Malicious Profile Identification in Online Social Networks

Dima Kagan

Supervisors: MICHAEL FIRE,  YUVAL ELOVICI

Complex Networks

Related Work

  • Reputation based filtering  [Golbeck and Hendler].
  • Topoplogy based identification [Fire et al.].
  •  Graph centrality measure based spammer identification [DeBarr and Wechsler].
  • Spammers  detection in social networks by using “honey-profiles" [Stringhini et al.].
  • Clustering  groups of accounts that act similarly at around the same time for a sustained period of time [Cao et al.].

Link Prediction

+

Crowd Wisdom

Supervised Fake Profile
Identification in Online Social Networks

  1. Fake profiles dataset - Recommended restricted links set + All unrestricted links set.
  2. Friends restriction dataset -  Alphabetically restricted links set + All unrestricted links set.
  3. All links dataset -  contains all the links.

Collected Datasets

Collected Data

Users Restricted Unrestricted
Fake-Profiles 434 2,860 138,286
​Friends Restrictions 355 6,145 138,286
​All Links 527 9,005 138,286

Features

Labeling Data is Hard

Unsupervised Anomaly Detection in Graphs Utilizing a Link Prediction Algorithm

Malicious Users Tend to Connect to Other Profiles Randomly

Topology Based

Feature Extraction

16 feautres

for directed

graphs

8 feautres for

undirected

graphs

◦ For undirected graphs:

  • Common Friends
  • Total Friends 
  • Jaccard’s-Coefficent 

 

     

    \frac{|\Gamma(v) \cap \Gamma(u)|}{|\Gamma(v) \cup \Gamma(u)|}
    ∣Γ(v)∩Γ(u)∣∣Γ(v)∪Γ(u)∣\frac{|\Gamma(v) \cap \Gamma(u)|}{|\Gamma(v) \cup \Gamma(u)|}
    |\Gamma(v) \cup \Gamma(u)|
    ∣Γ(v)∪Γ(u)∣|\Gamma(v) \cup \Gamma(u)|
    |\Gamma(v) \cap \Gamma(u)|
    ∣Γ(v)∩Γ(u)∣|\Gamma(v) \cap \Gamma(u)|
    |\Gamma(v)_{in}| \cap |\Gamma_{out}(u)|
    ∣Γ(v)in∣∩∣Γout(u)∣|\Gamma(v)_{in}| \cap |\Gamma_{out}(u)|
    \begin{cases} 1, & \text{if}\ (u,v)\in E \\ 0, & \text{otherwise} \end{cases}
    {1,if (u,v)∈E0,otherwise \begin{cases} 1, & \text{if}\ (u,v)\in E \\ 0, & \text{otherwise} \end{cases}

      ◦ For directed graphs:

    • Transitive Friends
    • Opposite Direction Friends

    Link Classification

    Aggregation of The Results

    \sum_{}
    ∑\sum_{}

    Meta Feature Exteraction

    AbnormalityVertexProbability(v) := \frac{1}{|\Gamma(v)|}\sum\nolimits_{u \in \Gamma(v)}p(v,u)
    AbnormalityVertexProbability(v):=1∣Γ(v)∣∑u∈Γ(v)p(v,u)AbnormalityVertexProbability(v) := \frac{1}{|\Gamma(v)|}\sum\nolimits_{u \in \Gamma(v)}p(v,u)

    We extracted 9 features​

    •                 - the confidence that an edge is fake.   
    •  
    p(v,u)
    p(v,u)p(v,u)

    Datasets

    Fully Simulated  Networks

    Semi Simulated Networks

    Real World Networks

    Kids Friendship Network

    AUC - 0.93
    AUC−0.93AUC - 0.93
    TPR - 0.91
    TPR−0.91TPR - 0.91
    FPR- 0.15
    FPR−0.15FPR- 0.15

    Twitter

    https://github.com/Kagandi/anomalous-vertices-detection

    Questions?

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