A few years ago, I didn't think much about computer security. As long as my PC turned on, my files were safe, and I could browse the internet without any obvious problems, I assumed everything was fine.

That mindset changed after a friend told me how malware had locked important files on his computer. He eventually recovered them, but the experience made me realize how quickly a normal day could turn into a stressful one.

I didn't want to wait until something went wrong before taking security seriously. Instead of looking for a complicated solution, I created a simple monthly routine with the help of a free antimalware tool that helps me keep my computer organized and better protected. It doesn't take long, and it's become one of the easiest habits I've built.

Why I Started Taking Security More Seriously

Step 1: Check for Windows Updates

Features I Appreciate Most

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Knowing that my important documents have additional protection gives me extra peace of mind.

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Since I spend a lot of time online, I like having another layer of protection while browsing unfamiliar websites.

Introduction

Once the attacker has breached the corporate network, subsequent stages of the attack often involve leveraging standard domain infrastructure protocols: using Kerberos, running DNS queries, accessing internal services, opening network shares, and other common networking actions. Because this activity is virtually indistinguishable from legitimate network traffic, it is extremely difficult to detect it with traditional network attack detection tools.
Kerberoasting and DNS tunneling have long ceased to be exotic techniques. They are becoming standard methods in modern attacks because they allow attackers to execute critical compromise stages while remaining undetected by traditional security tools. A clear example of this trend is seen in latest campaigns, employing both Kerberoasting and DNS tunneling.

Traditional network security tools perform well when the attack features a distinct and identifiable indicator: a characteristic query string, a known malicious traffic pattern, or the source code of an already discovered exploit. While this approach to threat detection remains effective, it cannot always be applied to discovering network attacks that blend seamlessly with legitimate traffic inside a corporate network.

Instead of searching for explicit indicators of attack, Network Anomaly Detection (NAD) analyzes all traffic for suspicious artifacts that deviate from the host’s typical network activity. Within Kaspersky’s solution portfolio, this technology is implemented specifically in the Kaspersky Anti Targeted Attack (KATA) platform.

The system analyzes network traffic data (DNS, DCE/RPC, Kerberos and other packets) and extracts key parameters used to identify anomalous behavior. This approach enables searching for attacks on domain controllers, signs of traffic tunneling and exfiltration, C2 communications, and other scenarios that may point to compromise of network infrastructure.

However, Network Anomaly Detection is not built on a single, universal set of indicators. Each attack scenario employs tailored detection models that account for the specifics of the corresponding network protocol, typical host behavior, and characteristic deviations from that baseline. This article examines two practical examples – detecting Kerberoasting and DNS tunneling – to demonstrate how these principles are implemented in KATA’s NAD rules and why this approach proves more effective than traditional signature-based analysis.

Kerberoasting attack detection by KATA

Why standard tools have a hard time detecting Kerberoasting

The Kerberoasting attack leverages the standard operational logic of the Kerberos protocol. The attacker identifies service accounts configured with a Service Principal Name (SPN), requests a Ticket-Granting Service (TGS) ticket for them, and attempts to crack the password offline using a dictionary attack against the retrieved ticket. If the password is weak or hasn’t been changed in a long time, the adversary can bruteforce it to get it in cleartext. Subsequently, these compromised credentials can be leveraged for both vertical and horizontal movement across the network.

The essence of a Kerberoasting attack is that an adversary possessing a compromised low-privileged account and a valid Ticket-Granting Ticket (TGT) for that account can request TGS tickets with weakened encryption for service accounts with SPNs. Crucially, it doesn’t matter whether the compromised account actually holds access permissions for those services. Having obtained these tickets, the attacker can then take them offline and bruteforce the service account’s password by trying to decrypt the corresponding ticket locally, without generating any network activity. As the encryption key is based on the password hash, the adversary can guess the password upon finding the correct key.

The attacker’s objective is to find a service account that has a simple password. Most likely, this will be an account created manually by the administrators of the infrastructure or a service. This is precisely why attackers are not interested in system service accounts with SPNs (such as CIFS/fileserver.company.local); these are generated automatically and feature highly complex passwords that are impossible to bruteforce.

We should note that the TGS ticket requests made by attackers are identical to standard, legitimate requests. Every domain naturally exhibits a high volume of Kerberos traffic. Therein lies the primary challenge of detecting Kerberoasting: legitimate service ticket requests (TGS-REQ) are indistinguishable from those issued by attackers. Consequently, the primary detection method relies on correlating indirect indicators rather than signature matching. Key indicators include an anomalous request source (atypical host or user account), a surge in requested SPNs within a short time window, attempts to obtain service tickets for sensitive or privileged service accounts, and off-hour timing or unusual request volume when benchmarked against the historical profile of both the user and the host.

Most of these indicators can be detected using NAD technology, which helps analysts cut through high volumes of Kerberos traffic to establish a concrete hypothesis: who initiated the Kerberoasting attack, which service accounts are at risk, and why this activity deviates from the baseline.

