An adaptive behavioral-based incremental batch learning malware variants detection model using concept drift detection and sequential deep learning

Malware variants are the major emerging threats that face cybersecurity due to the potential damage to computer systems. Many solutions have been proposed for detecting malware variants. However, accurate detection is challenging due to the constantly evolving nature of the malware variants that cause concept drift. Existing malware detection solutions assume that the mapping learned from historical malware features will be valid for new and future malware. The relationship between input features and the class label has been considered stationary, which doesn't hold for the ever-evolving nature of malware variants. Malware features change dynamically due to code obfuscations, mutations, and the modification made by malware authors to change the features' distribution and thus evade the detection rendering the detection model obsolete and ineffective. This study presents an Adaptive behavioral-based Incremental Batch Learning Malware Variants Detection model using concept drift detection and sequential deep learning (AIBL-MVD) to accommodate the new malware variants. Malware behaviors were extracted using dynamic analysis by running the malware files in a sandbox environment and collecting their Application Programming Interface (API) traces. According to the malware first-time appearance, the malware samples were sorted to capture the malware variants' change characteristics. The base classifier was then trained based on a subset of historical malware samples using a sequential deep learning model. The new malware samples were mixed with a subset of old data and gradually introduced to the learning model in an adaptive batch size incremental learning manner to address the catastrophic forgetting dilemma of incremental learning. The statistical process control technique has been used to detect the concept drift as an indication for incrementally updating the model as well as reducing the frequency of model updates. Results from extensive experiments show that the proposed model is superior in terms of detection rate and efficiency compared with the static model, periodic retraining approaches, and the fixed batch size incremental learning approach. The model maintains an average of 99.41% detection accuracy of new and variants malware with a low updating frequency of 1.35 times per month

Investigating the Quality of local E-Government Websites Using Mixed Techniques: Web Diagnostic Tools and Manual Investigation

Abstract-Most people today cannot stand without the internet. It provides access to many things like news, email, shopping, and entertainment, at anytime and anywhere. Governments around the world have adopted internet as a channel to introduce their information and services to citizens, businesses and other government sectors. The government websites work as an interface between the government and people. It helps in introducing the concerned government agency/ department to citizens. This interface should have enough content elements, design elements, and reasonable speed to fulfil the citizens' demands. It also has to support the assistive technology to be accessible for people with disabilities. This paper investigates the status of current local government website at the district level in India. It tries to find out whether these websites comply with the guidelines for websites, and have the right elements. Mixed techniques of web diagnostic tools and manual investigation have been used in this study. The results show that local the government website at the district level in India does not have many important elements and components required for government website. It needs to be improved to serve the citizens better and allow them to have the best out of the best

Investigating the quality of local e-government websites using mixed techniques: Web diagnostic tools and manual Investigation

Most people today cannot stand without the internet. It provides access to many things like news, email, shopping, and entertainment, at anytime and anywhere. Governments around the world have adopted internet as a channel to introduce their information and services to citizens, businesses and other government sectors. The government websites work as an interface between the government and people. It helps in introducing the concerned government agency/ department to citizens. This interface should have enough content elements, design elements, and reasonable speed to fulfil the citizens� demands. It also has to support the assistive technology to be accessible for people with disabilities. This paper investigates the status of current local government website at the district level in India. It tries to find out whether these websites comply with the guidelines for websites, and have the right elements. Mixed techniques of web diagnostic tools and manual investigation have been used in this study. The results show that local the government website at the district level in India does not have many important elements and components required for government website. It needs to be improved to serve the citizens better and allow them to have the best out of the best

Experimental evaluation of effectiveness of E-Government websites

Usability of the e-government website is a crucial factor that should be considered for improving effectiveness, efficiency and satisfaction in services to citizens. In this study the effectiveness of e-government website will be measured using usability testing approach. The aim of this study is to support the mission of the government Website by evaluating the existing design of government Web site. The results indicated that there is an urgent need to improve the usability of e-government website in order to be more effective for citizen

Building a tool to extract data from users and potential users of e-government

The demand to extract data about e-government users and potential users by the decision makers is increasing day by day. They need to know more about citizens attitude, skills, and willingness to use e-government online services to segment citizens into different groups and build the citizens profiles. Inspired also by the low rate of use of online government services, a tool was constructed and tested to address this gap. The tool is intended to extract the required data from the existing users and potential users of e-government. It will also determine the key factors with highest impact on e-government use and satisfaction, in order to select those elements that should be prioritized for improvement. The possible level of analysis and knowledge gained using this tool will also be shown. The tool focuses on: discovering the gap between interests in e-government (including attitudes, preferences and intentions to use) and actual use of e-government; the ability to use e-government; user satisfaction with e-government and future e-government developments (motivators and barriers for future use). The tool showed a high reliability score. Moreover, the tool is clear, easy to use and cost effective. The results of analysed data showed that the extracted data was meaningful, current, and accurate. It will help the decision makers to understand the users abilities and requirements, and improve the online services of e-government

Investigating the user profile of potential users of e-government in Yemen

Understanding the citizens needs by e-government decision makers offers great opportunities to make communication more effective and efficient, to infer and predict citizens behaviour and to even influence behaviour. The decision makers need to know more about citizens attitude, skills, and willingness to use e-government online services.The aim of this paper is to investigate the user profile of e-government in Yemen. We built a tool to collect the data online from internet users.The results will help the decision makers to understand the users needs and plan to improve the online services of e-government to its users

Visualization and deep-learning-based malware variant detection using OpCode-level features

Malicious software (malware) is a major threat to the systems and networks’ security. Although anti-malware products are used to protect systems and networks against malware attacks, obfuscated malware that is capable of evading analysis and detection by anti-malware software have become prevalent. Therefore, how to detect and remove obfuscated malware from the systems has become a major concern. In this research work, we propose a semi-supervised approach that integrates deep learning, feature engineering, image transformation and processing techniques for obfuscated malware detection. We validated the proposed approach through experiments and compared it with existing approaches. With 99.12% accuracy in detecting obfuscated malware detection, the proposed approach substantially outperformed the other approaches