An Adaptive Feature Centric XG Boost Ensemble Classifier Model for Improved Malware Detection and Classification

Machine learning (ML) is often used to solve the problem of malware detection and classification and various machine learning approaches are adapted to the problem of malware classification; still  acquiring poor performance by the way of feature selection, and classification. To manage the issue, an efficient Adaptive Feature Centric XG Boost Ensemble Learner Classifier “AFC-XG Boost” novel algorithm is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the process of XG Boost classifier in several stages to optimize the performance. At preprocessing stage, the data set given has been noise removed, normalized and tamper removed using Feature Base Optimizer “FBO” algorithm. The FBO would normalize the data points as well as performs noise removal according to the feature values and their base information. Similarly, the performance of standard XG Boost has been optimized by adapting Feature selection using Class Based Principle Component Analysis “CBPCA” algorithm, which performs feature selection according to the fitness of any feature for different classes. Based on the selected features, the method generates regression tree for each feature considered. Based on the generated trees, the method performs classification by computing Tree Level Ensemble Similarity “TLES” and Class Level Ensemble Similarity “CLES”. Using both method computes the value of Class Match Similarity “CMS” based on which the malware has been classified. The proposed approach achieves 97% accuracy in malware detection and classification with the less time complexity of 34 seconds for 75000 sample

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security

Indeterminacy-aware prediction model for authentication in IoT.

The Internet of Things (IoT) has opened a new chapter in data access. It has brought obvious opportunities as well as major security and privacy challenges. Access control is one of the challenges in IoT. This holds true as the existing, conventional access control paradigms do not fit into IoT, thus access control requires more investigation and remains an open issue. IoT has a number of inherent characteristics, including scalability, heterogeneity and dynamism, which hinder access control. While most of the impact of these characteristics have been well studied in the literature, we highlighted “indeterminacy” in authentication as a neglected research issue. This work stresses that an indeterminacy-resilient model for IoT authentication is missing from the literature. According to our findings, indeterminacy consists of at least two facets: “uncertainty” and “ambiguity”. As a result, various relevant theories were studied in this work. Our proposed framework is based on well-known machine learning models and Attribute-Based Access Control (ABAC). To implement and evaluate our framework, we first generate datasets, in which the location of the users is a main dataset attribute, with the aim to analyse the role of user mobility in the performance of the prediction models. Next, multiple classification algorithms were used with our datasets in order to build our best-fit prediction models. Our results suggest that our prediction models are able to determine the class of the authentication requests while considering both the uncertainty and ambiguity in the IoT system