Top 10 Cyberattacked Industries in 2020
According to IBM in its X-Force Threat Intelligence Index, the top 10 industries suffering the most cyberattacks in 2020 were:
1. Finance and Insurance
Most Common Type of Attack: Server access attack
Since 2016, the finance and insurance industry has been the most attacked industry. In 2020, attacks increased over 2019 by 238%. Hackers seeking to profit financially from attacks often hit this industry. Paralyzing banks is usually less of a goal with attacks on this industry, but accessing internal systems can yield hefty illicit returns. However, during the pandemic, hackers seeking to paralyze infrastructure including nation-state cyber criminals also hit banking institutions to cause chaos.
2. Manufacturing
Most Common Type of Attack: Ransomware
The attacks against manufacturers doubled in 2020 compared to 2019. 21% of all 2020 ransomware cyberattacks hit the manufacturing industry. However, this industry also saw 4X more BEC attacks than any other industry, and a significant number of data theft attacks. Hackers renewed interest in this industry, likely trying to take advantage of supply chain disruption and operational chaos caused by the global pandemic, as consumers saw (and continue to experience) shortages in manufactured goods.
3. Energy
Most Common Type of Attack: Data theft
35% of attacks in the energy sector involved data theft, while only 6% involved ransomware. This was likely indicative of hacker motivations to hit this industry including IP theft, customer data theft and extorsion. BEC and server access attacks were also notable in this industry.
4. Retail
Most Common Type of Attack: Credential theft
Attacks on the retail industry were actually lower in 2020 than in 2019. This was likely due to fewer retail transactions taking place during the 2020 (less room to hide as a hacker). Retail is typically a target because of the high volume of credit card and financial transactions.
5. Professional Services
Most Common Type of Attack: Ransomware
Professional services saw the highest percentage of attacks from ransomware attacks of any industry. Data theft and server access attacks were also common in 2020. These organizations are typically attractive to cyber criminals because they serve as a path to further victims and often hold confidential data about customers.
6. Government
Most Common Type of Attack: Ransomware
Government received the second highest number of ransomware attacks of the industries, totaling a third of the attacks that this industry faced. Yet, only 38% of state and local government employees have been trained on ransomware prevention. This industry also faced a large burden in moving operations to accommodate work from home environments, as much of this industry had all team members working on site, and was not equipped for moving a remote workforce. Data theft attacks were also notable.
7. Healthcare
Most Common Type of Attack: Ransomware
The healthcare industry suffered twice as many cyberattacks in 2020 than in 2019, likely due to hackers taking advantage of operational chaos caused by the global pandemic, shifts in workforce to cover emergency medical care while furloughing operational and administrative staff due to revenue challenges caused by elective medical services being put on hold. Hackers, including nation-states looking to disrupt and steal data from organizations in this industry, targeted those in medical research and development attempting to invent COVID-19 vaccines, as well as frontline providers.
8. Media
Most Common Type of Attack: Malicious domain name squatting
90% of malicious domain name spoofing attempts targeted the media. This sector includes telecommunications and mobile communications providers, as well as media and social media outlets that can play a critical role in political outcomes, especially during election years. The timing of 2020 being a U.S. presidential election year likely drove this type of attack in this industry.
9. Transportation
Most Common Type of Attack: Malicious insider / misconfiguration
Attacks against the transportation industry were much lower in 2020, likely because everyone was sheltering in place and travel bans existed across the globe during 2020. This industry ranked #3 in 2019 and fell to #9 in 2020.
10. Education
Most Common Type of Attack: Spam / adware
Education has historically been vulnerable to cyberattacks due to a large decentralized surface area of users hard to control with staff and students regularly logging into systems from home to complete and grade course work. However, the user and device based security measures that this distributed surface area drove for security protection before the pandemic, better equipped education for security operations during the pandemic than other industries accustomed to relying on firewall protection.
Center for Automotive Research Management Briefing Seminar
Center for Automotive Research Management Briefing Seminar
Grand Traverse Resort, Traverse City, MI
Aunalytics recognized for successful acquisition of Naveego, Inc. during the Automotive Communities Partnership luncheon
Aunalytics was recognized by the Grand Traverse Economic Development Corporation during the Automotive Communities Partnership luncheon of the Center for Automotive Research Management Briefing Seminar as a success story for its acquisition of Traverse City-based Naveego, Inc. Unlike many acquisitions that involve dismantling operations, Aunalytics builds communities and is pleased to add Traverse City to our operational bases. Since the merger, Naveego’s technology has become key data integration, cleansing, and data management technologies in the Aunalytics data platform. All team members have continued jobs as part of the 250+ employee organization and the company is creating new jobs in Traverse City. The GTEDC applauds our success story and welcomes Aunalytics to Northern Michigan.
Katie Horvath, CMO Aunalytics and Dennis Arouca, Board Member of Grand Traverse Economic Development Corporation pose together at the Automotive Communities Partnership luncheon
What are Smart Features?
Machine learning is a leading component of today’s business landscape, but even many forward-looking business leaders in the mid-market have difficulty developing a strategy to leverage these cutting edge techniques for their business. At Aunalytics, our mission is to improve the lives and businesses of others through technology, and we believe that what many organizations need to succeed in today’s climate is access to machine learning technology to enhance the ability to make data driven-decisions from complex data sources.
Imagine having the ability to look at a particular customer and understand based on past data how that individual compares in terms of various factors driving that business relationship:
- Which of our products is this customer most likely to choose next?
- How likely is this customer to default or become past due on an invoice?
- What is churn likelihood for this customer?
- What is the probable lifetime value of this customer relationship?
Aunalytics’ Innovation Lab data scientists have combed through data from our clients in industries like financial services, healthcare, retail, and manufacturing and have developed proprietary machine learning techniques based on a solid understanding of the data commonly collected by businesses in these sectors.
A SHAP (Shapley Additive Explanations) value chart for a remarkably accurate loan default risk model we developed shows which features have the highest impact on risk prediction.
From this, we append insights gleaned from machine learning to data models. We add high value fields to customer records to reveal insights about a customer learned from our algorithms. Smart Features provide answers to pressing business questions to recommend next steps to take with a particular customer to deepen relationships, provide targeted land and expand sales strategies, provide targeted marketing campaigns for better customer experiences, and yield business outcomes.
Machine learning techniques enable more accurate models of risk, propensity, and customer churn because they represent a more complex model of the various factors that go into risk modeling. Our models deliver greater accuracy than simpler, statistical models because they understand the relationship between multiple indicators.
Smart Features are one way that Aunalytics provides value to our clients by lending our extensive data science expertise to client-specific questions. Through these machine learning enriched data points, clients can easily understand a particular customer or product by comparing it to other customers with similar data. Whether you want to know if a customer is likely to select a new product, their default risk, churn likelihood, or any other number of questions, our data scientists and business analysts are experienced and committed to answering these questions based on years of experience with businesses in your industry.
How Data Quality Impacts Manufacturing 4.0
How Data Quality Impacts Manufacturing 4.0
At a time when major advances in machine learning (ML), artificial intelligence (AI) and internet of things (IoT) are being promoted in the industry, many manufacturers continue to struggle to incorporate advanced analytics into their business processes. Critical to the success of any data analytics initiative is having clean data. The data must be accurately labeled, free of duplicate records, and blended to generate the correct results. This is key to becoming a truly data-driven enterprise.
Aunalytics is a data platform company. We deliver insights as a service to answer your most important IT and business questions.
Global Manufacturing Company Automates Data Flow Between CRM and Webportal for Improved Customer Experience
