Utilizing Web Trackers for Sybil Defense

Detection method to eliminate Sybil attacks in Vehicular Ad-hoc Networks

Sybil attack is one of the most challenging problems that plague current decentralized Peer-to-Peer(P2P) systems. In Sybil attack, a single malicious user creates multiple peer identities known as sybils. These sybils are employed to target honest peers and hence subvert the system. In this paper, we describe a novel solution that enables all honest peers to protect themselves from sybils with high probability in large structured P2P systems. In our proposed sybil defense system, we associate every peer with another non-sybil peer known as SyMon. A given peer’s SyMon is chosen dynamically such that the chances of both of them being sybils are very low. The chosen SyMon is entrusted with the responsibility of moderating the transactions involving the given peer and hence makes it almost impossible for sybils to compromise the system. We show the effectiveness of our proposed system in defending against Sybil attack both analytically and experimentally. In addition to this, we explore the feasibility of our proposed solution in two P2P applications: reputation systems for P2P based file sharing applications and P2P applications susceptible to Denial-of-Service(DOS) attack, systems known to be highly vulnerable to Sybil attack. In each of our case studies, we discuss possible ways in which our solution can be employed to defend the system against Sybil attack.

A Sybil attack can inject many forged identities (called Sybils) to subvert a target system. Because of the severe damage that Sybil attacks can cause to a wide range of networking applications, there has been a proliferation of Sybil defense schemes. Of particular attention are those that explore the online social networks (OSNs) of users in a victim system in different ways. Unfortunately, while effective Sybil defense solutions are urgently needed, it is unclear how effective these OSN-based solutions are under different contexts. For example, all current approaches have focused on a common, classical scenario where it is difficult for an attacker to link Sybils with honest users and create attack edges; however, researchers have found recently that a modern scenario also becomes typical where an attacker can employ simple strategies to obtain many attack edges. In this work we analyze the state of OSN-based Sybil defenses. Our objective is not to design yet another solution, but rather to thoroughly analyze, measure, and compare how well or inadequate the well-known existing OSN-based approaches perform under both the classical scenario and the modern scenario. Although these approaches mostly perform well under the classical scenario, we find that under the modern scenario they are vulnerable to Sybil attacks. As shown in our quantitative analysis, very often a Sybil only needs a handful of attack edges to disguise itself as a benign node, and there is only a limited success in tolerating Sybils. Our study further points to capabilities a new solution must possess; in particular, in defense against Sybils under the modern scenario, we anticipate a new approach that enriches the structure of a social graph with more information about the relations between its users can work more effectively.

Spit (Spam over Internet Telephony), known as unsolicited bulk calls sent via VoIP networks, is a major problem that undermines the usability of VoIP. Countermeasures against spit face challenges in identifying and filtering spit in real time. A user behavior based on three parameters (interaction,historical, and social ratio) is used to design an anti-spit technique. The rationale for the technique is that voice spammers behave significantly different from legitimate callers because of their revenue-driven motivations. The scheme uses adaptive training to determine filtering accuracy and estimates the legitimacy of the caller. The ideas can be implemented at the router level for detecting and achieving voice spam control. Compared to existing spit defending techniques, it is simple, fast and effective. Experiments are reported and measure the accuracy based on call intensity, call density, and the size of training data. The proposed scheme inapplicable for detecting and filtering both machine initiated and human-initiated spam calls, better protects VoIP calls against Sybil attacks and spammer behavior changes.

Vehicular Ad-hoc Networks (VANET) are capable of offering inter and intra-vehicle wireless communication among mobility aware computing systems. Nodes are linked by applying concepts of mobile ad hoc networks. VANET uses cases empower vehicles to link to the network to aggregate and process messages in real-time. The proposed paper addresses a security vulnerability known as Sybil attack, in which numerous fake nodes broadcast false data to the neighboring nodes. In VANET, mobile nodes continuously change their network topology and exchange location sensor-generated data in real time. The basis of the presented technique is source testing that permits the scalable identification of Sybil nodes, without necessitating any pre-configuration, which was conceptualized from a comparative analysis of preceding research in the literature.

The combination of the Internet of Things (IoT) and aviation integration with the help of satellite and 6G communication technologies has led to the Internet of Unmanned Aerial Vehicles (UAV) or Internet of Drones (IoD). To host and share large amounts of drone data in real time, cloud-based IoD algorithm Cloudbased IoD is the wrong choice to reduce the weight of mobile drones. However, how to protect the sensitive drone at the fair is a matter of curiosity, and it is very difficult to break the environment where drone use is available and limited resources are available. Although our previous work in SPNCE21 (PATLDAC) developed an air-on middle of the main load of based drone data access control system with privacy policy, limited access time and user tracking, it provides conflicting and centralized cloud data storage for cloud environment and access with unreliable data. It is not reliable in terms of data access and tracking of users. To this end, we propose a blockchainbased privacy data access control (BPADAC) scheme for distributed and secure drone data sharing on cloudbased IoDs. Building on the granular, traceable, and privacypreserving drone data access capabilities of our previous work, we extend this by using blockchain and decentralized hash tables (DHT) to provide secure and reliable wireless access to humanmachine data and storage. as well as reliable and limited access guarantee mechanisms for cloud drone data sharing services. We also set up a public and denial user tracking system to prevent user abuse and deny the traitor. Finally, we propose a prototype for security analysis and performance evaluation using smart contracts on the Ethereum blockchain to demonstrate the feasibility of BPADAC. hash table, attributebased ciphertext encryption policy, and blockchain-based privacy-protected enabled and aware data access control Key Words: Blockchain, UAV,IoD, Distributed Hash Table, Ciphertext-Policy-Attribute-Based-Encryption, blockchainbased privacy-aware data access control

Crowdsourcing refers to the employment of workers to complete tasks. Workers will also be rewarded after completing the tasks, which creates two problems. One is how to effectively aggregate the right answers of tasks when the accuracy of workers’ answers varies. The other is how to deal with Sybil attacker, who will get rewards by controlling half or more Sybil workers to influence the Truth Inference. We propose a Truth Discovery with intelligent Sybil defense, called STDEL (Sybil-resistant Truth Discovery by Exploiting the Long-tail Effect). This algorithm can improve the accuracy of Truth aggregation and reduce the rewards by identifying and banning intelligent Sybil workers. Our Experiments on two real world datasets show that STDEL has better performance on accuracy and rewards compared with state-of-the-art Sybil defense Truth Discovery.

The Constant Murmur of Data

Fitness Wearables

Severe class imbalance in Indonesian language e-commerce reviews hampers the detection of customer grievances, because about 88 percent of testimonials are positive whereas under one percent are negative. This study designs a simple yet effective framework to mitigate that bias. The data 2,611 authentic reviews covering electronics, fashion, and household goods and reflecting online shopping patterns were cleaned, structured, and represented using a combination of TF-IDF weighting and Word2Vec embeddings to condense meaning. Imbalance was addressed with the Synthetic Minority Oversampling Technique integrated with Edited Nearest Neighbour, equalising the proportions of positive, neutral, and negative classes. A Support Vector Machine was trained with five-fold cross-validation and benchmarked against Multinomial Naïve Bayes and a Decision Tree classifier. Experiments yielded 94.6 percent accuracy and a 94.8 percent F1-score, while precision for the negative class reached 100 percent, outperforming conventional approaches by up to twelve percent. The contribution of this research is the demonstration that minimal pre-processing, lightweight feature extraction, and targeted data balancing can markedly enhance grievance detection without resorting to complex model architectures. Practically, the framework enables online merchants to monitor service quality and respond to customer issues in real time, and it can be integrated into recommendation engines and business dashboards to accelerate data-driven decision making and strengthen transparency for stakeholders. Academically, the findings open avenues for integrating large language models, temporal analysis, and cross-domain adaptation to enrich the global e-commerce ecosystem.

This paper describes the design and implementation of a new dynamic Web Recommender System using Hard and Fuzzy K-modes clustering. The system provides recommendations based on user preferences that change in real time taking also into account previous searching and behavior. The recommendation engine is enhanced by the utilization of static preferences which are declared by the user when registering into the system. The proposed system has been validated on a movie dataset and the results indicate successful performance as the system delivers recommended items that are closely related to user interests and preferences.

The presented solution supports innovative ways of manipulating computer multimedia content, such as: static images, videos and music clips and others that can be browsed subsequently. The system requires a standard web camera that captures images of the user face. The core of the system is formed by a head movement analyzing algorithm that finds a user face and tracks head movements in real time. Head movements are tracked with a Finite State Machine. State transitions are triggered by various spatial and temporal conditions. Whenever a state of the machine changes, an event is sent to the GUI application supposed to react accordingly. The system is immune to the presence of many faces in a video stream; only one face is tracked. The application is especially suitable to standalone, multimedia terminals where users may get acquainted with a company profile, situation layout or a store offer in a fast and convenient way. The application may also be used by disabled people.

The European Union's General Data Protection Regulation (GDPR) went into effect on May 25, 2018. Its privacy regulations apply to any service and company collecting or processing personal data in Europe. Many companies had to adjust their data handling processes, consent forms, and privacy policies to comply with the GDPR's transparency requirements. We monitored this rare event by analyzing the GDPR's impact on popular websites in all 28 member states of the European Union. For each country, we periodically examined its 500 most popular websites - 6,579 in total - for the presence of and updates to their privacy policy. While many websites already had privacy policies, we find that in some countries up to 15.7 % of websites added new privacy policies by May 25, 2018, resulting in 84.5 % of websites having privacy policies. 72.6 % of websites with existing privacy policies updated them close to the date. Most visibly, 62.1 % of websites in Europe now display cookie consent notices, 16 % more than in January 2018. These notices inform users about a site's cookie use and user tracking practices. We categorized all observed cookie consent notices and evaluated 16 common implementations with respect to their technical realization of cookie consent. Our analysis shows that core web security mechanisms such as the same-origin policy pose problems for the implementation of consent according to GDPR rules, and opting out of third-party cookies requires the third party to cooperate. Overall, we conclude that the GDPR is making the web more transparent, but there is still a lack of both functional and usable mechanisms for users to consent to or deny processing of their personal data on the Internet.

Comparison of seven popular patient-oriented medical web sites for three common emergency department complaints

Modern smartphone sensors can be leveraged for providing novel functionality and greatly improving the user experience. However, sensor data can be misused by privacy-invasive or malicious entities. Additionally, a wide range of other attacks that use mobile sensor data have been demonstrated; while those attacks have typically relied on users installing malicious apps, browsers have eliminated that constraint with the deployment of HTML5 WebAPI. In this article, we conduct a comprehensive evaluation of the multifaceted threat that mobile web browsing poses to users by conducting a large-scale study of mobile-specific HTML5 WebAPI calls across more than 183K of the most popular websites. We build a novel testing infrastructure consisting of actual smartphones on top of a dynamic Android app analysis framework, allowing us to conduct an end-to-end exploration. In detail, our system intercepts and tracks data access in real time, from the WebAPI JavaScript calls down to the Android system calls. Our study reveals the extent to which websites are actively leveraging the WebAPI for collecting sensor data, with 2.89% of websites accessing at least one sensor. To provide a comprehensive assessment of the risks of this emerging practice, we create a taxonomy of sensor-based attacks from prior studies and present an in-depth analysis by framing our collected data within that taxonomy. We find that 1.63% of websites can carry out at least one attack and emphasize the need for a standardized policy across all browsers and the ability for users to control what sensor data each website can access.