A prioritized cyber defense remediation plan is critical for effective risk management in Energy Delivery System (EDS). Due to the complexity of EDS in terms of heterogeneous nature blending Information Technology (IT) and Operation Technology (OT) and Industrial Control System (ICS), scale and critical processes tasks, prioritized remediations should be applied gradually to protect critical assets. In this work, we propose a methodology for a prioritized cyber risk remediation plan by detecting and evaluating paths to critical nodes in EDS. We propose critical nodes characteristics evaluation based on nodes’ architectural positions, a measure of centrality based on nodes’ connectivity and frequency of network traffic, as well as the controlled amount of physical loads. The paper also examines the relationship between cost models of budget allocation for the removal of vulnerabilities on critical nodes and its impact on gradual readiness.

Collaborative machine learning is a promising paradigm that allows multiple participants to jointly train a machine learning model without exposing their private datasets to other parties. Although collaborative machine learning is more privacy-friendly compared with conventional

Causality is an intriguing concept that once tamed, can have many applications. While having been widely investigated in other domains, its relevance and usefulness in the cybersecurity domain has received little attention. In this paper, we present a systematic investigation of a particular approach to causality, known as Granger causality (G-causality), in cybersecurity. We propose a framework, dubbed Cybersecurity Granger Causality (CGC), for characterizing the presence of G-causality in cyber attack rate time series and for leveraging G-causality to predict (i.e., forecast) cyber attack rates. The framework offers a range of research questions, which can be adopted or adapted to study G-causality in other kinds of cybersecurity time series data. In order to demonstrate the usefulness of CGC, we present a case study by applying it to a particular cyber attack dataset collected at a honeypot. From this case study, we draw a number of insights into the usefulness and limitations of G-causality in the cybersecurity domain.

One of the most common problems of election in Nigeria is inefficient data management. All subsequent elections were blighted by inefficient data management that resulted in violence in the country and distrust among political parties. These flaws prompted the government at d

The European Union Digital Library (EUDL) is the central repository for all content published by the different branches of the European Alliance for Innovation (EAI). EUDL also hosts or indexes content from selected partner or member organizations and affiliates.

Nowadays, some encryption schemes are not sensitive enough to plain-image, which leads to poor robustness and the scheme is vulnerability to attacks. By employing chaotic maps and cellular automata (CA), a novel image encryption algorithm is presented in this work t

Emails have been widely used in our daily life. It is important to understand user behaviors regarding email security situation assessments. However, there are very challenging and limited studies on email user behaviors. To study user security-related behaviors, we design and investigate an email

Digital forensics (DF) tools are used for post-mortem investigation of cyber-crimes. CFTT (Computer ForensicsTool Testing) Program at National Institute of Standards and Technology (NIST) has defined expectations for aDF tool’s behavior. Understanding these expectations and how DF tools work is critical for ensuring integrityof the forensic analysis results. In this paper, we consider standardization of one class of DF tools which arefor Deleted File Recovery (DFR). We design a list of canonical test file system images to evaluate a DFR tool.Via extensive experiments we find that many popular DFR tools do not satisfy some of the standards, and wecompile a comparative analysis of these tools, which could help the user choose the right tool. Furthermore,one of our research questions identifies the factors which make a DFR tool fail. Moreover, we also providecritique on applicability of the standards. Our findings is likely to trigger more research on compliance ofstandards from the researcher community as well as the practitioners.

Website fingerprinting attacks have been shown to be able to predict the website visited even if the network connection is encrypted and anonymized. These attacks have achieved accuracies as high as 92%. Mitigations to these attacks are using cover/decoy network traffic to add noise, padding to ens

The rapid inflation of software features brings inefficiency and vulnerabilities into programs, resulting in an increased attack surface with a higher possibility of exploitation. In this paper, we propose a novel framework for automated software mass customization (AMASS), which automatically ident