In the context of this attack, the network anomaly stems from a single host – likely using a single user account (cname) – receiving TGS tickets ("msg_type": "KRB_TGS_REP") for numerous unique services with SPNs (sname) within a short timeframe. These service accounts are non-system accounts.

Example of a TGS-REQ – TGS-REP event pair from network session attributes

To detect this anomaly, the NAD rule titled “Signs of a Kerberoasting attack” implements the following logic:

1. From Kerberos network sessions during the search depth period, select only those with a successful Kerberos TGS-REP response, subject to the following conditions:
   • The IP address that initiated the session must not be excluded in the excl_sip variable.
   • The requesting client name (cname) must not be included in the excluded users list (excl_users variable).
   • The SPN (sname) must not be excluded within the rule. System SPNs are omitted from detection logic because they exist across most corporate environments and hold no interest for adversaries in this attack vector; including them in the total count of unique SPNs could lead to predefined threshold being exceeded, triggering false positives.
2. Extract the cname (the name of the client requesting the TGS-REQ) and sname (SPN itself) from these qualifying sessions.
3. Group the sessions by the source IP address and client account name (cname), while aggregating sessions with unique SPNs.
4. Generate an alert if a single IP address using a single client account receives TGS-REP responses for N unique SPN names within the specified search depth window, where N equals or exceeds the threshold variable count_spns.
5. Within the event regeneration window, group under the initial alert all subsequent alerts associated with the same client IP address. This avoids creating duplicate event records by incrementing the aggregation counter (Total appearances).

We should note that this type of logic cannot be implemented using IDS signatures. Consider creating a Suricata rule designed to detect Kerberos TGS-REP packets. To minimize false positives, we’ll exclude system SPNs (which carry highly complex passwords) and apply a threshold for the number of responses a single client can receive. However, such a rule cannot evaluate the uniqueness of the requested SPNs; it can only track packet counts. As a result, this signature would produce a high volume of false positives because any domain naturally generates large amounts of identical legitimate TGS-REP messages.

Furthermore, adding exclusions and tuning thresholds to fit your specific infrastructure environments is significantly more practical when managed through user variables in the interface rather than directly modifying the underlying structure of the IDS rule itself.

Creating a Network Anomaly Detection rule

Network Anomaly Detection (NAD) rules are written as SQL queries executed against KATA’s ClickHouse database. Below, we demonstrate how to add and deploy a rule.

To begin working with NAD rules, navigate to the “Custom rules” section of the interface and select “Intrusion detection”. Under the “Network Anomaly Detection” tab, you can create a new rule.

The Network Anomaly Detection page UI

When adding a new rule, an analyst can select an appropriate rule template from the prebuilt set supplied with product updates. They can also manually modify the rule added from the template (converting it to a custom rule while keeping the original template intact) or author a rule from scratch using the provided guide.

Upon selecting a template, the analyst can review the rule description and either adjust or leave the default values for the following settings:

• Search depth (the lookback window over which the SQL query will run)
• Schedule (the execution frequency for running the query against the specified search depth)
• Event regeneration period (the timeframe during which identical alerts will be aggregated into a single record rather than displayed as distinct events)

UI for creating a new NAD rule

To ensure the rule functions correctly, we recommend navigating to the “SQL-specific query” tab before deployment to review the variables used within the rule – a description for each variable is available by hovering over the question mark icon.

The variables are lists of IP addresses, dates, strings or numeric values that define the network infrastructure – such as domain controllers, DNS servers, time ranges, critical segments, and other entities. This allows you to tailor each rule to different network environments and incorporate specific infrastructure characteristics without modifying the underlying logic.

In our example, using variables allows you to adjust the “Signs of a Kerberoasting attack” rule as follows without altering the underlying SQL query:

• Exclude the source IP address of the TGS-REQ requests from the scope of detection logic (you can specify a single address, a subnet mask, or a dictionary containing addresses and subnets) as well as the requesting client account (accepts a single value or a dictionary with multiple values).
• Adjust the threshold value required to trigger an alert based on the number of unique SPNs in the TGS-REQ messages.

Query contents and variables used in the new rule

On this same page, you can test if the rule is functional prior to saving it.

Rule execution test results

When this rule triggers, an NDR:NAD alert is generated. In the alert card, the analyst can review basic information: IP addresses, ports, and participating network endpoints.

Alert card for the NAD rule

From there, the analyst can navigate to the associated event, which provides a detailed breakdown of the anomaly alongside links to the affected hosts.

NAD rule triggering event

If needed, the analyst can view and export the network sessions associated with the alert. These sessions can be accessed directly from the alert or within the event card via the “Show related” drop-down list.

Network sessions that triggered the rule

Within an individual session, the analyst can inspect standard details including interacting parties, data volume sent and received, and other fields and metrics. On the “Attributes” tab, the analyst can review the specific events recorded within that session.

