A holistic based digital forensic readiness framework for Zenith Bank, Nigeria

The advancement of internet has made many business organizations conduct their operation automatically, in effect its open a possibly dangerous unforeseen information security incidents of both illegal and civil nature. Therefore, if any organization does’t arrange themselves for such instances, it’s likely that vital significant digital evidence will be damage. In other word an organization should has a digital forensic readiness framework (DFR). DFR is the capacity of anyassociation to exploit its prospective to use digital evidence whilst minimizing the cost of investigation. Subsequently, in order to prepare organizations for incident responds, the application of digital forensic readiness policies and procedures is important. Contemporary lack of forensic skills is one of the factors that make organizations reluctant to implement digital forensics. This project propose a holistic-based framework of DFR and investigate how it can be applied to Zenith Bank Plc. This paper surveys existing frameworks to identify the best-suited practical components for Zenith Bank’s operational unit

Hybrid method on clickjacking detection and prevention in modern advertisements

In modern advertisements, clickjacking attacks can be delivered through a vulnerability in web application. To overcome this, web application security is required that will prevent malvertisement. In this study, prevention of clickjacking in the modern web advertisements are implemented. Vulnerability checks on the potentially malicious website were conducted. Implementation of hybrid prevention method of clickjacking into new developed website were carried out. Among top 500 websites, 50 websites were chosen as a dataset in this study out of which 4 case studies were selected. Website with server privileges were required to implement the hybrid prevention method, consisting opacity, Z-Index and X-Frame option policy. A new website was developed to satisfy the requirements for the method implementation. The results show, among 50 selected websites, about 19 websites were vulnerable to clickjacking. When the hybrid prevention method were implemented in the developed website, it increases the security by mitigating the vulnerability of web application to clickjacking attack

Classification of online grooming on chat logs using two term weighting schemes

Due to the growth of Internet, it has not only become the medium for getting information, it has also become a platform for communicating. Social Network Service (SNS) is one of the main platform where Internet users can communicate by distributing, sharing of information and knowledge. Chatting has become a popular communication medium for Internet users whereby users can communicate directly and privately with each other. However, due to the privacy of chat rooms or chatting mediums, the content of chat logs is not monitored and not filtered. Thus, easing cyber predators preying on their preys. Cyber groomers are one of cyber predators who prey on children or minors to satisfy their sexual desire. Workforce expertise that involve in intelligence gathering always deals with difficulty as the complexity of crime increases, human errors and time constraints. Hence, it is difficult to prevent undesired content, such as grooming conversation, in chat logs. An investigation on two term weighting schemes on two datasets are used to improve the content-based classification techniques. This study aims to improve the content-based classification accuracy on chat logs by comparing two term weighting schemes in classifying grooming contents. Two term weighting schemes namely Term Frequency – Inverse Document Frequency – Inverse Class Space Density Frequency (TF.IDF.ICSdF) and Fuzzy Rough Feature Selection (FRFS) are used as feature selection process in filtering chat logs. The performance of these techniques were examined via datasets, and the accuracy of their result was measured by Support Vector Machine (SVM). TF.IDF.ICSdF and FRFS are judged based on accuracy, precision, recall and F score measurement

Comparative study on feature selection techniques in intrusion detection systems using ensemble classifiers

Network usage has become a paramount aspect of life, therefore, securing our networks is crucial. The world is experiencing a rapid breakthrough of internet usage, most especially with the concept of internet of things (IoT), now internet of everything (IoE. ). Real network data is rowdy, noisy and inconsistent. These issues with the data influences the performance of intrusion detection systems (IDS) and develop manifold of false alarms. Feature selection technique is used to remove the inconsistent and rowdy data from a large data set and presents a refined set of data. This research work adopts the use of two distinct feature selection technique in parallel: ReliefF ranking and particle swarm optimization, using linear discriminant analysis (LDA) and logistic regression (LR) as the machine learners, to first clean the data, train the classifiers, and subsequently classify new instances. The results showed that, the combination of the ReliefF with the ensemble machine learning (Linear Discriminant Analysis and Logistic Regression) has a higher classification accuracy of 99.7% compared to the Particle swarm optimization (PSO) which attained an accuracy of 98.6%

Integration of SQL injection prevention methods

In everybody’s life including the organisations, database plays a very important role, since today everything is connected via the Internet. There is a need for a database that helps organisations to organise, sort and manage the data and ensure that the data a user receives and sends via the database mean is secure, since the database stores almost everything such as banking details including user ID and password. Make this data really valuable and confidential for us and therefore security is really important for the database. In this age, SQL Injection database attacks are increasingly common. The hackers attempt to steal an individual’s valuable data through the SQL Injection Attack mean by using malicious query on the application, hence revealing an efficient individual data. Therefore the best SQL Injection Prevention technique is needed to safeguard individual data against hackers being stolen. This paper compares two types of SQL Injection using the SQL pattern matching database system attack (SQLPMDS) and a SQL injection union query attacks prevention using tokenisation technique (SIUQAPTT) that allows Database Administrator to select the best and most effective SQL Injection Prevention method for their organisation. Preventing SQL Injection Attack from occurring that would ultimately lead to no user data loss. The results were obtained by comparing it to the results of the SQL injection attack query on whether the attack was blocked or not by two prevention techniques, SQL pattern matching database system attacks and SQL injecting union query attacks prevention using website tokenisation techniques. The conclusion is that the best method of prevention is the SQL pattern that matches database system attacks

