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Journal articles on the topic 'Anonymizing network'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Das, Sudipto, Omer Egecioglu, and Amr El Abbadi. "Anónimos: An LP-Based Approach for Anonymizing Weighted Social Network Graphs." IEEE Transactions on Knowledge and Data Engineering 24, no. 4 (April 2012): 590–604. http://dx.doi.org/10.1109/tkde.2010.267.

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Siddula, Madhuri, Yingshu Li, Xiuzhen Cheng, Zhi Tian, and Zhipeng Cai. "Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users." Wireless Communications and Mobile Computing 2020 (September 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8892321.

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While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

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Moreno-Sanchez, Pedro, Tim Ruffing, and Aniket Kate. "PathShuffle: Credit Mixing and Anonymous Payments for Ripple." Proceedings on Privacy Enhancing Technologies 2017, no. 3 (July 1, 2017): 110–29. http://dx.doi.org/10.1515/popets-2017-0031.

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Abstract The I owe you (IOU) credit network Ripple is one of the most prominent alternatives in the burgeoning field of decentralized payment systems. Ripple’s path-based transactions set it apart from cryptocurrencies such as Bitcoin. Its pseudonymous nature, while still maintaining some regulatory capabilities, has motivated several financial institutions across the world to use Ripple for processing their daily transactions. Nevertheless, with its public ledger, a credit network such as Ripple is no different from a cryptocurrency in terms of weak privacy; recent demonstrative deanonymization attacks raise important concerns regarding the privacy of the Ripple users and their transactions. However, unlike for cryptocurrencies, there is no known privacy solution compatible with the existing credit networks such as Ripple. In this paper, we present PathShuffle, the first path mixing protocol for credit networks. PathShuffle is fully compatible with the current credit networks. As its essential building block, we propose PathJoin, a novel protocol to perform atomic transactions in credit networks. Using PathJoin and the P2P mixing protocol DiceMix, PathShuffle is a decentralized solution for anonymizing path-based transactions. We demonstrate the practicality of PathShuffle by performing path mixing in Ripple.

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Naumov, A. I., V. I. Radygin, and M. N. Ivanov. "IDENTIFICATION OF MIXER TRANSACTIONS IN THE BITCOIN NETWORK IN THE FRAMEWORK OF SOLVING THE PROBLEMS OF PREVENTING MONEY LAUNDERING AND TERRORIST FINANCING." SOFT MEASUREMENTS AND COMPUTING 1, no. 2 (2021): 78–90. http://dx.doi.org/10.36871/2618-9976.2021.02.007.

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This article addresses the problem of using cryptocurrencies in crimes related to money laundering and terrorist financing. The development of technologies that allow anonymizing participants of various cryptosystems not only increases their reliability and security for ordinary users, but also exposes such systems to the risk of being used by criminals, and also significantly complicates countering fraudulent or other illegal actions committed using cryptocurrencies. The authors propose algorithms that allow us to classify bitcoin transactions on the subject of whether they use mixers – the main means of hiding traces in the public blockchain and, perhaps, the main participant in most criminal schemes, whether it is money laundering or terrorist financing.

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Nastuła, Anna. "New threats in the cyberspace based on the analysis of the TOR (The Onion Router) network." ASEJ Scientific Journal of Bielsko-Biala School of Finance and Law 22, no. 4 (January 23, 2019): 28–31. http://dx.doi.org/10.5604/01.3001.0012.9839.

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New technologies change the society and generate new threats with respect to hiding human identity on the Internet. The aim of the paper is to present the origins, functioning and actual application of the TOR (The Onion Router) network. The author discusses the issue of anonymity as a driving force behind the TOR network which was ignited by social and political problems on the international arena. Also the relevance of TOR for the free flow of information and freedom of social communication on the Internet as well as thematic cross-section of its resources are presented. Further on in the paper the author shows a payment system in the TOR network which is based on cryptocurrencies, mainly on Bitcoin, and proves to be an ideal means of payment for illegal transactions. The paper also lists the most frequently committed crimes now and warns that the newly emerging forms of crimes which rely on anonymizing technologies, will pose a real challenge for law enforcement agencies and the system of justice. The TOR network by providing criminals with anonymity speeds up the development of cyber crime on an unprecedented scale and transfers traditional crime into a completely new dimension. The TOR network may have been well-meaning originally but has become a global threat.

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Martyniuk, Hanna, Valeriy Kozlovskiy, Serhii Lazarenko, and Yuriy Balanyuk. "Data Mining Technics and Cyber Hygiene Behaviors in Social Media." South Florida Journal of Development 2, no. 2 (May 26, 2021): 2503–15. http://dx.doi.org/10.46932/sfjdv2n2-108.

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The authors present in this work information about social media and data mining usage for that. It is represented information about social networking sites, where Facebook dominates the industry by boasting an account of 85% of the internet user’s worldwide. Applying data mining techniques to large social media data sets has the potential to continue to improve search results for everyday search engines, realize specialized target marketing for businesses, help psychologist study behavior, provide new insights into social structure for sociologists, personalize web services for consumers, and even help detect and prevent spam for all of us. The most common data mining applications related to social networking sites is represented. Authors have also gave information about different data mining techniques and list of these techniques. It is important to protect personal privacy when working with social network data. Recent publications highlight the need to protect privacy as it has been shown that even anonymizing this type of data can still reveal personal information when advanced data analysis techniques are used. A whole range of different threat of social networks is represented. Authors explain cyber hygiene behaviors in social networks, such as backing up data, identity theft and online behavior.

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7

Psaroudakis, Ioannis, Vasilios Katos, and Pavlos S. Efraimidis. "A novel mechanism for anonymizing Global System for Mobile Communications calls using a resource-based Session Initiation Protocol community network." Security and Communication Networks 8, no. 3 (June 26, 2014): 486–500. http://dx.doi.org/10.1002/sec.995.

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8

Franchi, Enrico, Agostino Poggi, and Michele Tomaiuolo. "Blogracy." International Journal of Distributed Systems and Technologies 7, no. 2 (April 2016): 37–56. http://dx.doi.org/10.4018/ijdst.2016040103.

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The current approach to build social networking systems is to create huge centralized systems owned by a single company. However, such strategy has many drawbacks, e.g., lack of privacy, lack of anonymity, risks of censorship and operating costs. In this paper the authors propose a novel P2P system that leverages existing, widespread and stable technologies such as DHTs and BitTorrent. In particular, they introduce a key-based identity system and a model of social relations for distributing content efficiently among interested readers. The system they propose, Blogracy, is a micro-blogging social networking system focused on: (i) anonymity and resilience to censorship; (ii) authenticatable content; (iii) semantic interoperability using activity streams. The authors have implemented the system and conducted various experiments to study its behaviour. The results are presented here, regarding (i) communication delays for some simulations of node churn, (ii) delays measured in test operations over PlanetLab, in direct communication, and (iii) through the I2P anonymizing network.

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Qiu, Ying, Yi Liu, Xuan Li, and Jiahui Chen. "A Novel Location Privacy-Preserving Approach Based on Blockchain." Sensors 20, no. 12 (June 21, 2020): 3519. http://dx.doi.org/10.3390/s20123519.

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Location-based services (LBS) bring convenience to people’s lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user’s transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.

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10

Zhou, Fan, Kunpeng Zhang, Shuying Xie, and Xucheng Luo. "Learning to Correlate Accounts Across Online Social Networks: An Embedding-Based Approach." INFORMS Journal on Computing 32, no. 3 (July 2020): 714–29. http://dx.doi.org/10.1287/ijoc.2019.0911.

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Cross-site account correlation correlates users who have multiple accounts but the same identity across online social networks (OSNs). Being able to identify cross-site users is important for a variety of applications in social networks, security, and electronic commerce, such as social link prediction and cross-domain recommendation. Because of either heterogeneous characteristics of platforms or some unobserved but intrinsic individual factors, the same individuals are likely to behave differently across OSNs, which accordingly causes many challenges for correlating accounts. Traditionally, account correlation is measured by analyzing user-generated content, such as writing style, rules of naming user accounts, or some existing metadata (e.g., account profile, account historical activities). Accounts can be correlated by de-anonymizing user behaviors, which is sometimes infeasible since such data are not often available. In this work, we propose a method, called ACCount eMbedding (ACCM), to go beyond text data and leverage semantics of network structures, a possibility that has not been well explored so far. ACCM aims to correlate accounts with high accuracy by exploiting the semantic information among accounts through random walks. It models and understands latent representations of accounts using an embedding framework similar to sequences of words in natural language models. It also learns a transformation matrix to project node representations into a common dimensional space for comparison. With evaluations on both real-world and synthetic data sets, we empirically demonstrate that ACCM provides performance improvement compared with several state-of-the-art baselines in correlating user accounts between OSNs.

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11

Tan, Rong, Yuan Tao, Wen Si, and Yuan-Yuan Zhang. "Privacy preserving semantic trajectory data publishing for mobile location-based services." Wireless Networks 26, no. 8 (June 15, 2019): 5551–60. http://dx.doi.org/10.1007/s11276-019-02058-8.

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Abstract The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k − 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.

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12

Décary-Hétu, David, Vincent Mousseau, and Sabrina Vidal. "Six Years Later." Contemporary Drug Problems 45, no. 4 (September 4, 2018): 366–81. http://dx.doi.org/10.1177/0091450918797355.

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Cryptomarkets are online illicit marketplaces where drug dealers advertise the sale of illicit drugs. Anonymizing technologies such as the Tor network and virtual currencies are used to hide cryptomarket participants’ identity and to limit the ability of law enforcement agencies to make arrests. In this paper, our aim is to describe how herbal cannabis dealers and buyers in the United States have adapted to the online sale of herbal cannabis through cryptomarkets. To achieve this goal, we evaluate the size and scope of the American herbal cannabis market on cryptomarkets and compare it to other drug markets from other countries, evaluate the impact of cryptomarkets on offline sales of herbal cannabis, and evaluate the ties between the now licit herbal cannabis markets in some States and cryptomarkets. Our results suggest that only a small fraction of herbal cannabis dealers and drug users have transitioned to cryptomarkets. This can be explained by the need for technical skills to buy and sell herbal cannabis online and by the need to have access to computers that are not accessible to all. The slow rate of adoption may also be explained by the higher price of herbal cannabis relative to street prices. If cryptomarkets were to be adopted by a larger portion of the herbal cannabis market actors, our results suggest that wholesale and regional distributors who are not active on cryptomarkets would be the most affected market’s participants.

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13

Ramya, G., and Ms Viji AmuthaMary. "Restricting Mischievous Users in Anonymizing Networks." International Journal of Engineering Trends and Technology 9, no. 2 (March 25, 2014): 88–92. http://dx.doi.org/10.14445/22315381/ijett-v9p218.

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14

Babu, Korra Sathya, Sanjay Kumar Jena, Jhalaka Hota, and Bijayinee Moharana. "Anonymizing social networks: A generalization approach." Computers & Electrical Engineering 39, no. 7 (October 2013): 1947–61. http://dx.doi.org/10.1016/j.compeleceng.2013.01.020.

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15

Tsang, Patrick P., Apu Kapadia, Cory Cornelius, and Sean W. Smith. "Nymble: Blocking Misbehaving Users in Anonymizing Networks." IEEE Transactions on Dependable and Secure Computing 8, no. 2 (March 2011): 256–69. http://dx.doi.org/10.1109/tdsc.2009.38.

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16

Vina Lomte, Prof, Pooja Gorlewar, Vishal Padghan, Kritika Gunjegaonkar, and Rashmi Kawale. "Nymble: Blocking Misbehaving Users In Anonymizing Networks." IOSR Journal of Computer Engineering 16, no. 1 (2014): 43–48. http://dx.doi.org/10.9790/0661-16124348.

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17

Ma, Jiangtao, Yaqiong Qiao, Guangwu Hu, Yongzhong Huang, Arun Kumar Sangaiah, Chaoqin Zhang, Yanjun Wang, and Rui Zhang. "De-Anonymizing Social Networks With Random Forest Classifier." IEEE Access 6 (2018): 10139–50. http://dx.doi.org/10.1109/access.2017.2756904.

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18

Yang, Ming, Junzhou Luo, Zhen Ling, Xinwen Fu, and Wei Yu. "De-anonymizing and countermeasures in anonymous communication networks." IEEE Communications Magazine 53, no. 4 (April 2015): 60–66. http://dx.doi.org/10.1109/mcom.2015.7081076.

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19

Fu, Luoyi, Jiapeng Zhang, Shuaiqi Wang, Xinyu Wu, Xinbing Wang, and Guihai Chen. "De-Anonymizing Social Networks With Overlapping Community Structure." IEEE/ACM Transactions on Networking 28, no. 1 (February 2020): 360–75. http://dx.doi.org/10.1109/tnet.2019.2962731.

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20

Chiasserini, Carla-Fabiana, Michel Garetto, and Emili Leonardi. "De-anonymizing Clustered Social Networks by Percolation Graph Matching." ACM Transactions on Knowledge Discovery from Data 12, no. 2 (March 13, 2018): 1–39. http://dx.doi.org/10.1145/3127876.

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21

Tahera, Mrs Umama. "Blacklisting Misbehaving Users for Enhancing Security in Anonymizing Networks." IOSR Journal of Computer Engineering 9, no. 5 (2013): 15–20. http://dx.doi.org/10.9790/0661-0951520.

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22

Zhang, Shiwen, Qin Liu, and Yaping Lin. "Anonymizing popularity in online social networks with full utility." Future Generation Computer Systems 72 (July 2017): 227–38. http://dx.doi.org/10.1016/j.future.2016.05.007.

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23

Xie, Yinglian, Fang Yu, and Martin Abadi. "De-anonymizing the internet using unreliable IDs." ACM SIGCOMM Computer Communication Review 39, no. 4 (August 16, 2009): 75–86. http://dx.doi.org/10.1145/1594977.1592579.

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24

Sun, Qi, Jiguo Yu, Honglu Jiang, Yixian Chen, and Xiuzhen Cheng. "De-anonymizing Scale-Free Social Networks by Using Spectrum Partitioning Method." Procedia Computer Science 147 (2019): 441–45. http://dx.doi.org/10.1016/j.procs.2019.01.262.

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25

Gao, Tianchong, Feng Li, Yu Chen, and XuKai Zou. "Local Differential Privately Anonymizing Online Social Networks Under HRG-Based Model." IEEE Transactions on Computational Social Systems 5, no. 4 (December 2018): 1009–20. http://dx.doi.org/10.1109/tcss.2018.2877045.

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26

Palanisamy, Balaji, Ling Liu, Kisung Lee, Shicong Meng, Yuzhe Tang, and Yang Zhou. "Anonymizing continuous queries with delay-tolerant mix-zones over road networks." Distributed and Parallel Databases 32, no. 1 (August 27, 2013): 91–118. http://dx.doi.org/10.1007/s10619-013-7128-4.

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27

Piacentino, Esteban, Alvaro Guarner, and Cecilio Angulo. "Generating Synthetic ECGs Using GANs for Anonymizing Healthcare Data." Electronics 10, no. 4 (February 5, 2021): 389. http://dx.doi.org/10.3390/electronics10040389.

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In personalized healthcare, an ecosystem for the manipulation of reliable and safe private data should be orchestrated. This paper describes an approach for the generation of synthetic electrocardiograms (ECGs) based on Generative Adversarial Networks (GANs) with the objective of anonymizing users’ information for privacy issues. This is intended to create valuable data that can be used both in educational and research areas, while avoiding the risk of a sensitive data leakage. As GANs are mainly exploited on images and video frames, we are proposing general raw data processing after transformation into an image, so it can be managed through a GAN, then decoded back to the original data domain. The feasibility of our transformation and processing hypothesis is primarily demonstrated. Next, from the proposed procedure, main drawbacks for each step in the procedure are addressed for the particular case of ECGs. Hence, a novel research pathway on health data anonymization using GANs is opened and further straightforward developments are expected.

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Zakerzadeh, Hessam, and Sylvia L. Osborn. "Delay-sensitive approaches for anonymizing numerical streaming data." International Journal of Information Security 12, no. 5 (March 26, 2013): 423–37. http://dx.doi.org/10.1007/s10207-013-0196-7.

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Otgonbayar, Ankhbayar, Zeeshan Pervez, and Keshav Dahal. "$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams." IEEE Internet of Things Journal 7, no. 2 (February 2020): 1438–50. http://dx.doi.org/10.1109/jiot.2019.2955435.

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NGUYEN-SON, Hoang-Quoc, Minh-Triet TRAN, Hiroshi YOSHIURA, Noboru SONEHARA, and Isao ECHIZEN. "Anonymizing Personal Text Messages Posted in Online Social Networks and Detecting Disclosures of Personal Information." IEICE Transactions on Information and Systems E98.D, no. 1 (2015): 78–88. http://dx.doi.org/10.1587/transinf.2014mup0016.

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31

Yin, Xiao Chun, Zeng Guang Liu, Bruce Ndibanje, Lewis Nkenyereye, and S. M. Riazul Islam. "An IoT-Based Anonymous Function for Security and Privacy in Healthcare Sensor Networks." Sensors 19, no. 14 (July 17, 2019): 3146. http://dx.doi.org/10.3390/s19143146.

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In the age of the Internet of Things, connected devices are changing the delivery system in the healthcare communication environment. With the integration of IoT in healthcare, there is a huge potential for improvement of the quality, safety, and efficiency of health care in addition to promising technological, economical, and social prospects. Nevertheless, this integration comes with security risks such as data breach that might be caused by credential-stealing malware. In addition, the patient valuable data can be disclosed when the perspective devices are compromised since they are connected to the internet. Hence, security has become an essential part of today’s computing world regarding the ubiquitous nature of the IoT entities in general and IoT-based healthcare in particular. In this paper, research on the algorithm for anonymizing sensitive information about health data set exchanged in the IoT environment using a wireless communication system has been presented. To preserve the security and privacy, during the data session from the users interacting online, the algorithm defines records that cannot be revealed by providing protection to user’s privacy. Moreover, the proposed algorithm includes a secure encryption process that enables health data anonymity. Furthermore, we have provided an analysis using mathematical functions to valid the algorithm’s anonymity function. The results show that the anonymization algorithm guarantees safety features for the considered IoT system applied in context of the healthcare communication systems.

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Khan, Rafiullah, Muhammad Arshad Islam, Mohib Ullah, Muhammad Aleem, and Muhammad Azhar Iqbal. "Privacy Exposure Measure: A Privacy-Preserving Technique for Health-Related Web Search." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1196–204. http://dx.doi.org/10.1166/jmihi.2019.2709.

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The increasing use of web search engines (WSEs) for searching healthcare information has resulted in a growing number of users posting personal health information online. A recent survey demonstrates that over 80% of patients use WSE to seek health information. However, WSE stores these user's queries to analyze user behavior, result ranking, personalization, targeted advertisements, and other activities. Since health-related queries contain privacy-sensitive information that may infringe user's privacy. Therefore, privacy-preserving web search techniques such as anonymizing networks, profile obfuscation, private information retrieval (PIR) protocols etc. are used to ensure the user's privacy. In this paper, we propose Privacy Exposure Measure (PEM), a technique that facilitates user to control his/her privacy exposure while using the PIR protocols. PEM assesses the similarity between the user's profile and query before posting to WSE and assists the user in avoiding privacy exposure. The experiments demonstrate 37.2% difference between users' profile created through PEM-powered-PIR protocol and other usual users' profile. Moreover, PEM offers more privacy to the user even in case of machine-learning attack.

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33

Himabindu, Ch. "The Challenges of Effectively Anonymizing Network Data." International Journal of Pharmacology and Pharmaceutical Technology, January 2013, 15–22. http://dx.doi.org/10.47893/ijppt.2013.1003.

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The availability of realistic network data plays a significant role in fostering collaboration and ensuring U.S. technical leadership in network security research. Unfortunately, a host of technical, legal, policy, and privacy issues limit the ability of operators to produce datasets for information security testing. In an effort to help overcome these limitations, several data collection efforts (e.g., CRAWDAD[14], PREDICT [34]) have been established in the past few years. The key principle used in all of these efforts to assure low-risk, high-value data is that of trace anonymization—the process of sanitizing data before release so that potentially sensitive information cannot be extracted.

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Tzanetakis, Meropi, David Décary-Hétu, Silje Bakken, Rasmus Munksgaard, Christian Katzenbach, Jakob Demant, Masarah Paquet-Clouston, and Laurin Weissinger. "DRUG MARKETS AND ANONYMIZING TECHNOLOGIES." AoIR Selected Papers of Internet Research, February 2, 2020. http://dx.doi.org/10.5210/spir.v2018i0.10463.

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Online drug markets taking advantage of social media and encryption software (e.g. Tor network) and cryptocurrencies (e.g. Bitcoin, Monero) to conceal the identity and physical location of their users are a relatively new area of internet research. Yet, a range of socio-technical innovations have contributed to the proliferation of drug markets on the Internet. Due to the illegality of drugs and drug dealing are anonymizing technologies regarded as important socio-technical practices among its participants allowing to mitigate risks of vendors and customers when exchanging drugs. This panel draws together a number of leading scholars in this emerging area of research to explore questions and issues associated with online platforms enabling illicit transactions. The collection of papers in this panel contribute empirical data and theoretical insight on a range of relevant topics in the study of online drug markets, including methodological challenges, social embeddedness, trust production and governance on cryptomarkets. Various papers in this panel propose new concepts for understanding cryptomarkets as social phenomena where relationships enable economic transactions. It also pluralizes trust building on online platforms and, expanding it from merely institution-based mechanisms to include social relations such as interpreting signs and signals or previous interactions between buyers and sellers. They also expand on reliability of data gathered via anonymous online interviews, drawing attention to participation of marginalized communities. The aim of this panel is to bring together new research to further our understanding of the overall impact of online platform emergence upon global drug markets and to better model their impact on drug dealing, online networks and society in general.

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Hodo, Elike, Xavier Bellekens, Ephraim Iorkyase, Andrew Hamilton, Christos Tachtatzis, and Robert Atkinson. "Machine Learning Approach for Detection of nonTor Traffic." Journal of Cyber Security and Mobility, November 3, 2017. http://dx.doi.org/10.13052/2245-1439.624.

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Intrusion detection has attracted a considerable interest from researchers and industry. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymizing the identity of internet users connecting through a series of tunnels and nodes. This work identifies two problems; classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users in using the UNB-CIC Tor Network Traffic dataset and classification of the Tor traffic flow in the network. This paper proposes a hybrid classifier; Artificial Neural Network in conjunction with Correlation feature selection algorithm for dimensionality reduction and improved classification performance. The reliability and efficiency of the propose hybrid classifier is compared with Support Vector Machine and naïve Bayes classifiers in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset. Experimental results show the hybrid classifier, ANN-CFS proved a better classifier in detecting nonTor traffic and classifying the Tor traffic flow in UNB-CIC Tor Network Traffic dataset.

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36

Linoy, Shlomi, Natalia Stakhanova, and Suprio Ray. "De‐anonymizing Ethereum blockchain smart contracts through code attribution." International Journal of Network Management, August 4, 2020. http://dx.doi.org/10.1002/nem.2130.

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37

Palmieri, Francesco. "A Distributed Flow Correlation Attack to Anonymizing Overlay Networks Based on Wavelet Multi-resolution Analysis." IEEE Transactions on Dependable and Secure Computing, 2019, 1. http://dx.doi.org/10.1109/tdsc.2019.2947666.

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38

Froese, Vincent, Brijnesh Jain, Rolf Niedermeier, and Malte Renken. "Comparing temporal graphs using dynamic time warping." Social Network Analysis and Mining 10, no. 1 (June 29, 2020). http://dx.doi.org/10.1007/s13278-020-00664-5.

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AbstractWithin many real-world networks, the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to compare different temporal graphs. To this end, we propose to study dynamic time warping on temporal graphs. We define the dynamic temporal graph warping (dtgw) distance to determine the dissimilarity of two temporal graphs. Our novel measure is flexible and can be applied in various application domains. We show that computing the dtgw-distance is a challenging (in general) -hard optimization problem and identify some polynomial-time solvable special cases. Moreover, we develop a quadratic programming formulation and an efficient heuristic. In experiments on real-world data, we show that the heuristic performs very well and that our dtgw-distance performs favorably in de-anonymizing networks compared to other approaches.

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