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University of Dayton Libraries added Shibboleth as an additional form of authentication for its licensed e-resources beginning in summer 2023. A major impetus for pursuing Shibboleth are the upcoming web browser changes that will obfuscate users' IP addresses, set to occur in later 2023 and 2024 and something which will likely disrupt on-campus IP range access via EZproxy. Implementation of Shibboleth for an initial round of fifty-five e-resource platforms was relatively quick as a result of the University of Dayton's participation in the InCommon Federation of U.S. higher education institutions, also known as an "authentication federation." Methods for achieving the implementation involved entering Shibboleth entities within vendors' admin portals or working with the vendor representatives via email. Contrary to our previous understanding of Shibboleth, this implementation did not require extensive work on our end with our central IT office and typically required us to provide only a handful of entities directly to the vendor (all of which are anonymous entities). This article aims to share our workflow and lessons learned in the hope that other InCommon member institutions can follow suit. [ABSTRACT FROM AUTHOR]

We provide evidence of a network of information flow between activists and other investors prior to 13D filings. We match EDGAR search activity to investor IP addresses, identifying specific investors who persistently download information on an individual activist's campaign targets in the days prior to that activist's 13D disclosures. This outside investor's knowledge of pending activist campaign plans seems to benefit both parties: the informed investor, unnamed in the 13D, increases its holdings in the targeted stock prior to the price surge upon 13D disclosure, while the activist earns voting support that increases their likelihood of pursuing and winning a proxy fight. [ABSTRACT FROM AUTHOR]

Summary: Cloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods. [ABSTRACT FROM AUTHOR]

Determining the maliciousness of a cybersecurity incident is essential to establish effective measures against it. To process large volumes of data in an automated way, machine learning techniques are commonly applied to the problem. One of the main obstacles to apply machine learning effectively is that the data distribution is not stationary, so a model trained on old data tends to degrade as new data with a different distribution is processed. This change in the distribution of data over time is known as concept drift and affects the reports of new events, which may compromise model performance. To tackle this problem this paper evaluates the effectiveness of transfer learning techniques in reducing the impact of concept drift on the performance of models for assigning maliciousness to IPs. We compare this approach with the application of online-updated models, which are another common approach to adapt to concept drift in the data. We analyse the performance of both approaches to determine which may be more effective in this setting. [ABSTRACT FROM AUTHOR]

The Black Sea is a vital resource with vast potential to boost the societal value of the Blue Economy for its surrounding countries. Improved knowledge and enhanced infrastructure together with better coordination and alignment of research and innovation efforts are critical for the better management of the deteriorated Black Sea ecosystem to help restore and maintain its resilience and enable sustainable use of natural resources. In 2019, the Black Sea Strategic Research and Innovation Agenda (Black Sea SRIA), was developed by the Black Sea experts, in cooperation with European marine institutes and organizations, with the support of the European Commission. Black Sea SRIA presents the priorities and ecosystem-based management options needed for a healthy, sustainable, and resilient Black Sea. Since 2019, efforts focused on developing concrete actions under the Black Sea SRIA Implementation Plan (IP) to contribute to the national blue economy strategies, better structuring of the relevant funding mechanisms for solution-oriented implementation and the international and regional strategies, supporting the co-funding mechanisms. In this study, we present a transnational process that has led to the development of the IP which utilized a bottom-up, co-design and co-creation-based approach. The SRIA and its IP support better governance of the blue economy principles towards sustainable development and conservation of unique Black Sea ecosystems. The Black Sea SRIA and IP address the fundamental research challenges of the region, promote the blue economy, and also build vital support systems and innovative research infrastructure and capacity together with academia, funding organizations, industry, policy, civil society and local communities. The IP is a long-lasting guide to catalyze new ideas and innovations towards and with the Black Sea community with strong implications for other sea basins. [ABSTRACT FROM AUTHOR]

Currently, telecom fraud is expanding from the traditional telephone network to the Internet, and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights. However, existing telecom fraud identification methods based on blacklists, reputation, content and behavioral characteristics have good identification performance in the telephone network, but it is difficult to apply to the Internet where IP (Internet Protocol) addresses change dynamically. To address this issue, we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering (DC-FIPD). First, we analyze the aggregation of fraudulent IP geographies and the homology of IP addresses. Next, the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent IPs. Then, we constructed the fraudulent IP feature set, used the genetic optimization algorithm to determine the weights of the fraudulent IP features, and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent IP. Finally, the risk value of the target IP is calculated and the IP is identified based on the risk value threshold. Experimental results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64% for fraudulent IPs. Additionally, the method records a precision of 86.08%, a recall of 45.24%, and an F1-score of 59.31%, offering a comprehensive evaluation of its performance in fraud detection. These results highlight the DC-FIPD method's effectiveness in addressing the challenges of fraudulent IP identification. [ABSTRACT FROM AUTHOR]

