitimate users. Among the security weaknesses of SDN is Distributed Denial of Service (DDoS) attacks. Even though DDoS attack strategy is known, the number of successful DDoS attacks launched has seen an increment at an alarming rate over the last decade. Existing detection mechanisms depend on signatures of known attacks which has not been successful in detecting unknown or different shades of DDoS attacks. Therefore, a novel detection mechanism that relies on deviation from confidence interval obtained from the normal distribution of throughput polled without attack from the server. Furthermore, sensitivity analysis to determine which of the network metrics (jitter, throughput and response time) is more sensitive to attack by introducing white Gaussian noise and evaluating the local sensitivity using feed-forward artificial neural network is evaluated. All metrics are sensitive in detecting DDoS attacks. However, jitter appears to be the most sensitive to attack. As a result, the developed framework provides an avenue to make the SDN technology more robust and secure to DDoS attacks

Removal of Colour from Textile Wastewater Generated by Local Dyers in Maiduguri using Millet Straw and Rice Husk Adsorbents

This study attempts to investigate the suitability of agricultural by-products based activated carbon as adsorbents for the treatment of textile wastewater. The chemical treatment adopted in the study is simple and qualitative. Two low– cost adsorbents namely: Rice husk (RH), and Millet straw (MS) were studied. The initial concentration of Colour in the wastewater was 25570 pt/co. Colour removal with the adsorbents were investigated under two conditions namely: constant adsorption time with varying dosage of adsorbents and constant adsorbent dosage with varying adsorption time. For MS, the optimum removal of colour at constant adsorption time of 2hrs was 95.9% and 94.5% at constant dosage consequently. For RS, the optimum removal of colour at constant adsorption time of 2hrs was 94.5% and 75.8% at constant dosage. Maximum saturation was attained for both adsorbents at 7 hrs contact period. The results of the adsorption study, shows that RH and MS adsorbents can serve as coagulants for removing colour from textile wastewater. The MS powder in particular, was discovered to have a high adorptive capacity. Keywords:Textile wastewater, colour removal, Rice husk, Millet straw,adsorbents

COCONUT HUSK CHAR BIOSORPTIVITY IN HEAVY METAL DIMINUTION FROM CONTAMINATED SURFACE WATER

Applicability of coconut husk char in heavy metal removal was examined in the study. The surface morphology and elemental compositions of the char was investigated with SEM-EDX machine. Heavy metals sorption on 100 g of the char dosage was studied under five different contact times in the column experiment. Isotherm and kinetic models were the probing tools for biosorption mechanism prediction. Results indicated removal efficiency for chromium, cobalt, cadmium, aluminum and arsenic at 60 mins contact time were 72, 80, 86, 89 and 100 % respectively. Contaminate removal depends on metal involved and sorption contact time. Adsorption data are fitted well into Freundlich isotherm model (R2 > 0.92). Pseudo kinetic second order well described the adsorption process, with most R2 values ≥ 0.94. Coconut husk char is an effective biosorbent in sequestration of arsenic, cadmium, aluminum and cobalt in contaminated surface water

Kinetic Study of Water Contaminants Adsorption by Bamboo Granular Activated and Non-Activated Carbon

The adsorptive capacity of metal ions from surface water with activated and non-activated carbon derived from bamboo was investigated. The validation of adsorption kinetics of Cl, PO4 and Pb was done by pseudo-first and second order model while adsorption isotherms was proved by Langmuir and Freundlich isotherm model for activated and non- activated bamboo granular carbon. Generally, the amount of metal ions uptake increases with time and activation levels and the pH of bamboo granular carbon increase with activation. Similarly, the pore space of the activated carbon also increases with activation levels. The correlation coefficients (R2 ) show that the pseudo-second order model gave a better fit to the adsorption process with 0.9918 as the least value and 1.00 as the highest value as compared with the pseudo-first order with 0.813 as the highest value and 0 as the least. The Freundlich isotherm was more favorable when compared with the Langmuir isotherm in determining the adsorptive capacity of bamboo granular activated carbon. The study has shown that chemical activation increases the pore space, surface area and the pH of bamboo granular carbon which ultimately increases the adsorption rate of metal ions in the contaminated surface water

