[{"content":"Stack: AWS EC2 · VPC · IAM · CloudWatch Logs · S3 · Athena · Terraform · Cowrie\nCode: github.com/Aditiicodes/CloudPot\nWhat it is A single-instance SSH honeypot running Cowrie on Ubuntu 22.04, deployed into an isolated AWS VPC and left exposed on port 22 to live internet traffic for seven days in March 2026. Every session was logged as JSON, shipped through the CloudWatch agent to S3, and queried with Athena.\nDesign decisions Egress containment. Inbound is deliberately open; outbound is not. The network ACL permits only DNS and HTTPS to AWS endpoints, so a honeypot that gets fully compromised still can\u0026rsquo;t be used as a pivot or a scanning host. This is the part of the build that mattered most.\nDownloads blocked. Cowrie can retrieve the payloads attackers try to fetch. This deployment doesn\u0026rsquo;t; it logs the URLs and stops there. That trades away payload hashes in exchange for not storing malware in an S3 bucket, and removes an entire class of handling and legal questions from a student project.\nLeast-privilege IAM. The instance role can write to one log group and one S3 prefix. No wildcards on action or resource.\nReal SSH relocated first. Administrative access moved to a non-standard port, restricted by source CIDR, before Cowrie bound port 22.\nLimitations A seven-day window is enough to characterize background internet scanning, not enough to say anything about targeted activity or weekly periodicity. Nothing observed here is confirmed malicious intent; it\u0026rsquo;s automated traffic against an unremarkable IP.\n","permalink":"https://aditiindre.com/projects/cloudpot/","summary":"A Cowrie honeypot on AWS, exposed to live internet traffic for seven days, with the telemetry pipeline and analysis to match.","title":"CloudPot: AWS SSH Honeypot"},{"content":"Authors: Aditi Indre, Jayesh Shinde, Dr. Srivaramangai Ramanujam\nPublished: IJRASET, 23 March 2025 · ISSN 2321-9653\nDOI: 10.22214/ijraset.2025.67796\nA survey of the deepfake detection landscape, focused on multimodal approaches that combine visual, audio, and physiological signals rather than relying on any single modality.\nThe paper reviews state-of-the-art detection models (CNNs, Vision Transformers, XGBoost classifiers) and their evaluation against the Celeb-DF and FaceForensics++ benchmarks. It covers facial landmark analysis, voice consistency checking, and heart-rate variability extraction as complementary detection signals.\nThe central argument is that generalization, not raw accuracy, is the open problem. Models perform well against the manipulation techniques they were trained on and degrade against novel ones, which makes adversarial training and adaptive approaches more important than incremental accuracy gains. Real-time processing and interpretability remain unsolved for forensic and legal use, where an unexplainable classifier isn\u0026rsquo;t useful regardless of its score.\nRead the paper\n","permalink":"https://aditiindre.com/research/deepfakes-media-authenticity/","summary":"First-author survey of multimodal deepfake detection (visual, audio, and physiological cues), published in IJRASET.","title":"The Impact of Deepfakes on Digital Media Authenticity"},{"content":"Stack: Python · Z-score anomaly detection · NIST CSF 2.0 · pytest\nCode: github.com/Aditiicodes/Nasdaq-TradeView\nWhat it is A market surveillance tool that scores NASDAQ-100 equities for suspicious trading activity. Each ticker\u0026rsquo;s price movement and volume are compared against that same stock\u0026rsquo;s own trailing 90-day mean and standard deviation. The two resulting Z-scores feed four weighted fraud sub-scores (pump and dump, money laundering, insider trading, volume anomaly), which average into a 0 to 100 risk score.\nScores tier into three bands: Critical (≥60) blocks the transaction in the UI, High (35 to 59) requires confirmation, Low (\u0026lt;35) passes.