The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with using two entropy-based approaches (i.e., information gain (IG) and gain ratio (GR)) to select and extract relevant features in various ratios. Then, mathematical set theory (union and intersection) is used to extract the best features. The model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms: Bagging, Multilayer Perception, J48, and IBk. Our approach has resulted in 11 and 28 relevant features (out of 86) using the intersection and union, respectively, on IoTID20 and resulted 15 and 25 relevant features (out of 41) using the intersection and union, respectively, on NSL-KDD. We have further compared our approach with other state-of-the-art studies. The comparison reveals that our model is superior and competent, scoring a very high 99.98% classification accuracy.

The purpose of this article is to clarify current and widespread misconceptions about the properties of blockchain technologies and to describe challenges and avenues for correct and trustworthy design and implementation of distributed ledger system (DLS) or Technology (DLT). The authors contrast the properties of a blockchain with desired, however emergent, properties of a DLS, which is a complex and distributed system. They point out and justify, with facts and analysis, current misconceptions about the blockchain and DLSs. They describe challenges that these systems will need to address and possible solution avenues for achieving trustworthiness. Many of the statements that have appeared on the internet, news and academic articles, such as immutable ledger and exact copies, may be misleading. These are desired emergent properties of a complex system, not assured properties. It is well-known within the distributed systems and critical software community that it is extremely hard to prove that a complex system correctly and completely implements emergent properties. Further research and development for trustworthy DLS design and implementation is needed, both practical and theoretical. This is the first known published attempt at describing current misconceptions about blockchain technologies. Further collaborative work, discussions, potential solutions, evaluations, resulting publications and verified reference implementations are needed to ensure DLTs are safe, secure, and trustworthy. Interdisciplinary teams with members from academia, business and industry, and from disciplines such as business, entrepreneurship, theoretical and practical computer science, cybersecurity, finance, mathematics and statistics, must be formed. Such teams must collaborate with the objective of developing strategies and techniques for ensuring the correctness and security of future DLSs in which our society may become dependent. The value and originality of this article is twofold: the disproving, through fact collection and systematic analysis, of current misconceptions about the properties of the blockchain and DLSs, and the discussion of challenges to achieving adequate trustworthiness along with the proposal of general avenues for possible solutions.

Due to the characteristics and connectivity of today’s critical infrastructure systems, cyber-attacks on these systems are currently difficult to prevent in an efficient and sustainable manner. Prevention and mitigation strategies need accurate identification and evaluation of: system vulnerabilities, potential threats and attacks, and applicable hardening measures. Furthermore, the ability to prioritize hardening measures based on accurate assessments of risk is needed. In addition, the consideration of the availability, applicability, and cost of potential mitigation strategies is also needed. To address this challenge we created HESTIA: High-level and Extensible System for Training and Infrastructure risk Assessment. In this article we present a formal model of the HESTIA system. We then also present a formal verification of the HESTIA semantic model.

I explore Neoliberalism and its effects on Computing Education and Research. I begin the exploration by analyzing the impact of declining state funding for higher education. I discuss the following mechanisms used by higher-ed institutions to cope with declines in state support: managerial culture, increased students’ fees, privatization of research, adjunctification of teaching professors, audit culture, and Artificial Intelligence agents as teachers. I then explore the commodified state of education and research resulting in a sovereign student, debasement of academic standards, and a perversion of academic subjectivity. I discuss how the commodification results in a dehumanization of faculty and students which produces a compliance culture, loss of professional and pedagogical identities, loss of autonomy and public trust, and marginalization of equity, civic engagement, and critical inquiry. I analyze the reasons underlying a successful permeation of Neoliberalism in academia including portraying Neoliberalism as a class war, categorization of STEM fields as luxury commodities, exploring exploitation of foreign workers, and commenting about the academic Stockholm syndrome. I discuss about a super-cycle of suffering-exploitation caused by Neoliberal pressures. I conclude the discussion with some strategies that can be used by computing researchers and practitioners to salvage the Neoliberal situation.

Multiplayer online video games are a multibillion-dollar industry, to which widespread cheating presents a significant threat. Game designers compromise on game security to meet demanding performance targets, but reduced security increases the risk of potential malicious exploitation. To mitigate this risk, game developers implement alternative security sensors. The alternative sensors themselves become a liability due to their intrusive and taxing nature. Online multiplayer games with real-time gameplay are known to be difficult to secure due to the cascading exponential nature of many-many relationships among the components involved. Behavior-based security sensor schemes, or referees (a trusted third party), could be a potential solution but require frameworks to obtain the game state information they need. We describe our Trust-Verify Game Protocol (TVGP), which is a sensor protocol intended for low-trust environments and designed to provide game state information to help support behavior-based cheat-sensing detection schemes. We argue TVGP is an effective solution for applying an independent trusted referee capability to trust-lacking subdomains and demands high-performance requirements. Our experimental results validate high efficiency and performance standards for TVGP. We identify and discuss the operational domain assumptions of the TVGP validation testing presented here.

In the context of Internet of Things (IoT) intelligent systems, the latest research regarding delegation using an access control model has gained attention, reflecting the need for models to support more functionalities in relation to hierarchical delegation. With respect to delegation procedures within access control, issues arise after delegation concerning the permissions to others with respect to revocation. Redundancy and conflict arising from delegation can occur depending on the delegation policies used within the hierarchical structure. This article discusses implementation of positive delegation represented by “YES” and negative delegation represented by “NO”. Furthermore, we also consider permission to delegate positively and negatively represented by (YES and NO). These challenges are addressed by creating additional features in a hierarchical policy model (HPol). The implementation was created using Python (ver. 3.10) code to verify the advantages of the approach, through experimentation under different scenarios. The model also has the capability to manage and adapt features of the Internet of Things (IoT) to a blockchain architecture, enhancing security and verification during the delegation process and increasing the scalability of Internet of Things (IoT) intelligent environment systems.