13. Social Engineering and Phishingfairly popular. Another interesting finding, which confirms anecdotal beliefs,is that vishers often rely on interactive voice responders to automate theircalls. Recently, a study by Isacenkova et al. [219], based on a publicly availabledataset of 419 scams, showed that this type of phishing practices, which aresometimes initiated via email, are on the rise. Interestingly, as suggested byMaggi et al. [266] in the past, Isacenkova et al. also found that phone numbersare the cornerstone that allows the different campaigns to be grouped together;their experiments also show that it is possible to identify large groups ofscam campaigns probably run by the same criminal organizations. Suspiciousphone calls have also been put under the microscope of Fujitsu and NagoyaUniversity, which developed a proprietary technique for creating a model of ascammer’s typical voice tone [34]; together with the extraction of keywordscharacteristics of scams, Fujitsu’s system can detect suspicious situations of“overtrust.”13.6 Research GapsThe effectiveness of social engineering and phishing attacks lies in the factthat users are unsuspecting and tend to trust communication (seemingly)originating from online contacts and sent through inherently “compromisable”media (e.g., email, online social networks). Defending against such attacksrequires inter-disciplinary research in two orthogonal dimensions: (i) effectivemethods for educating users about the attacks, providing them with thebasic skills for identifying them, and (ii) developing defense mechanisms forautomatically identifying phishing attempts.A major challenge for both dimensions is the ever increasing spear phishingattacks. From a technical aspect, they are deployed on a much smaller scale,and are thus able to evade the existing infrastructure (e.g., spam-traps) thatcollects samples for updating spam filters. From a user perspective, the contentis crafted to resemble a legitimate communication and includes informationand details that are very convincing, and can trick even careful users. Overall,we expect that in the near future attackers will have incorporated and beheavily dependent on social engineering techniques for delivering their attacks;accordingly, researchers will also have to focus on implementing effectivecountermeasures.13.7 Example ProblemsEven though phishing and social engineering are a relatively cold topic from aresearch perspective, these issues still lack effective solution. In this sectionwe provide three example research problems, which all revolve around theidea of correlating phishing activities: the goal is to gain insights into howcybercriminals use their resources to carry out phishing and related threats.100
13.7. Example ProblemsThis will shed more light on their modus operandi and, hopefully, allowresearchers and practitioners to track them.Intelligence-gathering malware and spear phishing: the prerequisite of spearphishing is that the phisher has gathe<strong>red</strong> enough intelligence to “personalize”the interaction with the victim, with the goal of increasingthe chances of the victim falling prey to the scam. On the one hand,the miscreants are arguably collecting such “intelligence” informationmanually; on the other hand, the abundance of data-stealing malware,both for desktop and mobile platforms, raises the question of whetherthis malicious software could be the main source of such information.The answer to this question is arguably positive; however, the empiricalevidence is lacking, especially if we want to answer more complexquestions such as “to what extent can spear phishing be automated?”To answer this and related questions, an accurate data-collection andcorrelation activity must be carried out.Phishing and mobile malware infections: to bypass restrictions set by mobileOS, attackers incorporate social engineering to trick users into installingapplications. An in-depth study focusing on the correlationbetween mobile infections and the techniques used to trick users intoinstalling malicious apps, can provide researchers with valuable informationthat will lead to the implementation of monitoring components thatdetect such malicious activities and prohibit users from completing them.Apart from identifying the phishing techniques, these components candraw inspiration from traditional anti-phishing defenses such as domainblacklisting, and blacklisting malicious applications that have not beenremoved from application markets.Cross-channel phishing correlation: modern phishing is complex and ofteninvolves several channels (e.g., email, IM, SMS, phone, online socialnetworks). A holistic approach is thus requi<strong>red</strong> to observe and mitigatephishing more effectively. Based on an idea proposed in [266], we proposeto capture different aspects of phishing campaigns, with a particular focuson the emerging use of the voice channel. The general approach is torecord inbound calls received on honey phone lines, place outboundcalls to the same caller identifiers (when available) and also to telephonenumbers obtained from different sources. These sources include, forinstance, phishing or scam instant messages, suspicious emails (e.g.,spam, phishing). Extracted telephone numbers, URLs and popular wordscan be correlated to recognize campaigns by means of cross-channelrelationships between messages.101
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SEVENTH FRAMEWORK PROGRAMMETHERED B
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The Red Book. ©2013 The SysSec Con
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PrefaceAfter the completion of its
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Contents1 Executive Summary 32 Intr
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1 Executive SummaryBased on publish
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1.2. Grand Challenges4. will have t
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2. Introductionwho want to get at t
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2. Introduction• Although conside
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2. Introductionfuture, where each a
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2. Introductiondrones), such sensor
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2. Introductioncover our energy nee
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Part I: Threats Identified
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3. In Search of Lost Anonymity3.2 W
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3. In Search of Lost Anonymityguide
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4 Software VulnerabilitiesExtending
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4.1. What Is the Problem?infrastruc
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4.5. State of the Artparts of criti
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4.7. Example Problemstem mitigation
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5. Social Networks5.1 Who Is Going
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5. Social Networksby such an applic
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5. Social Networksdisasters. This r
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6. Critical Infrastructure Security
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6. Critical Infrastructure Security
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6. Critical Infrastructure Security
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6. Critical Infrastructure Security
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25.2. Research Themesand radio broa
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25.2. Research Themesconsists of se
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25.2. Research ThemesRisk managemen
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AMethodologiesIn this appendix we o
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BSysSec Threats Landscape Evolution
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B.4. SysSec 2013 Threats LandscapeT
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B.4. SysSec 2013 Threats LandscapeS
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Bibliography[1] 10 Questions for Ke
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Bibliography[45] SCADA & Security o
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Bibliography[88] A. Avizienis, J.-C
- Page 179 and 180:
Bibliography[130] G. Cluley. 600,00
- Page 181 and 182:
Bibliography[172] D. Evans. Top 25
- Page 183 and 184:
Bibliography[214] ICS-CERT. Monthly
- Page 185 and 186:
Bibliography[253] C. Lever, M. Anto
- Page 187 and 188:
Bibliography[291] Mozilla. Browseri
- Page 189 and 190:
Bibliography[329] F. Raja, K. Hawke
- Page 191 and 192:
Bibliography[370] T. Telegraph. Bog
- Page 193 and 194:
Bibliography[407] W. Yang, N. Li, Y