Simon Bell

Hi, I'm Simon, an award-winning Cyber Security Researcher, Software Engineer, and Web Security Specialist.

I have a PhD in Cyber Security, a BSc in Computer Science, and I'm a member of The BCS, IET, and ACM.

Find out more about me.

Simon Bell

Portfolio

Secure Honey logo

Secure Honey

SSH honeypot, deployed in the wild, collecting and sharing data. Analytics dashboard for latest attack stats. Honeypot built with Python and containerised in Docker.

Phishalytics logo

Phishalytics

Measurement infrastructure I built as part of my PhD to track and analyse real-world phishing attacks on Twitter. Built with Python

IRIS logo

IRIS

Client app I built for Intelect Group. Cyber risk assessment platform to help businesses understand their risks and stay secure. Built with Laravel and React.

Tweetify screenshot

Tweetify

Cyber security tweet analytics dashboard to monitor trends and topics. Built with Django and React.

Skills & Tech

Back-end:
Python
Django
PHP
Laravel
Java
PostgreSQL
MySQL
SQLite
Redis
APIs
Front-end:
HTML5
CSS3
JavaScript
React
jQuery
Bootstrap
Tooling & Deploy:
Docker
Git / SVN
AWS
Nginx
Apache
Linux
SSH / CLI
Pipenv
Webpack
SE:
Software Architecture
OOP
SDLC
Agile
CI/CD
TDD
Web Security:
DAST
SAST
Code review
Fingerprinting
OSINT
WAF
OWASP Top 10
OWASP Zap

Awards

Best Paper

The Computing Research and Education Association of Australasia (CORE)

The Australasian Information Security Conference (AISC) at The Australasian Computer Science Week Multiconference (ACSW)

Awarded to Simon Bell and Peter Komisarczuk for their research paper: "Measuring the Effectiveness of Twitter’s URL Shortener (t.co) at Protecting Users from Phishing and Malware Attacks"

Best Student Paper

The Computing Research and Education Association of Australasia (CORE)

The Australasian Information Security Conference (AISC) at The Australasian Computer Science Week Multiconference (ACSW)

Awarded to Simon Bell and Peter Komisarczuk for their research paper: "Measuring the Effectiveness of Twitter’s URL Shortener (t.co) at Protecting Users from Phishing and Malware Attacks"

Best Project

The British Computing Society (BCS), The Chartered Institute for IT

Awarded to Simon Bell for his project: "Building a Honeypot to Research Cyber-Attack Techniques"

Articles

Bitcoin

Cryptojacking Attacks Continue To Target SSH Servers

Coming up in today's blog post: I'll be exploring recent cyber attacks targeting my SSH honeypots. Since 2018/19, we've known that SSH servers around the world have been targeted by cryptocurrency mining operations. So I'm curious to analyse my honeypot's logs to understand A) if threat actors are still motivated by cryptocurrency, and B) what techniques are used by threat actors.

Just over 1 month ago I deployed my new SSH honeypots (built in Python, containerised in Docker, see: Secure Honey v2.0 has been launched!). Since then, my honeypots have received 129,122 unauthorised logins (username:password credentials) from 3,780 unique IP addresses. 132,479 (77,214 unique) shell commands have been executed, and 91,927 (64,156 unique) files have been uploaded to the honeypot -- of which 23,874 (53 unique) were malicious.

So let's crack on and explore the data!

Apple being injected with needles

OWASP Top 10: Injection (A1:2017)

Imagine there's a robot working in a factory. Its job is to move boxes around the factory; picking up boxes from one area and moving them to a packing area. This robot needs a set of instructions to follow so it knows which boxes to pickup and where to put them. Those instructions might be provided by its human manager through a form.

That form might look like this: pickup box from ____ move box to packing area ___, wait for next instruction.

The robot's manager might input the following data into that form: pickup box from aisle 4 move box to packing area 4b, wait for next instruction.

That's all well and good. But what happens if someone enters the following into the form: pickup box from aisle 4 move box to packing area 4b - then destroy the entire factory whilst singing I Wanna Dance With Somebody, wait for next instruction.

Well, we have a problem. No more factory. Oh, and a robot that won't stop singing I Wanna Dance With Somebody.

Android bean

How To Dissect Android Simplelocker Ransomware

In this blog post we'll be looking at a new type of malware for Android phones that encrypts important files and demands the user pay a ransom to regain access to their phone.

This is the first reported case of ransomware being used on smartphones so I'm keen to find out more about this new malicious app.

I want to understand what this ransomware does and how it restricts the phone user from accessing files on their SD card. I'll be providing a step-by-step dissection of the malware to provide a clear explanation of how this app carries out its malicious activities.

