The rise of generative artificial intelligence (GenAI), powered by large language models, has led to the emergence of real-time, voice-based conversational applications that enable dynamic, multi-modal interactions for everyday tasks such as checking the weather or planning a trip. These human-to-GenAI calling applications blend speech processing, generative intelligence, and real-time communication, presenting new challenges in latency optimization, network infrastructure design, and resilience under load. Despite their growing popularity, little is known about the operational characteristics and performance of these applications. This paper conducts an empirical measurement of six human-to-GenAI calling applications from Google, Meta, Microsoft, and OpenAI, focusing on their input/output modalities, network behavior, latency metrics, and robustness. Our findings reveal key design choices and performance bottlenecks in these emerging applications. For example, the conversational latency often reaches several seconds, far exceeding the typical sub-second delays of human-to-human voice communication and potentially impairing interactivity. Moreover, voice-based GenAI traffic is inherently asymmetric: the uplink, carrying real-time human speech, benefits from streaming-based transmission, while the typically large downlink GenAI responses are better served through batch-based delivery.

With the increasing adoption of cloud computing, a growing number of users outsource their datasets to cloud. To preserve privacy, the datasets are usually encrypted before outsourcing. However, the common practice of encryption makes the effective utilization of the data difficult. For example, it is difficult to search the given keywords in encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users' retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we propose ECSED, a novel semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. ECSED uses two cloud servers. One is used to store the outsourced datasets and return the ranked results to data users. The other one is used to compute the similarity scores between the documents and the query and send the scores to the first server. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. We employ the multi-keyword ranked search over encrypted cloud data as our basic frame to propose two secure schemes. The experiment results based on the real world datasets show that the scheme is more efficient than previous schemes. We also prove that our schemes are secure under the known ciphertext model and the known background model.

With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.

With the increasing adoption of cloud computing, a growing number of users outsource their datasets into cloud. The datasets usually are encrypted before outsourcing to preserve the privacy. However, the common practice of encryption makes the effective utilization difficult, for example, search the given keywords in the encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we proposed an innovative semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. More specifically, our scheme first indexes the documents and builds trapdoor based on the concept hierarchy. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. Our experiment results based on the real world datasets show the scheme is more efficient than previous scheme. We also study the threat model of our approach and prove it does not introduce any security risk.

This paper focuses on detecting and studying HTTP scanners, which are malicious entities that explore a website selectively for "opportunities" that can potentially be used for subsequent intrusion attempts. Interestingly, there is practically no prior work on the detection of these entities, which are different from web crawlers or machines performing network-level reconnaissance activities such as port scanning. Detecting HTTP scanners is challenging as they are stealthy and often only probe a few key places on a website, so finding them is a needle-in-the-haystack problem. At the same time, they pose serious risk because they perform the first, exploratory step to provide the seed information that may allow hackers to compromise a website. Our work makes two main contributions. First, we propose Scanner Hunter, arguably the first method to detect HTTP scanners efficiently. The novelty and success of the method lies in the use of community structure, in an appropriately constructed bipartite graph, in order to expose groups of HTTP scanners. The rationale is that the aggregated behavior makes identifying groups of scanners easier than attempting to profile and label IP addresses individually. Scanner Hunter achieves an impressive 96.5% detection precision, which is roughly twice as high as the precision of the Machine Learning-based methods that we use as reference. Second, we provide an extensive study of HTTP scanners in an effort to understand: (a) their spatial and temporal properties, (b) the techniques and tools used by the scanners, and (c) the types of resources they are looking for, which can provides hints as to what the subsequent penetration attempt may target. We use six months worth of web traffic logs collected in 2012 from a University campus, the websites hosted by which received over 1.9 billion requests from 12.8 million IPs. We found that the number of HTTP scanners is non-trivial with roughly 4,000 IPs engaging in this type of activity per week. Our work will hopefully raise the awareness of the community regarding this problem while at the same time provide a promising detection technique that can provide the basis for mitigating the risk posed by HTTP scanners.

SUMMARY Software evolution is a fact of life. Over the past 30 years, researchers have proposed hypotheses on how software changes and provided evidence that both supports and refutes these hypotheses. To paint a clearer image of the software evolution process, we performed an empirical study on long spans in the lifetime of nine open‐source projects. Our analysis covers 705 official releases and a combined 108 years of evolution. We first tried to confirm Lehman's eight laws of software evolution on these projects using statistical hypothesis testing. Our findings indicate that only the laws of continuing change and continuing growth are confirmed for all programs, whereas the other six laws are violated by some programs, or can be both confirmed and invalidated, depending on the laws' operational definitions. Second, we analyze the growth rate for projects' development and maintenance branches, and the distribution of software changes. We find similarities in the evolution patterns of the programs we studied, which brings us closer to constructing rigorous models for software evolution. Copyright © 2011 John Wiley & Sons, Ltd.

Many research efforts propose the use of flow-level features (e.g., packet sizes and inter-arrival times) and machine learning algorithms to solve the traffic classification problem. However, these statistical methods have not made the anticipated impact in the real world. We attribute this to two main reasons: (a) training the classifiers and bootstrapping the system is cumbersome, (b) the resulting classifiers have limited ability to adapt gracefully as the traffic behavior changes. In this paper, we propose an approach that is easy to bootstrap and deploy, as well as robust to changes in the traffic, such as the emergence of new applications. The key novelty of our classifier is that it learns to identify the traffic of each application in isolation, instead of trying to distinguish one application from another. This is a very challenging task that hides many caveats and subtleties. To make this possible, we adapt and use subspace clustering, a powerful technique that has not been used before in this context. Subspace clustering allows the profiling of applications to be more precise by automatically eliminating irrelevant features. We show that our approach exhibits very high accuracy in classifying each application on five traces from different ISPs captured between 2005 and 2011. This new way of looking at application classification could generate powerful and practical solutions in the space of traffic monitoring and network management.

Profiling Internet backbone traffic is becoming an increasingly hard problem since users and applications are avoiding detection using traffic obfuscation and encryption. The key question addressed here is: Is it possible to profile traffic at the backbone without relying on its packet and flow level information, which can be obfuscated? We propose a novel approach, called Profiling-By-Association (PBA), that uses only the IP-to-IP communication graph and information about some applications used by few IP-hosts (a.k.a. seeds). The key insight is that IP-hosts tend to communicate more frequently with hosts involved in the same application forming communities (or clusters). Profiling few members within a cluster can "give away" the whole community. Following our approach, we develop different algorithms to profile Internet traffic and evaluate them on real-traces from four large backbone networks. We show that PBA's accuracy is on average around 90% with knowledge of only 1% of all the hosts in a given data set and its runtime is on the order of minutes (≈ 5).

Software evolution is a fact of life. Over the past thirty years, researchers have proposed hypotheses on how software changes, and provided evidence that both supports and refutes these hypotheses. To paint a clearer image of the software evolution process, we performed an empirical study on long spans in the lifetime of seven open source projects. Our analysis covers 653 official releases, and a combined 69 years of evolution. We first tried to verify Lehman's laws of software evolution. Our findings indicate that several of these laws are confirmed, while the rest can be either confirmed or infirmed depending on the laws' operational definitions. Second, we analyze the growth rate for projects' development and maintenance branches, and the distribution of software changes. We find similarities in the evolution patterns of the programs we studied, which brings us closer to constructing rigorous models for software evolution.