\nWhy the logic lives in Python, not just the browser The front end is a single static HTML file with no build step. But detection logic inside a \u0026lt;script\u0026gt; tag can\u0026rsquo;t be unit-tested, cited, or reproduced independently, which matters for a project whose premise is an auditable, explainable method. So the same formulas exist as an authoritative Python module:\nTestable: 11 unit tests covering the Z-score formula, threshold boundaries, liquidity and market-closed flags, the insider-bonus gate, and score bounding Reproducible: one script re-derives every number in the UI straight from the source CSV; nothing is hand-typed Citable: one module is the reference implementation Honest limitations The sub-score weights are heuristic. They aren\u0026rsquo;t fit to labeled data, because no fraud ground truth exists in this dataset; flagged events are statistically extreme trading days, not regulator-confirmed manipulation. Validating against SEC AAER or FINRA enforcement data is the obvious next step.\nClient-side transaction blocking suits a research demo and would not survive production; a real deployment enforces that server-side.\n","permalink":"https://aditiindre.com/projects/nasdaq-tradeview/","summary":"An independent project: statistical anomaly detection over NASDAQ-100 market data, mapped to NIST CSF 2.0, with the detection logic split out as a tested Python package.","title":"NASDAQ TradeView: Financial Fraud Detection Platform"},{"content":"Venue: CCSC Central Plains 2026 · Student Posters session\nPresented: 11 April 2026 · Drury University, Springfield, MO (program)\nInstitution: Saint Louis University\nAbstract: ccsc.org/centralplains/2026/talks/talk-G8\nAn incident analysis of the May 2017 WannaCry outbreak, examined through a threat intelligence lens.\nWannaCry exploited EternalBlue, a vulnerability in Microsoft\u0026rsquo;s SMB protocol, to propagate without user interaction, worm behavior that let it spread globally within hours. The operational impact fell hardest on critical infrastructure: hospitals cancelled surgeries, and total losses were estimated in the billions.\nThe report covers the technical and operational factors behind the outbreak, the mitigation and remediation strategies available at the time, the residual risks organizations still carry, and executive-level recommendations for ransomware resilience.\n","permalink":"https://aditiindre.com/research/wannacry-eternalblue/","summary":"Threat intelligence analysis of the WannaCry outbreak and the EternalBlue SMB vulnerability. Student poster, CCSC Central Plains 2026.","title":"Cybersecurity Incident Report: WannaCry Ransomware"},{"content":"Stack: Android · Firebase Authentication · Firebase database\nCode: github.com/Aditiicodes/Recipe-App\nA community-driven recipe platform: users authenticate, create and share recipes, save favourites into collections, and rate and review what others post. Built iteratively with user testing between releases.\nThe security-relevant part is access control. Firebase defaults to open test-mode rules, so any app with user-generated content needs scoped read and write rules written deliberately.\n","permalink":"https://aditiindre.com/projects/bon-appetit/","summary":"A community recipe app built on Android and Firebase, with authentication, user-generated content, and access rules.","title":"Bon Appetit: Recipe Sharing Android App"},{"content":"Stack: Proxmox VE · OPNsense · NetBird · Technitium DNS · Ubuntu\nDocs: proxlink.aditiindre.com\nTeam: Group project with Dhanraj Chavan. Aditi configured the firewall, the network segmentation, and the Technitium DNS blocklists.\nWhat it is A home lab on one Beelink SER5 Pro mini PC (Ryzen 7 5800H, 32 GB RAM, 1 TB NVMe) running Proxmox VE 8 in place of the stock Windows install. Virtual machines sit behind a virtualized firewall, split into separate trust zones.\nNetwork segmentation Proxmox provides three bridges, one per subnet:\nWAN (vmbr0, 192.168.1.0/24): the upstream router and internet access LAN (vmbr1, 192.168.2.0/24): trusted internal VMs, such as an Ubuntu Desktop DMZ (vmbr2, 192.168.3.0/24): semi-trusted, exposed services, such as an Ubuntu Server Firewall An OPNsense VM has an interface on each bridge and handles routing between the zones, NAT and port forwarding, and inbound and outbound filtering.\nDNS filtering Technitium DNS Server is the resolver for devices on the LAN and DMZ, handed out by OPNsense over DHCP. Blocklists stop ads, trackers, and malicious domains network-wide.\nRemote access A NetBird routing peer exposes the LAN, and optionally the DMZ, to authorized remote peers over WireGuard. That traffic still passes through OPNsense, so the same firewall rules apply to remote access as to local traffic.