COCONUT HUSK CHAR BIOSORPTIVITY IN HEAVY METAL DIMINUTION FROM CONTAMINATED SURFACE WATER

Applicability of coconut husk char in heavy metal removal was examined in the study. The surface morphology and elemental compositions of the char was investigated with SEM-EDX machine. Heavy metals sorption on 100 g of the char dosage was studied under five different contact times in the column experiment. Isotherm and kinetic models were the probing tools for biosorption mechanism prediction. Results indicated removal efficiency for chromium, cobalt, cadmium, aluminum and arsenic at 60 mins contact time were 72, 80, 86, 89 and 100 % respectively. Contaminate removal depends on metal involved and sorption contact time. Adsorption data are fitted well into Freundlich isotherm model (R2 > 0.92). Pseudo kinetic second order well described the adsorption process, with most R2 values ≥ 0.94. Coconut husk char is an effective biosorbent in sequestration of arsenic, cadmium, aluminum and cobalt in contaminated surface water

Mapping of river waterquality using inverse distance weighted interpolation in Ogun-Osun river basin, Nigeria

Sustainable management of water resources involves inventory, conservation, efficient utilization, and quality management. Although, activities relating to quantity assessment and management in terms of river discharge and water resources planning are given attention at the basin level, water quality assessment are still being done at specific locations of major concern. The use of Geographical Information System (GIS) based water quality information system and spatial analysis with Inverse Distance Weighted interpolation enabled the mapping of water quality indicators in Ogun and Ona catchment of Ogun-Osun River Basin, Nigeria. Using 27 established gauging stations as sampling locations, water quality indicators were monitored over 12 months covering full hydrological season. Maps of seasonal variations in 10 water quality indicators as impacted by land-use types were produced. This ensured that trends of specific water quality indicator and diffuse pollution characteristics across the basin were better presented with the variations shown along the river courses than the traditional line graphs. The production of water quality maps will improve monitoring, enforcement of standards and regulations towards better pollution management and control. This strategy holds great potential for real time monitoring of water quality in the basin with adequate instrumentation

Behavioral Study of Software-Defined Network Parameters Using Exploratory Data Analysis and Regression-Based Sensitivity Analysis

To provide a low-cost methodical way for inference-driven insight into the assessment of SDN operations, a behavioral study of key network parameters that predicate the proper functioning and performance of software-defined networks (SDNs) is presented to characterize their alterations or variations, given various emulated SDN scenarios. It is standard practice to use simulation environments to investigate the performance characteristics of SDNs, quantitatively and qualitatively; hence, the use of emulated scenarios to typify the investigated SDN in this paper. The key parameters studied analytically are the jitter, response time and throughput of the SDN. These network parameters provide the most vital metrics in SDN operations according to literature, and they have been behaviorally studied in the following popular SDN states: normal operating condition without any incidents on the SDN, hypertext transfer protocol (HTTP) flooding, transmission control protocol (TCP) flooding, and user datagram protocol (UDP) flooding, when the SDN is subjected to a distributed denial-of-service (DDoS) attack. The behavioral study is implemented primarily via univariate and multivariate exploratory data analysis (EDA) to characterize and visualize the variations of the SDN parameters for each of the emulated scenarios, and linear regression-based analysis to draw inferences on the sensitivity of the SDN parameters to the emulated scenarios. Experimental results indicate that the SDN performance metrics (i.e., jitter, latency and throughput) vary as the SDN scenario changes given a DDoS attack on the SDN, and they are all sensitive to the respective attack scenarios with some level of interactions between them

Design Considerations and Data Communication Architecture for National Animal Identification and Traceability System in Nigeria

