Tag Archives: consumer tech

Four Common IoT Security Holes

If you follow the Internet of Things space, not a day passes where you do not see an analyst report or news article talking about IoT security vulnerabilities across every sector: consumer, enterprise, industrial and government/Smart City.

I’ve been working with Internet-connected devices (medical devices, industrial actuators, sensors for environmental, security monitoring, even military systems) for many years. In my job, I am lucky enough to able to work with industrial and enterprise devices daily. At home, I play with them both as a consumer and developer. Time and again, I see the following IoT security holes with alarming frequency:

Security Hole #1: Not Using Strong Encryption

It is amazing that in 2016 people are still not using strong encryption to protect important data. However, I frequently see IoT devices that use no encryption at all: they store and transmit data in the clear. Other devices use homegrown encryption techniques that are are unproven by peer review and relatively easy to hack.

Most of the arguments I have seen against encryption fall into three camps: 1) it is too computationally expensive for low-powered devices, 2) it is too hard to use for IoT protocols, and 3) the device data is too obscure to understand. Let’s look at each:

  1. Yes, encryption is computationally expensive. However, ongoing investments in the space are providing more efficient RSA, AES, and ECC algorithms that work on smaller devices. In addition, Moore’s Law is even allowing penny-sized devices to have enough power to use these.
  2. IoT protocols are also getting better and better at providing strong encryption and secure connections (see Security Hole #2).
  3. Finally, the old “Our-data-is-too-obscure-for-hackers-to-understand Argument” was proven a fallacy years ago, first by the credit card industry’s Cardholder Information Security Program, and later by its replacement: PCI DSS. Any disgruntled employee (or hacker masquerading as a contractor) can bypass the “obscurity protection.”

Not using strong encryption is probably the most egregious security vulnerability. Any 14-year-old can use downloadable packet sniffing programs to capture your data. Solutions that mitigate this risk are readily available. There is no excuse to not encrypting your data.

Security Hole #2: Not Using Secured Sessions

A common error is information/cyber security is forgetting that secure communication consists of two components:

  1. Encryption of data and
  2. Establishment of secured sessions

Secured sessions use protocols to establish mutual authentication and to exchange  shared secret that only the transmitter and receiver have. If you do not establish a secured session you are blindly guessing that the recipient of your data is the correct person. When you do not use secured session you invite a Man-In-The-Middle (MITM) attack where the attacker can intercept and redirect your transmissions.

Many people think they are not likely targets of a MITM attack. Here is simple scenario.

  • A disgruntled employee or hacker-posing-as-contractors first intercepts and copies traffic from your devices.
  • From this data, he learns what devices are attached to items of interest (a patient, your house, etc.). He can then also learn the normal pattern of communication from the device.
  • Next he replaces the data from your device to send his own. This can give the appearance that a patient who is sick is now health (or vice versa) or that your house is not being broken into (allowing his partners to break in). The hacker can even intercept your over-the-air commands and download programmable software or send commands to shut-down devices.

This work is technically hard, but doable with software downloadable on the Internet. If communication between your IoT devices and your secured (and encrypted), the hacker would have to gain enough permissions to get a hold of your SSL certificates and hijack DNS (if he has this, you are in a lot of trouble already). However, if the communication between your IoT devices and servers is not secured, a hacker can conduct this MITM attack from anywhere. By the time you learn about it, the damage will be long done.

Thankfully, there are many solutions available in the IoT domain that provide both strong encryption and secured sessions (plugging Security Holes #1 and #2):

  • If you are using standard “Internet of Servers” protocols, simply installing a full compliment of certificates will enable you to use SSL over TLS for HTTPS and FTPS (but not SFTP).
  • If you are using MQTT (one of my favorites), there are many brokers available that also provide SSL over TLS.
  • If you are using CoAP (which rides over UDP), you can use DTLS.
  • If your devices have edge constellations, you can turn on Bluetooth Security Mode 4 and get SSL with the same Elliptic Curve Diffie-Hellman secret key exchange used by the NSA.
  • You can even download and borrow the wonderful MTproto protocol designed by the folks over at Telegram (it is designed for low-powered, lossy, distributed communication).

None of these solutions are perfect. However, all reduce security risks significantly. Furthermore, all are evolving in the open source community as people find new vulnerabilities. Why more people do not use them is puzzling.

Security Hole #3: Not Protecting Against Buffer Overflow

When a hacker triggers a Buffer Overflow vulnerability, she typically causes a program to do two things: dump critical data and crash.

The first documented cases of Buffer Overview exploits data back to 1972. As more and more computers were connected to the Internet, these attacks became more pervasive. Fifteen years ago, Code Red highlighted to much of the general public what a Buffer Overflow exploit can do.

Over the past few years, application framework libraries have and higher-level languages, have added many defensive programming protection to make these vulnerabilities less prevalent than they were in the past. (As anyone who has encountered an awlful error page that shows you a stack trace error, these defenses are still far-from-perfect). Nevertheless, they have plugged many holes.

However, IoT devices are bringing this vulnerability back into the mainstream again. As most IoT devices operate with far less memory and CPU than expensive devices like your laptop or smartphone, their firmware and applications are primarily written in lower level programing languages. It is much easier to trigger buffer overflows in these languages than more forgiving higher level languages. Exception handling libraries are less robust. More often than not, memory management is handled using good old-fashioned C/C++ programming (there is no Garbage Collector to save you). This significantly raises the risk of buffer overflows in devices.

