02158nas a2200241   4500008004100000020002200041245004500063210004500108260002800153520147700181653001501658653001801673653002101691653001701712653002301729653001801752653001301770653001901783100001801802700001501820700002301835856005801858       2021                        eng d  a978-1-6654-3578-900aData Cooperatives for Neighborhood Watch0 aData Cooperatives for Neighborhood Watch  aSydney, AustraliabIEEE3 a<p>The increasing proliferation of user data is moving the world from the era of 'big data' to a new era of shared data, and some are considering data as a factor of production that is paving the way for a new business economy. In this paper, we propose a solution that uses blockchain technology as a platform for online neighborhood watch using a form of data cooperative among individuals or organizations in the sharing of data through a peer-to-peer mechanism. We prove the concept by implementing a distributed phishing data sharing system that will maintain a community ledger of reported phishing activities with a consensus-based approval of the phishing transaction and a novel reputation scoring system thereby adding reliability to the system and effectively tackling the phishing problem. The data cooperative provides a way for timely multi-party sharing of phishing data among anti-phishing organizations and users of the internet eliminating the current approach of each organization maintaining its database. Our results show that blockchain is effective in complementing the existing methods of phishing detection and serves as a platform for sharing phishing data with respect to scalability, cost, and memory consumption. Also, our results further show that transaction times on the Ropsten test net follow a Gamma distribution. Our approach can be extrapolated to other data sharing systems like medical data, spam calls, discussion forums, etc.</p>
10ablockchain10aCybersecurity10adata cooperative10adata sharing10adistributed ledger10apeer-to- peer10aphishing10asmart contract1 aSalau, Abiola1 aDantu, Ram1 aUpadhyay, Kritagya  uhttps://ieeexplore.ieee.org/abstract/document/946105601962nas a2200181   4500008004100000245003100041210003100072490000600103520151100109653002501620653002101645653001301666653000901679100001501688700002001703700002001723856003701743       2008                        eng d00aClassification of phishers0 aClassification of phishers0 v53 a<p>Phishing attackers masquerade as genuine senders and try to steal consumers' personal identity data and financial account credentials. In spite of aggressive efforts, technology companies have had limited success in restricting phishing attacks. Unfortunately the nature of phishing attacks changed over time from passive, such as password guessing and eavesdropping to active attacks, such as employing Trojans to intercept traffic and adopting social engineering techniques. No matter how many authentication techniques we develop, phishers always adapt. However, phishers cannot become part of the recipient's social network without consent. Though they can forge certain fields in an email header, phishers do not have access to the complete header. In this paper, we describe techniques for detecting phishers based on their traffic paths, traffic patterns, and on the receivers' social network. Considering such issues, we based our solution on the trustworthiness of the relays participating in routing the emails. We examine the email's header rather than the content. We designed our classifier to perform the following analyses in four steps: i) DNS-header analysis, ii) Social network analysis, iii) Wantedness analysis, and iv) Proactive classification. We classify phishers into: i) Serial phishers, ii) Recent phishers, iii) Prospective phishers, and iv) Suspects. Finally, our classifier appends an alert level or label to the email's "subject" before adding the email to the inbox.</p>
10aapplication security10anetwork security10aphishing10aspam1 aDantu, Ram1 aPalla, Srikanth1 aCangussu, João  uhttps://nsl.cse.unt.edu/node/235