Network session attributes

Detecting DNS tunneling in KATA

How DNS tunnels work

DNS tunneling is a technique used to transmit data or control malware through firewalls by encoding information within DNS protocol requests and responses. Instead of performing standard name resolution, an infected host transmits data encoded within subdomain strings and receives response data via DNS records. This covert channel can be leveraged for C2 communication, bypassing network restrictions, or data exfiltration.

One method of implementing DNS tunneling involves utilizing TXT records. In this scenario, the client issues DNS TXT record queries for domain names where the right-hand portion of the domain name (the higher-level domains) remains static, while the left-hand portion (the lowest-level subdomain) carries encoded or encrypted data sent from the client to the server. Under this structure, a sample domain name might look like ZFcABQAIBA[.]testlab[.]local, where testlab[.]local serves as the static right-hand portion and ZFcABQAIBA represents the variable left-hand string containing the data transmitted by the client.

In response to these queries, the server delivers commands or messages inside the data field of the TXT response. Because the right-hand portion of the domain name remains static, all client queries are consistently routed to the same C2 server, even if the intermediate DNS resolvers targeted by the client change.

DNS query (left) and corresponding response (right) during DNS tunneling via TXT records

It is rather challenging to identify this malicious activity within DNS traffic without generating false positives. DNS traffic is permitted across almost all corporate networks, long domain names occur routinely in both internal and external environments, and TXT records are frequently leveraged for legitimate operational purposes.

Suspicion is established through a combination of indicators: a high volume of long, seemingly random subdomains associated with a single top-level domain, high request frequency, an unusually large number of unique names, non-standard record types, and significant data transfer volumes within a single DNS session.

By analyzing DNS traffic for threat detection, we identified three primary fields of interest:

• Requested DNS name
• DNS record type
• TXT data field within the response

As shown in the image above, all of these fields are present in the DNS response. In a real-world scenario, a tunnel of this nature will transmit a volume of data that is abnormally large compared to standard DNS traffic.

Data exchange within a DNS tunnel

Thus, in the context of DNS tunneling, a network anomaly occurs when 1) a single query source host sends data embedded in the variable left-hand portion of domain names (rrname) while 2) maintaining a static right-hand portion (rrname) and 3) receives DNS server responses containing TXT records (rtype) with varying data (rdata), while 4) the total volume of data transmitted in the left-hand portion of the requested domain name together with the TXT data response (rdata + rrname) exceeds a predefined threshold.

Request and response events from DNS session attributes

When detecting DNS tunneling, the following nuances must be considered:

• A single tunnel will not be constrained to a single DNS session; data may be transmitted across multiple sessions with the DNS server, or each individual request may occur within a separate session.
• A client DNS query can contain more than one requested domain name.
• A DNS response can contain multiple TXT records, as well as a large volume of various non-TXT record types.
• Traffic between DNS servers must be excluded, as it duplicates client requests and can trigger false positives.
• Although the factors outlined above (an abnormally large or frequently changing left-hand subdomain alongside a static right-hand domain, or an unusually long string in a TXT record) serve as key indicators of DNS tunneling, they can also occur within legitimate network traffic.

These challenges create a high likelihood of false positives when detecting DNS tunneling, particularly when using IDS-based tools. Writing an accurate IDS rule for this type of activity is practically impossible. With rare exceptions, DNS tunneling tools possess static markers that can be leveraged for signature-based detection. However, in the absence of such markers, signature methods fail to deliver high detection accuracy without generating an overwhelming number of false positives. In these cases, a comprehensive approach combining multiple correlated indicators is essential to improve overall detection quality.

DNS tunneling detection logic

To add a rule for detecting this anomaly, you can use the prebuilt “DNS data tunneling via TXT records” template in the new rule creation interface. The “SQL-specific query” tab will display the list of variables used:

• user_DNS_servers: a list of internal DNS server addresses within the infrastructure, required for the rule to function correctly and minimize potential false positives
• excl_sip: IP addresses to be excluded from the scope of the rule (you can specify a single address, a subnet mask, or a list containing both addresses and subnets)
• traffic_size: the threshold value for the total volume of data (in bytes) transmitted through the tunnel

Variables used in the “DNS data tunneling via TXT records” rule

The detection logic for this network anomaly is structured as follows:

1. From network sessions using the DNS protocol within the timeframe defined by the rule’s search depth, select only those sessions containing at least one TXT response.
   Additionally:
   • The IP address that initiated the session must not be excluded in the excl_sip variable.
   • The source IP address that initiated the session must not belong to the internal DNS servers listed in the user_DNS_servers variable.
   • The DNS names requested by the client must not be excluded within the rule.
2. Split qualifying DNS sessions into individual log lines, each corresponding to an individual request or response. Retain only DNS responses containing TXT data.
3. Extract DNS names and their associated TXT data from these DNS responses. Retain only unique values.
4. Group all resulting records by the session’s source IP address, aggregating all unique DNS names and TXT data blocks.
5. