01 / DEMONSTRATED IN THIS PROJECT
Security analysis
Authentication event triage, configurable time-window rules, evidence linking, and false-positive reasoning.
FINAL-YEAR MCA · LOVELY PROFESSIONAL UNIVERSITY
Curious by nature.
Defensive
by design.
I’m Adarsh. I turn cybersecurity concepts into clear, testable tools — starting with the signals hidden in authentication logs.
Seeking entry-level cybersecurity & software opportunities
SENTINEL LOG LABPROJECT / 001
12SYNTHETIC EVENTS
03DETECTION RULES
01EXPLAINABLE PROJECT
Observe. Correlate. Investigate.Try the demo ↗
DEFENSIVE SECURITYPYTHON & JAVASCRIPTEVIDENCE-LED ANALYSISCONTINUOUS LEARNING
01 / PROFESSIONAL PROFILE
Build with purpose.
Explain with clarity.
A final-year Master of Computer Applications student at Lovely Professional University, focused on defensive cybersecurity and software development.
My academic foundation spans network security, digital forensics, programming, and data systems. I’m connecting those subjects through practical projects that make security decisions traceable: clear inputs, understandable rules, and evidence you can inspect.
I’m looking to contribute to entry-level SOC, security analyst, and junior software roles while deepening my skills in log analysis, detection engineering, and secure application development.
02 / SKILLS & FOUNDATIONS
A broad foundation.
A focused practice.
Skills are shown through the project and relevant coursework, with room to grow.
02 / DEMONSTRATED IN THIS PROJECT
Building & testing
Python CLI and browser JavaScript implementations, input validation, JSON reporting, and behavioral tests.
03 / ACADEMIC FOUNDATION
Systems & networks
TCP/IP, access controls, network administration, digital forensics, cloud computing, and vulnerability assessment concepts.
04 / ACADEMIC FOUNDATION
Software & data
Java, SQL and database design, algorithms, data structures, data mining, and big-data fundamentals.
Explore my MCA coursework
Mapped from my supplied study materials. Coursework describes my academic foundation; it does not imply professional proficiency in every subject.
The finance elective “Security Analysis and Portfolio Management” concerns investments, not cybersecurity. Course mapping and source references are included in the project download.
03 / FEATURED PROJECT
DEFENSIVE / EDUCATIONAL PROTOTYPE
Sentinel
Log Lab.
From raw authentication events to explainable investigation leads.
The problem
A single failed login tells you little. Patterns across time, accounts, and sources can reveal activity worth investigating.
The solution
Validate normalized events, correlate them with three explicit rules, and attach the contributing event IDs to every finding. Tune the window and threshold to explore the trade-offs.
THE OUTPUT
A structured report with priority, evidence, suggested checks, configuration, and an input digest.
A small, inspectable pipeline
- ValidateCheck timestamps, IPv4 addresses, outcomes, IDs, and input limits.
- CorrelateSort events; group failures by source, account, and service.
- ExplainRecord rule matches and their exact supporting evidence.
- ExportDownload a JSON report with settings and a SHA-256 digest.
Python and JavaScript implement the same rule semantics; a parity test compares the synthetic sample.
Investigation leads need context.
Retries, password changes, or a shared gateway can trigger a rule. A match is not proof of compromise; no matches do not prove safety.
This prototype accepts normalized JSON with IPv4 and UTC timestamps. It does not collect live logs, scan networks, block users, or parse native log formats.
Each rule emits once per key per file. It can miss low-rate, distributed, or successful-only compromise. A digest compares input bytes; it does not establish authenticity or chain of custody.
Built with AI assistance. The source and guide are available for independent review and extension.
Repeated failures
Multiple failed logins for one source, account, and service within a time window.
Possible password spraying
One source failing across several distinct accounts on the same service.
Success after failures
A successful login after repeated failures for the same source and account.
04 / INTERACTIVE SECURITY LAB
Explore the evidence.
Choose a scenario. Adjust the rules. See what changes.
Logs stay in your browser
Synthetic data: a failure burst, a later success, and cross-account failures.
02 / INSPECT & CONFIGURE
| UTC time | Account | Source IPv4 | Result |
|---|
Required fields: id, timestamp, src_ip, user, service, outcome. Import up to 2 MiB / 10,000 events.
Preparing the synthetic example…
Input schema & rule behavior
Each row requires unique id, exact UTC timestamp (YYYY-MM-DDTHH:MM:SSZ), IPv4 src_ip, user, service, and outcome (failure/success). Fields must be nonempty strings up to 128 characters. Invalid rows reject the whole input. Native log formats require a separate adapter.
The time window includes its boundary. A success clears that account/source/service failure history. Cross-account failure history remains. Each rule emits once per key per input; findings can overlap.
Download the example JSON ↓03 / REVIEW FINDINGS
Your findings will appear here.
Run analysis after changing the input or settings.
0Events analyzed
0Investigation leads
0High priority
Input integrity & report details
SHA-256 OF ANALYZED EDITOR TEXT
A digest identifies the analyzed UTF-8 text. Edits or newline normalization can change it from the original file. It does not prove authenticity or establish chain of custody.
Findings are triage signals, not confirmed attacks. Always verify the context before taking action.
05 / THE NEXT CHAPTER
Let’s build
something useful.
Interested in entry-level cybersecurity and software opportunities, internships, and practical learning projects.
EMAILadarsh2522015@gmail.com ↗ACADEMIC PATH
Final-year MCA
Lovely Professional University
This portfolio and educational prototype were prepared with AI assistance. Source, tests, limitations, and a project guide are included for review. Coursework is distinct from professional experience.