People are beginning to get used to the easy life enabled by smart homes and the Internet of Things (IoT), but they are at risk if they do not prioritize security. The IoT has increased quality of life for many; it has helped the differently abled and the elderly to continue living independently and laid the technological groundwork for another industrial revolution. On the other hand, it has also enticed cybercriminals to commit fraud in new and novel ways.

The Challenge of Combating IoT Crime

In October 2015, the FBI advised citizens to be vigilant with their IoT devices. In recent years, cybercriminals have progressed from targeting computers and networks to people, medical devices, power grids, cars, kitchen appliances and other connected devices. In addition, search engines such as Shodan and Censys create pools of potential IoT devices for fraudsters to target.

Smart devices are the main components of the IoT. They are easy to use and deploy and are usually connected to the internet without any security enabled. These devices range from smart locks to medical equipment, TVs, refrigerators, light controls, security systems, baby monitors and automobiles, any of which can be used to steal personal information, spread malicious code, eavesdrop or even interfere with the operation of machinery. In a worst-case scenario, these malicious acts can potentially put human lives at risk.

Due to the rising number of connected devices, it has become necessary to develop new approaches and tap all available resources to combat future crimes. This new strategy should be implemented in the early stages of the investigation, particularly when investigators don’t know where to begin.

Listen to the podcast series: Five Indisputable Facts About IoT Security

Classifying Cybercrime With an IoT Perspective

It is difficult to collect evidence to support IoT crime cases, and even more challenging to connect various pieces of evidence. That’s why it’s important to consider the classification of the crime, the methods of collecting evidence and all relevant laws and regulations.

Classification is a data mining technique that categorizes data to aid in a more accurate analysis. Classification should also help analysts predict the target class for each new case in the data. IoT-related crimes can be categorized into the following three classes.

1. The IoT as a Target

These crimes are relatively new, which explains why organizations and individuals around the world are largely unprepared to combat them. They are usually committed by a select group of criminals who have the computer skills and scientific knowledge to execute attacks on smart devices. This class of IoT crime involves attackers exploiting vulnerabilities in smart devices, such as pacemakers, medical infusion pumps, smart cars and sniper rifles, and executing malicious instructions that could endanger human life.

2. The IoT as a Tool

The target of this type of crime is not the IoT device itself, but the smart device used to commit the offense. In this scenario, identifying and prosecuting the perpetrators is more difficult. This class of crime usually requires less technical expertise and depends on manufacturer-introduced vulnerabilities. Since security is typically not the main focus of device manufacturers, IoT devices are perfect tools for attackers to build botnets to execute large distributed denial-of-service (DDOS) attacks. A prominent example is the Mirai botnet, which used connected devices to attack various high-profile technology providers. Attackers typically exploit vulnerabilities such as fixed encryption keys, default passwords and failure to patch or update device firmware.

3. The IoT as an Eyewitness

These are the crimes that have existed for centuries, such as trespassing, homicide and kidnapping. The only difference here is that these crimes take place in smart environments. Motion sensors, climate controls and smart-light logs can record the exact time of an intrusion and indicate the intruder’s route throughout the house, which can help investigators determine where to look for fingerprints. Smart locks can indicate whether the intruder brute-forced, hacked or leveraged a legitimate code to enter the smart home. Additionally, wireless access points (WAP) may have historical logs of wireless connection attempts and other local WAP activities, which could contain unintentional connection attempts from the intruder’s phone.

The Value of IoT Crime Classification

Classification of IoT crimes can enable investigators to efficiently assess large amounts of information and rapidly extract intelligence from the huge amount of collected data. It also reduces the time it takes to identify threat actors and helps investigators focus on a smaller number of potential suspects. Studying similar cases from the same class should provide guidance on relevant evidence, the legal admissibility process and evidence verification methods.

Classification of IoT crimes can also prepare investigators to combat emerging threats, even in the absence of proper processes. By assigning a new crime to a predefined category, investigators can begin collecting and examining evidence based on past experience.

more from Endpoint

IOCs vs. IOAs — How to Effectively Leverage Indicators

Cybersecurity teams are consistently tasked to identify cybersecurity attacks, adversarial behavior, advanced persistent threats and the dreaded zero-day vulnerability. Through this endeavor, there is a common struggle for cybersecurity practitioners and operational teams to appropriately leverage indicators of compromise (IOCs) and indicators of attack (IOAs) for an effective monitoring, detection and response strategy. Inexperienced security […]

TrickBot Gang Uses Template-Based Metaprogramming in Bazar Malware

Malware authors use various techniques to obfuscate their code and protect against reverse engineering. Techniques such as control flow obfuscation using Obfuscator-LLVM and encryption are often observed in malware samples. This post describes a specific technique that involves what is known as metaprogramming, or more specifically template-based metaprogramming, with a particular focus on its implementation […]