The global Internet is a complex network of interconnected autonomous systems (ASes). Understanding Internet inter-domain path information is crucial for understanding, managing, and improving the Internet. The path information can also help protect user privacy and security. However, due to the complicated and heterogeneous structure of the Internet, path information is not publicly available. Obtaining path information is challenging due to the limited measurement probes and collectors. Therefore, inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths. The purpose of this survey is to provide an overview of techniques that have been conducted to infer Internet inter-domain paths from 2005 to 2023 and present the main lessons from these studies. To this end, we summarize the inter-domain path inference techniques based on the granularity of the paths, for each method, we describe the data sources, the key ideas, the advantages, and the limitations. To help readers understand the path inference techniques, we also summarize the background techniques for path inference, such as techniques to measure the Internet, infer AS relationships, resolve aliases, and map IP addresses to ASes. A case study of the existing techniques is also presented to show the real-world applications of inter-domain path inference. Additionally, we discuss the challenges and opportunities in inferring Internet inter-domain paths, the drawbacks of the state-of-the-art techniques, and the future directions. [ABSTRACT FROM AUTHOR]

Behind everyday websites, a hidden shadow world tracks the behavior of Internet users. Web tracking analyzes online activity based on collected data and delivers content tailored to users' interests. It gathers vast amounts of information for various purposes, ranging from sensitive personal data to seemingly minor details such as IP addresses, devices, browsing histories, settings, and preferences. While Web tracking is largely a legitimate technology, the increase in illegal user tracking, data breaches, and the unlawful sale of data has become a growing concern. As a result, the demand for technologies that can detect and prevent Web trackers is more important than ever. This paper provides an overview of Web tracking technologies, relevant research, and website measurement tools designed to identify web-based tracking. It also explores technologies for preventing Web tracking and discusses potential directions for future research. [ABSTRACT FROM AUTHOR]

Traditional body temperature monitoring technology has some problems, such as inaccurate manual reading, unable to monitor continuously for a long time, and difficult data storage and analysis. To solve these problems, this paper designs an application scenario of Internet of Things in body temperature monitoring, which integrates Internet of Things, network communication, network programming and other technologies, designs the overall architecture, topology structure and IP address allocation scheme of the system, and realizes real-time acquisition, transmission, storage, analysis, and display functions based on the Packet Tracer simulation platform. The performance evaluation and optimization are also carried out. The system realizes wireless monitoring and analysis of the body temperature data collected by the temperature sensor through the home gateway and single chip microcomputer and other devices. After testing, the designed system is stable, which provides a solution for the application of Internet of Things in body temperature monitoring. [ABSTRACT FROM AUTHOR]

With the increasing sophistication of online survey tools and the necessity of distanced research during the COVID-19 pandemic, the use of online questionnaires for research purposes has proliferated. Still, many researchers undertake online survey research without knowledge of the prevalence and likelihood of experiencing survey questionnaire fraud nor familiarity with measures used to identify fraud once it has occurred. This research note is based on the experience of researchers across four sites who implemented an online survey of families' experiences with COVID-19 in the U.S. that was subject to substantial fraud. By the end of data collection, over 70% of responses were flagged as fraudulent with duplicate IP addresses and concurrent start/end times representing the most common indicators of fraud observed. We offer lessons learned to illustrate the sophisticated nature of fraud in online research and the importance of multi-pronged strategies to detect and limit online survey questionnaire fraud. [ABSTRACT FROM AUTHOR]

Named Data Networking (NDN) is a next-generation internet architecture that routes data packets based on content identity rather than IP addresses, shifting focus from location to identity. However, because NDN routers are decentralized and have a limited capacity for data packet authentication, the networks are exposed to a variety of security risks like cache poisoning. Identifying and blocking malicious sources in NDN networks is challenging. To overcome these issues, a novel Secure Named Data networking using Deep Learning (SEND-DL) technique has been proposed to address security concerns in NDN by focusing on mitigating threats such as cache poisoning attacks and identifying malicious sources. The proposed method introduces a Feedback packet type and utilizes the Boneh-Lynn-Shacham (BLS) signature scheme for authentication, alongside Convolutional Neural Network (CNN) and Deep Belief Network (DBN) based techniques for content store cleanup and malicious source identification. The method dynamically adjusts forwarding paths, prioritizing legitimate content retrieval while blocking malicious sources. Experimental outcomes validate the efficiency of the proposed SEND-DL approach using evaluation metrics like content retrieval latency, communication overhead, cache hit rate, deployment complexity, security effectiveness, and fault tolerance which are compared to existing techniques like Q-learning based cache pollution attack mitigation approach for named data networking (Q-ICAN), Dynamic FOrest of Random Subsets based Capability enhancing Security Architecture (DFORS-CSA), and Mitigation of Sophisticated Interest flooding-based DDoS attacks in Named data networking (MISDN). The security effectiveness of the proposed SEND-DL is 6.18%, 20.06%, and 9.62% higher than the existing Q-ICAN, DFORS-CSA, and MISDN methods respectively. [ABSTRACT FROM AUTHOR]