\n","permalink":"https://aditiindre.com/projects/proxlink/","summary":"A group-built home lab on a single mini PC: Proxmox VE, an OPNsense firewall segmenting WAN, LAN, and DMZ networks, Technitium DNS blocklists, and NetBird for remote access.","title":"ProxLink: Proxmox Home Lab"},{"content":"Background I\u0026rsquo;m passionate about cybersecurity, with a strong interest in ethical hacking, digital forensics, and secure application development. I have both Android and web development skills, and I focus on robust security measures within development. I enjoy studying emerging technologies, identifying vulnerabilities, and contributing to the safety of the digital ecosystem through continuous learning and collaboration.\nI completed an M.S. in Cybersecurity at Saint Louis University (Jun 2024 to May 2026) through a 1+1 joint degree that began at Mumbai University (2024 to 2025).\nMy work sits where detection engineering meets analysis: standing up infrastructure that generates real security telemetry, then doing something useful with what comes back. A honeypot that logs live internet attacks. A fraud-detection engine with unit tests and documented thresholds. A pentest with a remediation writeup rather than a screenshot of a flag.\nExperience Cybersecurity Team Member, Cyber Billikens\nJan 2026 to Present\nProject Manager Intern, Excelerate\nJun 2025 to Jul 2025\nCybersecurity Operations Intern, CyberFrat\nApr 2025 to Jul 2025\nWeb Development Intern, Prodigy InfoTech\nJun 2024\nApplication Developer, LinkSum\nAug 2023 to Aug 2024\nData Science Intern, YHills\nJun 2023 to Aug 2023\nEducation Saint Louis University, St. Louis, MO\nM.S. Cybersecurity · Jun 2024 to May 2026 · GPA 4.0\n1+1 joint degree with Mumbai University, Mumbai, India (2024 to 2025)\nCoursework: Digital Forensics, Incident Response, Network Security, Cryptography\nUniversity of Mumbai, Mumbai, India\nB.Sc. Computer Science · Jun 2021 to May 2024 · GPA 3.5\nCoursework: Computer Networks, Operating Systems, Database Management Systems\nCertifications CompTIA Security+ ISC2 Certified in Cybersecurity (CC) Courses \u0026amp; programs\nAWS Academy Graduate - Generative AI Foundations (AWS Academy) Mastercard Cybersecurity Job Simulation (Forage) Game Hacking (CyberFrat) The Agnirva Space Internship Program (Indian Space Research Organisation) Research Methodology (Udemy) Community Black Hat USA and DEF CON 34 scholarship recipient (2026) Volunteered at BSides Las Vegas: registration, badge check, and speaker operations Skills Languages: Python, Bash, SQL, PowerShell, JavaScript, C, C++, Java, HTML, PHP\nSecurity Tools: Splunk, Wireshark, Nmap, Burp Suite, OWASP ZAP, Metasploit, Nessus, CrowdStrike Falcon, Semgrep, SonarQube, Ghidra, Autopsy, FTK Imager, Cowrie\nCloud \u0026amp; Infrastructure: AWS (EC2, VPC, IAM, CloudWatch, S3, Athena), Microsoft Azure, GCP, Terraform, Docker, Kubernetes, Linux (Kali, Ubuntu), Active Directory, Git, GitHub\nDevelopment: Android (Firebase), web development, secure application development\nSecurity Practices: Network traffic analysis, log analysis, incident detection and response, vulnerability assessment, digital forensics, threat detection engineering, MITRE ATT\u0026amp;CK mapping, OSINT, cryptography, firewalls, IDS/IPS, VPNs, NIST CSF 2.0, OWASP Top 10\nProject \u0026amp; Analysis: Agile methodologies, SDLC, requirements gathering, risk assessment, risk tracking, stakeholder communication, status reporting, data analysis, data visualization, research and report writing\nContact aditi.indre@slu.edu · linkedin.com/in/aditi-indre · github.com/Aditiicodes\n","permalink":"https://aditiindre.com/about/","summary":"\u003ch2 id=\"background\"\u003eBackground\u003c/h2\u003e\n\u003cp\u003eI\u0026rsquo;m passionate about cybersecurity, with a strong interest in ethical\nhacking, digital forensics, and secure application development. I have both\nAndroid and web development skills, and I focus on robust security measures\nwithin development. I enjoy studying emerging technologies, identifying\nvulnerabilities, and contributing to the safety of the digital ecosystem\nthrough continuous learning and collaboration.\u003c/p\u003e\n\u003cp\u003eI completed an M.S. in Cybersecurity at Saint Louis University (Jun 2024 to\nMay 2026) through a 1+1 joint degree that began at Mumbai University\n(2024 to 2025).\u003c/p\u003e","title":"About"}]