The escalating threat of phishing emails has become increasingly sophisticated with the rise of Large Language Models (LLMs). As attackers exploit LLMs to craft more convincing and evasive phishing emails, it is crucial to assess the resilience of current phishing defenses. In this study we conduct a comprehensive evaluation of traditional phishing detectors, such as Gmail Spam Filter, Apache SpamAssassin, and Proofpoint, as well as machine learning models like SVM, Logistic Regression, and Naive Bayes, in identifying both traditional and LLM-rephrased phishing emails. We also explore the emerging role of LLMs as phishing detection tools, a method already adopted by companies like NTT Security Holdings and JPMorgan Chase. Our results reveal notable declines in detection accuracy for rephrased emails across all detectors, highlighting critical weaknesses in current phishing defenses. As the threat landscape evolves, our findings underscore the need for stronger security controls and regulatory oversight on LLM-generated content to prevent its misuse in creating advanced phishing attacks. This study contributes to the development of more effective Cyber Threat Intelligence (CTI) by leveraging LLMs to generate diverse phishing variants that can be used for data augmentation, harnessing the power of LLMs to enhance phishing detection, and paving the way for more robust and adaptable threat detection systems.

The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications Given its immense potential, quantum-based computing systems have attracted increasing attention with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting.

Efficiently scheduling jobs in clouds is critical for job performance, system throughput and resource utilization. The growing importance of parallel applications in clouds introduces challenges in scheduling data-parallel jobs. Production data-parallel jobs increasingly have complex dependency structure, i.e., complex task dependencies expressed as directed acyclic graphs (DAGs), and heterogeneous resource demands. NP-hard problems are introduced by relaxing either of these challenges (i.e., scheduling of homogeneous tasks with dependency constraints or independent and heterogeneous tasks) for scheduler design. It is challenging to design a scheduler for simultaneously achieving low latency and high resource utilization due to the complex dependency structure and job heterogeneity. In this paper, we propose Sailfish, a dependency-aware and resource efficient scheduling for low latency in clouds. Sailfish first uses the machine learning algorithm to classify jobs into two categories (high priority jobs and low priority jobs) based on the extracted features. Next, Sailfish splits the jobs into tasks and distributes the tasks to the master nodes based on the dependency of tasks and the load of master nodes. Then, Sailfish utilizes the dependency information of tasks to determine tasks’ priority, and packs tasks by leveraging the complementary of tasks’ requirements on different resource types and task dependency. Finally, the master nodes leverage the proposed mutual reinforcement algorithm to distribute tasks to workers in the system based on the resource demands of tasks, the available resources of workers and task dependency. Extensive experimental results based on a real cluster and experiments using real-world Amazon EC2 cloud service show that Sailfish can improve the average resource utilization (by up to 40%) and reduce the latency (the average job completion time) significantly (by up to 91%) compared to the existing schedulers.

In the past decade, we have envisioned enormous growth in the data generated by different sources, ranging from weather sensors and customer purchasing records to Internet of Things devices. Emerging data-driven technologies have been reforming our daily life for years, such as Amazon Personalize [1], which creates real-time individualized recommendations for customers according to multidimensional data analytics. It is, however, a challenging task to fully utilize and harness the potential of data, especially big data, due to Volume, Velocity, Variety, Variability and Value (5Vs) [2]. Most businesses thus choose to migrate their hardware demands to cloud providers, such as Amazon Web Service [3], which is powered by hundreds of thousands of servers. A cluster that builds up by a number of cloud servers is a basic management unit to provide shared computing resources. The typical structure of a cluster consists of managers and workers. When a job arrives at the cluster, as the first step, managers have to select a worker to host the incoming job. Traditionally, the selection process is based on the state of the workers, e.g., resource availability and specifications of jobs, e.g., labels, zones and regions. With respect to currently running jobs, we propose a progress based container placement scheme, named ProCon. When scheduling incoming containers, ProCon not only considers instant resource utilization on the workers but also takes into account the estimation of future resource usage. Through monitoring the progress of running jobs, ProCon balances the resource contentions across the cluster and reduces the completion time as well as the makespan. Specifically, extensive experiments prove that ProCon reduces completion time by up to 53.3% for a particular job and improves overall performance by 23.0%. Additionally, ProCon records an improvement of makespan for up to 37.4% when compared to the default scheduler available in Kubernetes.