With the rapid progress of network technology, intelligence and virtualization have become the trend. People pay more and more attention to artificial intelligence, and the user behavior information analysis and reasoning function based on IPC and machine learning algorithm is widely used in computer applications. Traditional SDN algorithms may encounter performance bottlenecks when dealing with large-scale networks, resulting in the inability to continue to expand the network scale. This limits applications in complex network environments such as large enterprises or cloud data centers. Based on SDN architecture, this paper proposes a new intelligent optimized web crawler system, which covers user access control strategy. The research focuses on improving the IP address mapping capability, and verifies the characteristics of different node-level permissions with examples. The experimental results show that the resource utilization of the traditional algorithm is 75%, 80%, 70%, 85% and 78%, while the resource utilization of the SDN routing algorithm is above 79%, which is less than 10% higher than the traditional one. It can be seen that the intelligent algorithm shows a higher utilization of the five resources [ABSTRACT FROM AUTHOR]

The degree of severity of a cybersecurity event of potentially malicious activity is crucial to determine an appropriate response. Machine learning techniques are used to obtain models to achieve a correct characterization of events as well as an accurate maliciousness measure. A model is constructed by setting up an effective collection of features that are able to characterize the maliciousness of IP addresses. As a first approach, a set of contextual features has been selected. These features are simple to extract and require no high costs. That set contains 23 features: 18 obtained through time series analysis, and the other 5 extracted directly related with the spatial (geolocation) and temporal (time of occurrence) similarity of the events. The test of the feature set is conducted with statistical analyses to determine which features are the most effective and the impact of hyperparameter selection in the construction of a time series of features. In addition, the effect of IP geo-localization shifts on model performance is studied through concept drift theory. The results conclude that, overall, the feature set provides adequate performance for our task, although more complex features may be required to achieve better performance. The best results are obtained with the geolocation and time occurrence features. [ABSTRACT FROM AUTHOR]

Industrial Control Systems are used in a wide variety of industrial facilities, including critical infrastructures, becoming the main target of multiple security attacks. A malicious and successful attack against these infrastructures could cause serious economic and environmental consequences, including the loss of human lives. Static networks configurations and topologies, which characterize Industrial Control Systems, represent an advantage for attackers, allowing them to scan for vulnerable devices or services before carrying out the attack. Identifying active devices and services is often the first step for many attacks. This paper presents a proactive network reconnaissance defense mechanism based on the temporal randomization of network IP addresses, MAC addresses and port numbers. The obtained information distortion minimizes the knowledge acquired by the attackers, hindering any attack that relies on network addressing. The temporal randomization of network attributes is performed in an adaptive way, minimizing the overhead introduced in the network and avoiding any error and latency in communications. The implementation as well as the tests have been carried out in a laboratory with real industrial equipment, demonstrating the effectiveness of the presented solution. [ABSTRACT FROM AUTHOR]

The Dark Web is a subset of the Deep Web, requiring special browsers, the Dark Net refers to encrypted networks, the Deep Web encompasses non-indexed online content, and darknet includes unused IP address networks. The Dark Net has become a hotbed of cybercrime, with individuals and groups using the anonymity and encryption provided by the network to carry out a range of criminal activities. One of the most concerning trends in recent years has been the weaponization of cybercrimes, as criminals use their technical skills to create tools and techniques that can be used to launch attacks against individuals, businesses, and governments. This paper examines the weaponization of cybercrimes on the Dark Net, focusing on the question of detection and application. This paper uses a Systematic Literature Review (SLR) method to appraise the Dark Web, examine the crimes and their consequences and identify future measures to reduce crime threats. Data from 88 relevant articles from 2011 to 2023 were extracted and synthesized, along with the latest data from 2024 to answer research questions, providing comprehensive knowledge on growing crimes; assessing social, economic, and ethical impacts; and analyzing established techniques and methods to locate and apprehend criminals. [ABSTRACT FROM AUTHOR]

Copyright of Computer und Recht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer und Recht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ben Collier's book "Tor: From the Dark Web to the Future of Privacy" delves into the history and significance of Tor, a privacy-preserving technology that allows users to browse the internet without leaving digital traces. The book explores the development of Tor, its unique features such as onion routing, and its diverse user base, including intelligence officers, journalists, and human rights activists. Collier also addresses the paradox of why the U.S. government has supported a tool that limits its surveillance capabilities, highlighting the complexities and evolution of Tor over the years. [Extracted from the article]