[SHEHZADE//ABDULLAH ANSARI]
Red Team Consultant @ Google | MS in Cybersecurity @ NYU
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PUBLIC MIRRORGo v1.0
Multi-protocol network egress assessment and DLP verification framework featuring automated L4 sweeps, corporate proxy traversal, and covert protocol smuggling across 20+ channels.
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| 1 | package dlp |
| 2 | |
| 3 | import ( |
| 4 | "fmt" |
| 5 | "math/rand" |
| 6 | "strings" |
| 7 | ) |
| 8 | |
| 9 | type EmployeeProfile struct { |
| 10 | ID string `json:"id"` |
| 11 | FirstName string `json:"first_name"` |
| 12 | LastName string `json:"last_name"` |
| 13 | Email string `json:"email"` |
| 14 | PhoneUS string `json:"phone_us"` |
| 15 | PhoneE164 string `json:"phone_e164"` |
| 16 | Department string `json:"department"` |
| 17 | JobTitle string `json:"job_title"` |
| 18 | Street string `json:"street"` |
| 19 | City string `json:"city"` |
| 20 | State string `json:"state"` |
| 21 | ZIP string `json:"zip"` |
| 22 | SSN string `json:"ssn"` |
| 23 | Salary int `json:"salary"` |
| 24 | } |
| 25 | |
| 26 | var firstNames = []string{"James", "Mary", "Robert", "Patricia", "John", "Jennifer", "Michael", "Linda", "David", "Elizabeth", "William", "Barbara", "Richard", "Susan", "Joseph", "Jessica", "Thomas", "Sarah", "Charles", "Karen", "Christopher", "Nancy", "Daniel", "Lisa", "Matthew", "Margaret", "Anthony", "Betty", "Donald", "Sandra", "Mark", "Ashley", "Paul", "Dorothy", "Steven", "Kimberly", "Andrew", "Emily", "Kenneth", "Donna"} |
| 27 | var lastNames = []string{"Smith", "Johnson", "Williams", "Brown", "Jones", "Garcia", "Miller", "Davis", "Rodriguez", "Martinez", "Hernandez", "Lopez", "Gonzalez", "Wilson", "Anderson", "Thomas", "Taylor", "Moore", "Jackson", "Martin", "Lee", "Perez", "Thompson", "White", "Harris", "Sanchez", "Clark", "Ramirez", "Lewis", "Robinson", "Walker", "Young", "Allen", "King", "Wright", "Scott", "Torres", "Nguyen", "Hill", "Flores"} |
| 28 | var departments = []string{"Engineering", "Information Security", "Finance", "Legal", "Human Resources", "Product Management", "Sales Operations", "Customer Support", "Executive"} |
| 29 | var jobTitles = []string{"Senior Software Engineer", "Staff Security Architect", "Financial Analyst", "Corporate Counsel", "HR Business Partner", "Lead Product Manager", "Enterprise Account Exec", "DevOps Engineer", "Director of Infrastructure"} |
| 30 | var streets = []string{"Market St", "Main St", "Silicon Way", "Park Ave", "Lexington Ave", "Oak St", "Pine Rd", "Cedar Ln", "Maple Blvd", "Washington St"} |
| 31 | var citiesStates = []struct{ City, State, ZIPPrefix string }{ |
| 32 | {"San Francisco", "CA", "941"}, |
| 33 | {"New York", "NY", "100"}, |
| 34 | {"Austin", "TX", "787"}, |
| 35 | {"Seattle", "WA", "981"}, |
| 36 | {"Chicago", "IL", "606"}, |
| 37 | {"Boston", "MA", "021"}, |
| 38 | } |
| 39 | |
| 40 | func GenerateRandomName(r *rand.Rand) string { |
| 41 | return firstNames[r.Intn(len(firstNames))] + " " + lastNames[r.Intn(len(lastNames))] |
| 42 | } |
| 43 | |
| 44 | func GenerateRandomPhone(r *rand.Rand) string { |
| 45 | area := 200 + r.Intn(800) |
| 46 | prefix := 200 + r.Intn(800) |
| 47 | line := 1000 + r.Intn(9000) |
| 48 | return fmt.Sprintf("(%d) %d-%04d", area, prefix, line) |
| 49 | } |
| 50 | |
| 51 | func GenerateRandomAddress(r *rand.Rand) string { |
| 52 | num := 100 + r.Intn(9900) |
| 53 | street := streets[r.Intn(len(streets))] |
| 54 | cs := citiesStates[r.Intn(len(citiesStates))] |
| 55 | zip := fmt.Sprintf("%s%02d", cs.ZIPPrefix, r.Intn(100)) |
| 56 | return fmt.Sprintf("%d %s, %s, %s %s", num, street, cs.City, cs.State, zip) |
| 57 | } |
| 58 | |
| 59 | func GenerateEmployeeProfile(r *rand.Rand, domain string) EmployeeProfile { |
| 60 | fn := firstNames[r.Intn(len(firstNames))] |
| 61 | ln := lastNames[r.Intn(len(lastNames))] |
| 62 | if domain == "" { |
| 63 | domain = "acme-corp.internal" |
| 64 | } |
| 65 | email := fmt.Sprintf("%s.%s@%s", strings.ToLower(fn), strings.ToLower(ln), domain) |
| 66 | cs := citiesStates[r.Intn(len(citiesStates))] |
| 67 | zip := fmt.Sprintf("%s%02d", cs.ZIPPrefix, r.Intn(100)) |
| 68 | area := 200 + r.Intn(800) |
| 69 | pfx := 200 + r.Intn(800) |
| 70 | line := 1000 + r.Intn(9000) |
| 71 | |
| 72 | return EmployeeProfile{ |
| 73 | ID: fmt.Sprintf("EMP-%06d", r.Intn(1000000)), |
| 74 | FirstName: fn, |
| 75 | LastName: ln, |
| 76 | Email: email, |
| 77 | PhoneUS: fmt.Sprintf("(%d) %d-%04d", area, pfx, line), |
| 78 | PhoneE164: fmt.Sprintf("+1%d%d%04d", area, pfx, line), |
| 79 | Department: departments[r.Intn(len(departments))], |
| 80 | JobTitle: jobTitles[r.Intn(len(jobTitles))], |
| 81 | Street: fmt.Sprintf("%d %s", 100+r.Intn(9900), streets[r.Intn(len(streets))]), |
| 82 | City: cs.City, |
| 83 | State: cs.State, |
| 84 | ZIP: zip, |
| 85 | SSN: GenerateSSN(r, true), |
| 86 | Salary: 65000 + r.Intn(140000), |
| 87 | } |
| 88 | } |