533 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

    Get PDF

    Undergraduate Catalog of Studies, 2023-2024

    Get PDF

    Securing the Internet of Things: A Study on Machine Learning-Based Solutions for IoT Security and Privacy Challenges

    Get PDF
    The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy

    Undergraduate Catalog of Studies, 2022-2023

    Get PDF

    A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks

    Full text link
    Cyber threat attribution is the process of identifying the actor of an attack incident in cyberspace. An accurate and timely threat attribution plays an important role in deterring future attacks by applying appropriate and timely defense mechanisms. Manual analysis of attack patterns gathered by honeypot deployments, intrusion detection systems, firewalls, and via trace-back procedures is still the preferred method of security analysts for cyber threat attribution. Such attack patterns are low-level Indicators of Compromise (IOC). They represent Tactics, Techniques, Procedures (TTP), and software tools used by the adversaries in their campaigns. The adversaries rarely re-use them. They can also be manipulated, resulting in false and unfair attribution. To empirically evaluate and compare the effectiveness of both kinds of IOC, there are two problems that need to be addressed. The first problem is that in recent research works, the ineffectiveness of low-level IOC for cyber threat attribution has been discussed intuitively. An empirical evaluation for the measure of the effectiveness of low-level IOC based on a real-world dataset is missing. The second problem is that the available dataset for high-level IOC has a single instance for each predictive class label that cannot be used directly for training machine learning models. To address these problems in this research work, we empirically evaluate the effectiveness of low-level IOC based on a real-world dataset that is specifically built for comparative analysis with high-level IOC. The experimental results show that the high-level IOC trained models effectively attribute cyberattacks with an accuracy of 95% as compared to the low-level IOC trained models where accuracy is 40%.Comment: 20 page

    Majority Voting Approach to Ransomware Detection

    Full text link
    Crypto-ransomware remains a significant threat to governments and companies alike, with high-profile cyber security incidents regularly making headlines. Many different detection systems have been proposed as solutions to the ever-changing dynamic landscape of ransomware detection. In the majority of cases, these described systems propose a method based on the result of a single test performed on either the executable code, the process under investigation, its behaviour, or its output. In a small subset of ransomware detection systems, the concept of a scorecard is employed where multiple tests are performed on various aspects of a process under investigation and their results are then analysed using machine learning. The purpose of this paper is to propose a new majority voting approach to ransomware detection by developing a method that uses a cumulative score derived from discrete tests based on calculations using algorithmic rather than heuristic techniques. The paper describes 23 candidate tests, as well as 9 Windows API tests which are validated to determine both their accuracy and viability for use within a ransomware detection system. Using a cumulative score calculation approach to ransomware detection has several benefits, such as the immunity to the occasional inaccuracy of individual tests when making its final classification. The system can also leverage multiple tests that can be both comprehensive and complimentary in an attempt to achieve a broader, deeper, and more robust analysis of the program under investigation. Additionally, the use of multiple collaborative tests also significantly hinders ransomware from masking or modifying its behaviour in an attempt to bypass detection.Comment: 17 page

    Next-Generation Industrial Control System (ICS) Security:Towards ICS Honeypots for Defence-in-Depth Security

    Get PDF
    The advent of Industry 4.0 and smart manufacturing has led to an increased convergence of traditional manufacturing and production technologies with IP communications. Legacy Industrial Control System (ICS) devices are now exposed to a wide range of previously unconsidered threats, which must be considered to ensure the safe operation of industrial processes. Especially as cyberspace is presenting itself as a popular domain for nation-state operations, including against critical infrastructure. Honeypots are a well-known concept within traditional IT security, and they can enable a more proactive approach to security, unlike traditional systems. More work needs to be done to understand their usefulness within OT and critical infrastructure. This thesis advances beyond current honeypot implementations and furthers the current state-of-the-art by delivering novel ways of deploying ICS honeypots and delivering concrete answers to key research questions within the area. This is done by answering the question previously raised from a multitude of perspectives. We discuss relevant legislation, such as the UK Cyber Assessment Framework, the US NIST Framework for Improving Critical Infrastructure Cybersecurity, and associated industry-based standards and guidelines supporting operator compliance. Standards and guidance are used to frame a discussion on our survey of existing ICS honeypot implementations in the literature and their role in supporting regulatory objectives. However, these deployments are not always correctly configured and might differ from a real ICS. Based on these insights, we propose a novel framework towards the classification and implementation of ICS honeypots. This is underpinned by a study into the passive identification of ICS honeypots using Internet scanner data to identify honeypot characteristics. We also present how honeypots can be leveraged to identify when bespoke ICS vulnerabilities are exploited within the organisational network—further strengthening the case for honeypot usage within critical infrastructure environments. Additionally, we demonstrate a fundamentally different approach to the deployment of honeypots. By deploying it as a deterrent, to reduce the likelihood that an adversary interacts with a real system. This is important as skilled attackers are now adept at fingerprinting and avoiding honeypots. The results presented in this thesis demonstrate that honeypots can provide several benefits to the cyber security of and alignment to regulations within the critical infrastructure environment

    Ransomware Simulator for In-Depth Analysis and Detection: Leveraging Centralized Logging and Sysmon for Improved Cybersecurity

    Get PDF
    Abstract Ransomware attacks have become increasingly prevalent and sophisticated, posing significant threats to organizations and individuals worldwide. To effectively combat these threats, security professionals must continuously develop and adapt their detection and mitigation strategies. This master thesis presents the design and implementation of a ransomware simulator to facilitate an in-depth analysis of ransomware Tactics, Techniques, and Procedures (TTPs) and to evaluate the effectiveness of centralized logging and Sysmon, including the latest event types, in detecting and responding to such attacks. The study explores the advanced capabilities of Sysmon as a logging tool and data source, focusing on its ability to capture multiple event types, such as file creation, process execution, and network traffic, as well as the newly added event types. The aim is to demonstrate the effectiveness of Sysmon in detecting and analyzing malicious activities, with an emphasis on the latest features. By focusing on the comprehensive aspects of a cyber-attack, the study showcases the versatility and utility of Sysmon in detecting and addressing various attack vectors. The ransomware simulator is developed using a PowerShell script that emulates various ransomware TTPs and attack scenarios, providing a comprehensive and realistic simulation of a ransomware attack. Sysmon, a powerful system monitoring tool, is utilized to monitor and log the activities associated with the simulated attack, including the events generated by the new Sysmon features. Centralized logging is achieved through the integration of Splunk Enterprise, a widely used platform for log analysis and management. The collected logs are then analyzed to identify patterns, indicators of compromise (IoCs), and potential detection and mitigation strategies. Through the development of the ransomware simulator and the subsequent analysis of Sysmon logs, this research contributes to strengthening the security posture of organizations and improving cybersecurity measures against ransomware threats, with a focus on the latest Sysmon capabilities. The results demonstrate the importance of monitoring and analyzing system events to effectively detect and respond to ransomware attacks. This research can serve as a basis for further exploration of ransomware detection and response strategies, contributing to the advancement of cybersecurity practices and the development of more robust security measures against ransomware threats

    Data ethics : building trust : how digital technologies can serve humanity

    Get PDF
    Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century: For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car, from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad, for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world? How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication. The authors and institutions come from all continents. The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust! The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors

    Towards a Peaceful Development of Cyberspace - Challenges and Technical Measures for the De-escalation of State-led Cyberconflicts and Arms Control of Cyberweapons

    Get PDF
    Cyberspace, already a few decades old, has become a matter of course for most of us, part of our everyday life. At the same time, this space and the global infrastructure behind it are essential for our civilizations, the economy and administration, and thus an essential expression and lifeline of a globalized world. However, these developments also create vulnerabilities and thus, cyberspace is increasingly developing into an intelligence and military operational area – for the defense and security of states but also as a component of offensive military planning, visible in the creation of military cyber-departments and the integration of cyberspace into states' security and defense strategies. In order to contain and regulate the conflict and escalation potential of technology used by military forces, over the last decades, a complex tool set of transparency, de-escalation and arms control measures has been developed and proof-tested. Unfortunately, many of these established measures do not work for cyberspace due to its specific technical characteristics. Even more, the concept of what constitutes a weapon – an essential requirement for regulation – starts to blur for this domain. Against this background, this thesis aims to answer how measures for the de-escalation of state-led conflicts in cyberspace and arms control of cyberweapons can be developed. In order to answer this question, the dissertation takes a specifically technical perspective on these problems and the underlying political challenges of state behavior and international humanitarian law in cyberspace to identify starting points for technical measures of transparency, arms control and verification. Based on this approach of adopting already existing technical measures from other fields of computer science, the thesis will provide proof of concepts approaches for some mentioned challenges like a classification system for cyberweapons that is based on technical measurable features, an approach for the mutual reduction of vulnerability stockpiles and an approach to plausibly assure the non-involvement in a cyberconflict as a measure for de-escalation. All these initial approaches and the questions of how and by which measures arms control and conflict reduction can work for cyberspace are still quite new and subject to not too many debates. Indeed, the approach of deliberately self-restricting the capabilities of technology in order to serve a bigger goal, like the reduction of its destructive usage, is yet not very common for the engineering thinking of computer science. Therefore, this dissertation also aims to provide some impulses regarding the responsibility and creative options of computer science with a view to the peaceful development and use of cyberspace
    • …
    corecore