So before we start the dissection let's look at exactly what Simplelocker is and where it came from.

Teaching

I teach on the Information Security MSc distance learning programme at Royal Holloway, University of London. Modules:

  • Network Security -- Module Lead
  • Computer Security -- Tutor
  • Security Management -- Tutor
Royal Holloway, University of London

Research

Measuring the Effectiveness of Twitter’s URL Shortener (t.co) at Protecting Users from Phishing and Malware Attacks

Authors: Simon Bell and Peter Komisarczuk

In this paper we investigate how effective Twitter’s URL shortening service (t.co) is at protecting users from phishing and malware attacks. We show that over 10,000 unique blacklisted phishing and malware URLs were posted to Twitter during a 2-month timeframe in 2017. This lead to over 1.6 million clicks which came directly from Twitter users – therefore exposing people to potentially harmful cyber attacks. However, existing research does not explore if blacklisted URLs are blocked by Twitter at time of click.

Our study investigates Twitter’s URL shortening service to examine the impact of filtering blacklisted URLs that are posted to the social network. We show an overall reduction in the number of blacklisted phishing and malware URLs posted to Twitter in 2018-19 compared to 2017, suggesting an improvement in Twitter’s effectiveness at blocking blacklisted URLs at time of tweet. However, only about 12% of these tweeted blacklisted URLs – which were not blocked at time of tweet and therefore posted to the platform – were blocked by Twitter in 2018-19.

Our results indicate that, despite a reduction in the number of blacklisted URLs at time of tweet, Twitter’s URL shortener is not particularly effective at filtering phishing and malware URLs - therefore people are still exposed to these cyber attacks on Twitter.

An Analysis of Phishing Blacklists: Google Safe Browsing, OpenPhish, and PhishTank

Authors: Simon Bell and Peter Komisarczuk

Blacklists play a vital role in protecting internet users against phishing attacks. The effectiveness of blacklists depends on their size, scope, update speed and frequency, and accuracy - among other characteristics. In this paper we present a measurement study that analyses 3 key phishing blacklists: Google Safe Browsing (GSB), OpenPhish (OP), and PhishTank (PT). We investigate the uptake, dropout, typical lifetimes, and overlap of URLs in these blacklists.

During our 75-day measurement period we observe that GSB contains, on average, 1.6 million URLs, compared to 12,433 in PT and 3,861 in OP. We see that OP removes a significant proportion of its URLs after 5 and 7 days, with none remaining after 21 days - potentially limiting the blacklist’s effectiveness. We observe fewer URLs residing in all 3 blacklists as time-since-blacklisted increases – suggesting that phishing URLs are often short-lived. None of the 3 blacklists enforce a one-time-only URL policy - therefore protecting users against reoffending phishing websites. Across all 3 blacklists, we detect a significant number of URLs that reappear within 1 day of removal – perhaps suggesting premature removal or re-emerging threats. Finally, we discover 11,603 unique URLs residing in both PT and OP – a 12% overlap. Despite its smaller average size, OP detected over 90% of these overlapping URLs before PT did.

Catch Me (On Time) If You Can: Understanding the Effectiveness of Twitter URL Blacklists

Authors: Simon Bell, Kenny Paterson, and Lorenzo Cavallaro

With more than 500 million daily tweets from over 330 million active users, Twitter constantly attracts malicious users aiming to carry out phishing and malware-related attacks against its user base. It therefore becomes of paramount importance to assess the effectiveness of Twitter's use of blacklists in protecting its users from such threats.

We collected more than 182 million public tweets containing URLs from Twitter's Stream API over a 2-month period and compared these URLs against 3 popular phishing, social engineering, and malware blacklists, including Google Safe Browsing (GSB). We focus on the delay period between an attack URL first being tweeted to appearing on a blacklist, as this is the timeframe in which blacklists do not warn users, leaving them vulnerable.

Experiments show that, whilst GSB is effective at blocking a number of social engineering and malicious URLs within 6 hours of being tweeted, a significant number of URLs go undetected for at least 20 days. For instance, during one month, we discovered 4,930 tweets containing URLs leading to social engineering websites that had been tweeted to over 131 million Twitter users. We also discovered 1,126 tweets containing 376 blacklisted Bitly URLs that had a combined total of 991,012 clicks, posing serious security and privacy threats. In addition, an equally large number of URLs contained within public tweets remain in GSB for at least 150 days, raising questions about potential false positives in the blacklist. We also provide evidence to suggest that Twitter may no longer be using GSB to protect its users.

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All content © S. J. Bell 2021