Search Results (6)

Data exfiltration poses a major cybersecurity challenge because it involves the unauthorized transfer of sensitive information. Intrusion Detection Systems (IDSs) are vital security controls in identifying such attacks; however, their effectiveness in cloud computing environments remains limited, particularly against covert channels such as Internet Control Message Protocol (ICMP) and Domain Name System (DNS) tunneling. This study compares two widely used IDSs, Snort and Suricata, in a controlled cloud computing environment. The assessment focuses on their ability to detect data exfiltration techniques implemented via ICMP and DNS tunneling, using DNSCat2 and Iodine. We evaluate detection performance using standard classification metrics, including Recall, Precision, Accuracy, and F1-Score. Our experiments were conducted on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances, where IDS instances monitored simulated exfiltration traffic generated by DNSCat2, Iodine, and Metasploit. Network traffic was mirrored via AWS Virtual Private Cloud (VPC) Traffic Mirroring, with the ELK Stack integrated for centralized logging and visual analysis. The findings indicate that Suricata outperformed Snort in detecting DNS-based exfiltration, underscoring the advantages of multi-threaded architectures for managing high-volume cloud traffic. For DNS tunneling, Suricata achieved 100% detection (recall) for both DNSCat2 and Iodine, whereas Snort achieved 85.7% and 66.7%, respectively. Neither IDS detected ICMP tunneling using Metasploit, with both recording 0% recall. It is worth noting that both IDSs failed to detect ICMP tunneling under default configurations, highlighting the limitations of signature-based detection in isolation. These results emphasize the need to combine signature-based and behavior-based analytics, supported by centralized logging frameworks, to strengthen cloud-based intrusion detection and enhance forensic visibility. Full article

The increasing complexity of cybersecurity risks highlights the critical need for novel teaching techniques that provide students with the necessary skills and information. Traditional on-premises laboratory setups frequently lack the scalability, flexibility, and accessibility necessary for efficient training in today’s dynamic world. This study compares the efficacy of cloud-based solutions—specifically, Amazon Web Services (AWS) Elastic Compute Cloud (EC2)—against traditional settings like VirtualBox, with the goal of determining their potential to improve cybersecurity education. The study conducts systematic experimentation to compare lab environments based on parameters such as lab completion time, CPU and RAM use, and ease of access. The results show that AWS EC2 outperforms VirtualBox by shortening lab completion times, optimizing resource usage, and providing more remote accessibility. Additionally, the cloud-based strategy provides scalable, cost-effective implementation via a pay-per-use model, serving a wide range of pedagogical needs. These findings show that incorporating cloud technology into cybersecurity curricula can lead to more efficient, adaptable, and inclusive learning experiences, thereby boosting pedagogical methods in the field. Full article

As the use of personal mobility (PM) devices continues to rise, regulatory violations have become more frequent, highlighting the need for technological solutions to ensure efficient enforcement. This study addresses these challenges by proposing an AI-based enforcement platform. The system integrates the You Only Look Once version 5 (YOLOv5) object detection model, a deep-learning-based framework, with Global Positioning System (GPS) location data, Raspberry Pi 5, and Amazon Web Services (AWS) for data processing and web-based implementation. The YOLOv5 model was deployed in two configurations: one for detecting electric scooter usage and another for identifying legal violations. The system utilized AWS Relational Database Service (RDS), Simple Storage Service (S3), and Elastic Compute Cloud (EC2) to store violation records and host web applications. The detection performance was evaluated using mean average precision (mAP) metrics. The electric scooter detection model achieved mAP50 and mAP50-95 scores of 99.5 and 99.457, respectively. Meanwhile, the legal violation detection model attained mAP50 and mAP50-95 scores of 99.5 and 81.813, indicating relatively lower accuracy for fine-grained violation detection. This study presents a practical technological platform for monitoring regulatory compliance and automating fine enforcement for shared electric scooters. Future improvements in object detection accuracy and real-time processing capabilities are expected to enhance the system’s overall reliability. Full article

Cloud provider Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 spot instances (SIs) offer spare computational capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that the delay in acquiring spots can be incredible high. Moreover, SIs may not always be available as they can be reclaimed by EC2 at any given time, with a two-minute interruption notice. In this paper, we propose a multi-workflow scheduling algorithm, allied with a container migration-based mechanism, to dynamically construct and readjust virtual clusters on top of non-reserved EC2 pricing model instances. Our solution leverages recent findings on performance and behavior characteristics of EC2 spots. We conducted simulations by submitting real-life workflow applications, constrained by user-defined deadline and budget quality of service (QoS) parameters. The results indicate that our solution improves the rate of completed tasks by almost 20%, and the rate of completed workflows by at least 30%, compared with other state-of-the-art algorithms, for a worse-case scenario. Full article

An internet of things (IoT) platform is a multi-layer technology that enables automation of connected devices within IoT. IoT platforms serve as a middle-ware solution and act as supporting software that is able to connect different hardware devices, access points, and networks to other parts of the value chain. Virtual objects have become a vital component in every IoT platform. Virtual objects are the digital representation of a physical entity. In this paper, we design and implement a cloud-centric IoT platform that serves a purpose for registration and initialization of virtual objects so that technology tinkerers can consume them via the IoT marketplace and integrate them to build IoT applications. The proposed IoT platform differs from existing IoT platforms in the sense that they provide hardware and software services on the same platform that users can plug and play. The proposed IoT platform is separate from the IoT marketplace where users can consume virtual objects to build IoT applications. Experiments are conducted for IoT platform and interworking IoT marketplace based on virtual objects in CoT. The proposed IoT platform provides a user-friendly interface and is secure and reliable. An IoT testbed is developed and a case study is performed for a domestic environment to reuse virtual objects on the IoT marketplace. It also provides the discovery and sharing of virtual objects. IoT devices can be monitored and controlled via virtual objects. We have conducted a comparative analysis of the proposed IoT platform with FIWARE. Results conclude that the proposed system performs marginally better than FIWARE. Full article

To efficiently process time-evolving graphs where new vertices and edges are inserted over time, an incremental computing model, which processes the newly-constructed graph based on the results of the computation on the outdated graph, is widely adopted in distributed time-evolving graph computing systems. In this paper, we first experimentally study how the results of the graph computation on the local graph structure can approximate the results of the graph computation on the complete graph structure in distributed environments. Then, we develop an optimization approach to reduce the response time in bulk synchronous parallel (BSP)-based incremental computing systems by processing time-evolving graphs on the local graph structure instead of on the complete graph structure. We have evaluated our optimization approach using the graph algorithms single-source shortest path (SSSP) and PageRankon the Amazon Elastic Compute Cloud(EC2), a central part of Amazon.com’s cloud-computing platform, with different scales of graph datasets. The experimental results demonstrate that the local approximation approach can reduce the response time for the SSSP algorithm by 22% and reduce the response time for the PageRank algorithm by 7% on average compared to the existing incremental computing framework of GraphTau. Full article