Wireless communication systems and their supporting infrastructure continue to play a vital role in contemporary daily activities. Due to the unprecedented levels of interconnectivity achieved between wireless devices in recent times, new insights and paradigms for the robust deployment and better utilization of wireless communication systems are always of interest to many countries for socio-economic development. Present-day Nigeria is faced with the challenge of insurgencies whose financing has been linked to proceeds from livestock theft or rustling according to many scholarly works and news reports. To mitigate rustling and the sales of stolen livestock via identification and traceability from ‘herds to markets to homes’, the design considerations and data communication architecture for national animal identification and traceability system in Nigeria (NAITS) is proposed in this paper for safer and improved livestock farming and production. Particularly, technical insight into the co-use of radio frequency identification (RFID) and fifth-generation new radio (5G NR) technologies for the implementation of NAITS are highlighted and discussed in this paper for a prospective technological policy plan and development in Nigeria

Sensitivity Analysis of Population in the Generation of Hazardous and Non-Harzardous Wastes, and Gas from Dumpsites of Ogbomosoland in Nigeria

This paper applies the principles of system dynamics modeling in studying the pattern of population changes and the corresponding non-hazardous wastes and gas being generated from the dumpsites of Ogbomosoland, Nigeria. The five (5) Local government Areas (LGAs) of Ogbomosoland were categorized as Urban (Ogbomoso North and Ogbomoso South) and Rural (Oriire, Ogo Oluwa and Suurulere) based on the size, population of residents, consumption pattern and socio-economic activities of the area. A sensitivity analysis of the simulated variables i.e the population, wastes and gas, was performed by employing the developed model results. Findings showed that the wastes and gas increased with the increased population in the 1000 years period. Also, gas production exceeds wastes generation rates for the rural LGAs in all cases. After a 25 years benchmark, when the simulated population of the urban and rural LGAs are respectively 303,411 and 344,735, the rates of waste generation are 3.33x106 and 6.22 x106 m 3 , while the corresponding rates of gas production is 2.44x103 and 6.47x103 m 3 in same order. The study concludes that wastes and gas generation from dumpsites are highly sensitive to population growth. It also concluded that the rate of gas generation is higher in organic wastes of the rural LGAs. The maximum population permissible in the model is 300,000 thus design of full-fledge landfills is recommended to replace the existing dumpsites in the study area

Detection and Classification of DDoS Flooding Attacks on Software-Defined Networks: A Case Study for the Application of Machine Learning

Software-defined networks (SDNs) offer robust network architectures for current and future Internet of Things (IoT) applications. At the same time, SDNs constitute an attractive target for cyber attackers due to their global network view and programmability. One of the major vulnerabilities of typical SDN architectures is their susceptibility to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks can render SDN controllers unavailable to their underlying infrastructure, causing service disruption or a complete outage in many cases. In this paper, machine learning-based detection and classification of DDoS flooding attacks on SDNs is investigated using popular machine learning (ML) algorithms. The ML algorithms, classifiers and methods investigated are quadratic discriminant analysis (QDA), Gaussian Naïve Bayes (GNB), k -nearest neighbor (k-NN), and classification and regression tree (CART). The general principle is illustrated through a case study, in which, experimental data (i.e. jitter, throughput, and response time metrics) from a representative SDN architecture suitable for typical mid-sized enterprise-wide networks is used to build classification models that accurately identify and classify DDoS flooding attacks. The SDN model used was emulated in Mininet and the DDoS flooding attacks (i.e. hypertext transfer protocol (HTTP), transmission control protocol (TCP), and user datagram protocol (UDP) attacks) have been launched on the SDN model using low orbit ion cannon (LOIC). Although all the ML methods investigated show very good efficacy in detecting and classifying DDoS flooding attacks, CART demonstrated the best performance on average in terms of prediction accuracy (98%), prediction speed ( 5.3×105 observations per second), training time (12.4 ms), and robustness