01962nas 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/23502674nas a2200349   4500008004100000245004900041210004900090300001400139490000600153520156800159653005601727653002201783653003401805653003401839653001201873653002201885653002101907653004201928653001501970653002901985653003702014653005002051653001902101653003302120653002402153653003102177653002102208100001502229700002002244700002302264856003702287       2007                        eng d00aFast Worm Containment Using Feedback Control0 aFast Worm Containment Using Feedback Control  a119 - 1360 v43 a<p>In a computer network, network security is accomplished using elements such as firewalls, hosts, servers, routers, intrusion detection systems, and honey pots. These network elements need to know the nature or anomaly of the worm a priori to detect the attack. Modern viruses such as Code Red, Sapphire, and Nimda spread quickly. Therefore, it is impractical if not impossible for human mediated responses to these fast-spreading viruses. Several epidemic studies show that automatic tracking of resource usage and control provides an effective method to contain the damage. In this paper, we propose a novel security architecture based on the control system theory. In particular, we describe a state-space feedback control model that detects and control the spread of these viruses or worms by measuring the velocity of the number of new connections an infected host makes. The mechanism's objective is to slow down a worm's spreading velocity by controlling (delaying) the number of new connections made by an infected host. A proportional and integral (PI) controller is used for a continuous control of the feedback loop. The approach proposed here has been verified in a laboratory setup, and we were able to contain the infection so that it affected less than 5 percent of the hosts. We have also implemented a protocol for exchanging control-specific information between the network elements. The results from the simulation and experimental setup combined with the sensitivity analysis demonstrate the applicability and accuracy of the approach.</p>
10acommunication/networking and information technology10acomputer networks10aComputer systems organization10acontrol engineering computing10ageneral10ainvasive software10anetwork security10anetwork-level security and protection10aPI control10aprocess control systems.10aproportional-integral controller10aspecial-purpose and application-based systems10astate feedback10astate-space feedback control10astate-space methods10atelecommunication security10aworm containment1 aDantu, Ram1 aCangussu, João1 aPatwardhan, Sudeep  uhttps://nsl.cse.unt.edu/node/202