When buffer overflow crashes occur in the data center there is at least someone around to fix things. When they happen to a remote IoT device in the field, they can literally shut down a security or medical sensor. There is no IT or Ops department nearby to fix it. The device is shut down (at best, or bricked at worst). Essentially device is dead to world. Depending on what is was responsible for, lots real-world physical damage can ensure.

Devices that maintain continuously open Internet connections (like all those connected baby monitors) are especially prone to buffer flow attacks as remote hackers can discover them using port-scanning software. However, even industrial IoT devices that only pull commands and programs down over-the-air are vulnerable to MITM attacks that can shut them down by flooding data to the device (this reinforces the need to plug Security Holes #1 and #2 discussed above).

The fix to this problem is fairly clear:  implement defensive programming and test it aggressively. Today’s automation technologies for continuous integration and delivery make this a much easier and trustworthy process than it was even a decade ago.

Security Hole #4: Weak Systems Engineering

The fourth big security hole I commonly see spans the intersection of technical design, system processes, and human behavior. It essentially boils down to this: if you use flawless technology in ways that it is not intended, you can create big vulnerabilities. If I design perfectly secure medical device but put it on the wrong patient (accidentally or maliciously), I will prevent capture of data about that sensor. If someone who installs the security sensors in my house sets my account up to call their cell phone (and not mine), they can break in while I am gone and I trick the company into thinking it is a false alarm.

The way around this is to design IoT devices that work when things (humans, the network, servers, etc.) fail.

  • Build in redundancy (devices, network paths and servers) to mitigate technical failures
  • Build in positive and negative feedback looks to mitigate human failures. For example, I should not just be notified if my home security sensor goes off. I should should be notified if my smartphone and my security companies servers both cannot communicate with my home security IoT devices.

Plugging this systems engineering IoT security hole takes a combination of technology engineering and business process design.  This is a natural fit to the enterprise, where IoT can be used as a component of business transformation. In the consumer segment the answer is usually an ecosystem solution. Amazon’s and Google’s solutions stand out regarding robustness and security.


The Internet of Things offers great potential to transform how we work and live by removing many tedious tasks from our day-to-day activities. Making this a reality requires a secure Internet of Things. We will never make security perfect. However, we have the tools to make it trustworthy. What is needed is just the discipline to include them as we build new IoT devices, systems and processes.

The Expanding (Digital) Universe: Visualizing How BIG a Zettabyte Really Is

Note: This post was originally published at Oulixeus Consulting

A lot of news articles recently (Google News currently shows 1,060 articles) are citing the annual EMC-IDC Digital Universe studies of the massive growth of the digital universe through 2020. If you have not read the study, it indicates that the digital universe is now doubling every two years and will grow 44-fold 50-fold now 55-fold from 0.8 Zettabytes (ZB) of data in 2009 to 35 40 now 44 Zettabytes in 2020. (Every year IDC has revised the growth curve upward by several Zettabytes.)

Usually these articles show a diagram such as this:


This type of diagram is great at showing how much 44-fold growth is. However it really does not convey how big a Zettabyte really is—and how much data we will be swimming (or drowning in) by 2020.

A Zettabyte (ZB) is really, really big – in terms of today’s information systems. It is not a capacity that people encounter every day. It’s not even in Microsoft Office’s spell-checker, Word “recommended” that I meant to type “Petabyte” instead 😉

The Raw Definition: How big is a Zettabyte?

A Computer Scientist will tell you that 1 Zettabyte is 270 bytes. That does not sound very big to a person who does not usually visualize think in exponential or scientific notation—especially given that a one-Terabyte (1 TB) solid state drive has a capacity to store 240 bytes.

Wikipedia describes a ZB (in decimal math) as one-sextillion bytes. While this sounds large, it is a hard to visualize. It is easier to visualize 1 ZB (and 44 ZBs) in relation to things we use everyday.

Visualizing Zettabytes in Units of Smartphones

The most popular new smartphones today have 32 Gigabytes (GB) or 32 x 230 bytes of capacity. To get 1 ZB you would have to fill 34,359,738,368 (34.4 billion) smartphones to capacity. If you put 34.4 billion Samsung S5’s end-to-end (length-wise) you would circle the Earth 121.8 times:

Click to see a higher resolution image and the dot that represents Earth to-scale vs. the line

You can actually circumnavigate Jupiter almost 11 times—but that is not obvious to visualize.

The number of bytes in 44 Zettabytes is a number too large for Microsoft Excel to compute correctly. (The number you will get is so large that Excel will cut off seven digits of accuracy–read that as a potential rounding error up to one million bytes). Assuming that Moore’s Law will allow us to double the capacity of smartphones three times between now and 2020, it would take 188,978,561,024 (188+ trillion) smartphones to store 44 ZB of data. Placing these end-to-end- would circumnavigate the world over nearly 670 times.

This is too hard to visualize, so lets look at it another way. You could tile the entire City of New York two times over (and the Bronx and Manhattan three times over) with smartphones filled to capacity with data to store 44 ZBs. That’s a big Data Center!

Amount of Smartphones (with 2020 tech) you would need to store 44 ZB (click for higher resolution)

This number also represents 25 smartphones per person for the entire population of the planet. Imagine the challenge of managing data spread out across that many smartphones.

Next Page: Visualizing Zettabytes in Units of Facebook