Intrusion alert reduction based on unsupervised and supervised learning algorithms

Security and protection of information is an ever-evolving process in the field of information security. One of the major tools of protection is the Intrusion Detection Systems (IDS). For so many years, IDS have been developed for use in computer networks, they have been widely used to detect a range of network attacks; but one of its major drawbacks is that attackers, with the evolution of time and technology make it harder for IDS systems to cope. A sub-branch of IDS-Intrusion Alert Analysis was introduced into the research system to combat these problems and help support IDS by analyzing the alert triggered by the IDS. Intrusion Alert analysis has served as a good support for IDS systems for many years but also has its own short comings which are the amount of the voluminous number of alerts produced by IDS systems. From years of research, it has been observed that majority of the alerts produced are undesirables such as duplicates, false alerts, etc., leading to huge amounts of alerts causing alert flooding. This research proposed the reduction alert by targeting these undesirable alerts through the integration of supervised and unsupervised algorithms and approach. The research first selects significant features by comparing two feature ranking techniques this targets duplicates, low priority and irrelevant alert. To achieve further reduction, the research proposed the integration of supervised and unsupervised algorithms to filter out false alerts. Based on this, an effective model was gotten which achieved 94.02% reduction rate of alerts. Making use of the dataset ISCX 2012, experiments were conducted and the model with the highest reduction rate was chosen. The model was evaluated against other experimental results and benchmarked against a related work, it also improved on the said related work

Oil well detection system for seismic surveying based on internet of things (IoT)

Seismic Surveying is a geophysical survey that was conducted to measure the physicals principle in earth's geography like magnetic, gravitational and thermal. There are several simulations that have been produced to be used in oil and gas field, such as Petrel by Schlumberger and ECLIPSE. however, this simulation is confidential and cannot be used by individuals outside the company. Therefore, some of petroleum geologists are not able to use the simulations in their geology analysis. This issue is also experienced by students studying in this field as they are not able to access any simulations. Hence, making them not able to experience the real environment of the process for their future used. The existing software also do not analyse real time data, which will be covered in this project. Oil Well Detection System for Seismic Surveying is a web-based system that aims to analyse data for seismic surveying to give user better understanding on the process before conducting it in real life. This system also uses real time data in order to generate the result for the simulation. Users are able to generate the data in numeric data sets, 2D or 3D images. Notifications will be sent to users whenever there are data generating the best limestone result. This is to make sure that users have the best field for new oil reservoir and comparing the data of seismic surveying to make sure the best region to drill. The sensors technology will be used to detect the elements in limestone and send the data to user's smartphones. Prototype Model Methodology is used throughout the development process alongside Firebase Cloud Database for storing information. This project will help petroleum geologists and students to experience the real time simulation in order to have better understanding about seismic surveying before conducting it in real life. They also can use it as study purposes as it is beneficial to students

Visual analytics design for students assessment representation based on supervised learning algorithms

Visual Analytics is very effective in many applications especially in education field and improved the decision making on enhancing the student assessment. Student assessment has become very important and is identified as a systematic process that measures and collects data such as marks and scores in a manner that enables the educator to analyze the achievement of the intended learning outcomes. The objective of this study is to investigate the suitable visual analytics design to represent the student assessment data with the suitable interaction techniques of the visual analytics approach. sheet. There are six types of analytical models, such as the Generalized Linear Model, Deep Learning, Decision Tree Model, Random Forest Model, Gradient Boosted Model, and Support Vector Machine were used to conduct this research. Our experimental results show that the Decision Tree Models were the fastest way to optimize the result. The Gradient Boosted Model was the best performance to optimize the result

Integration of PSO and K-means clustering algorithm for structural-based alert correlation model

Network-based Intrusion Detection Systems (NIDS) will trigger alerts as notifications of abnormal activities detected in computing and networking resources. As Distributed Denial-of-Service (DDOS) attacks are getting more sophisticated, each attack consists of a series of events which in turn trigger a series of alerts. However, the alerts are produced in a huge amount, of low quality and consist of repeated and false positive alerts. This requires clustering algorithm to effectively correlate the alerts for identifying each unique attack. Soft computing including bio-inspired algorithms are explored to optimally cluster the alerts. Therefore, this study investigates the effects of bio-inspired algorithm in alert correlation (AC) model. Particle Swarming Optimization (PSO) is integrated with K-Means clustering algorithm to conduct structural-based AC. It was tested on the benchmarked DARPA 2000 dataset. The efficiency of the AC model was evaluated using clustering accuracy, error rate and processing time measurements. Surprisingly, the experimental results show that K-Means algorithm works better than the integration of PSO and K-Means. K-Means gives 99.67% clustering accuracy while PSO and K-Means gives 92.71% clustering accuracy. This indicates that a single clustering algorithm is sufficient for optimal structural-based AC instead of integrated PSO and K-Means

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus,Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction