A Personalized Recommender Algorithm Based on Semantic Tree

2011 ◽  
Author(s):  
Zhaoguo Xuan ◽  
Haoxiang Xia ◽  
Jing Miao
Keyword(s):  
Semantic Tree ◽  
Recommender Algorithm

SARCP - Exploiting Cyber-Attack Prediction Through Socially-Aware Recommendation

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Social Network ◽  
Real World ◽  
Cyber Security ◽  
Betweenness Centrality ◽  
Cyber Attacks ◽  
Experimental Results ◽  
Cyber Attack ◽  
Defence Mechanisms ◽  
Network Measure ◽  
Recommender Algorithm

In the domain of cyber security, the defence mechanisms of networks has traditionally been placed in a reactionary role. Cyber security professionals are therefore disadvantaged in a cyber-attack situation due to the fact that it is vital that they maneuver such attacks before the network is totally compromised. In this paper, we utilize the Betweenness Centrality network measure (social property) to discover possible cyber-attack paths and then employ computation of similar personality of nodes/users to generate predictions about possible attacks within the network. Our method proposes a social recommender algorithm called socially-aware recommendation of cyber-attack paths (SARCP), as an attack predictor in the cyber security defence domain. In a social network, SARCP exploits and delivers all possible paths which can result in cyber-attacks. Using a real-world dataset and relevant evaluation metrics, experimental results in the paper show that our proposed method is favorable and effective.

scholarly journals SSID Oracle Attack on Undisclosed Wi-Fi Preferred Network Lists

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ante Dagelić ◽  
Toni Perković ◽  
Bojan Vujatović ◽  
Mario Čagalj
Keyword(s):  
Location Privacy ◽  
Real Life ◽  
User Mobility ◽  
Window Of Opportunity ◽  
Privacy Concerns ◽  
Private Location ◽  
Life Tests ◽  
Active Attacks ◽  
Recommender Algorithm ◽  
Dictionary Attacks

User’s location privacy concerns have been further raised by today’s Wi-Fi technology omnipresence. Preferred Network Lists (PNLs) are a particularly interesting source of private location information, as devices are storing a list of previously used hotspots. Privacy implications of a disclosed PNL have been covered by numerous papers, mostly focusing on passive monitoring attacks. Nowadays, however, more and more devices no longer transmit their PNL in clear, thus mitigating passive attacks. Hidden PNLs are still vulnerable against active attacks whereby an attacker mounts a fake SSID hotspot set to one likely contained within targeted PNL. If the targeted device has this SSID in the corresponding PNL, it will automatically initiate a connection with the fake hotspot thus disclosing this information to the attacker. By iterating through different SSIDs (from a predefined dictionary) the attacker can eventually reveal a big part of the hidden PNL. Considering user mobility, executing active attacks usually has to be done within a short opportunity window, while targeting nontrivial SSIDs from user’s PNL. The existing work on active attacks against hidden PNLs often neglects both of these challenges. In this paper we propose a simple mathematical model for analyzing active SSID dictionary attacks, allowing us to optimize the effectiveness of the attack under the above constraints (limited window of opportunity and targeting nontrivial SSIDs). Additionally, we showcase an example method for building an effective SSID dictionary using top-N recommender algorithm and validate our model through simulations and extensive real-life tests.

Googling the Web

2020 ◽  
pp. 143-158
Author(s):  
Chris Bleakley
Keyword(s):  
Web Sites ◽  
World Wide ◽  
Search Algorithm ◽  
Internet Usage ◽  
Web Pages ◽  
The World ◽  
Set Up ◽  
Recommender Algorithm ◽  
A Company ◽  
The Web

Chapter 8 explores the arrival of the World Wide Web, Amazon, and Google. The web allows users to display “pages” of information retrieved from remote computers by means of the Internet. Inventor Tim Berners-Lee released the first web software for free, setting in motion an explosion in Internet usage. Seeing the opportunity of a lifetime, Jeff Bezos set-up Amazon as an online bookstore. Amazon’s success was accelerated by a product recommender algorithm that selectively targets advertising at users. By the mid-1990s there were so many web sites that users often couldn’t find what they were looking for. Stanford PhD student Larry Page invented an algorithm for ranking search results based on the importance and relevance of web pages. Page and fellow student, Sergey Brin, established a company to bring their search algorithm to the world. Page and Brin - the founders of Google - are now worth US$35-40 billion, each.

scholarly journals SARPPIC: Exploiting COVID-19 Contact Tracing Recommendation through Social Awareness

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Nana Yaw Asabere ◽  
Amevi Acakpovi ◽  
Emmanuel Kwaku Ofori ◽  
Wisdom Torgby ◽  
Marcellinus Kuuboore ◽  
...  
Keyword(s):  
Contact Point ◽  
Healthcare Facility ◽  
Contact Tracing ◽  
Contact Points ◽  
Trace Back ◽  
Effective Contact ◽  
Hygiene Measures ◽  
Real World Datasets ◽  
Recommender Algorithm ◽  
Prevention Methods

Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.

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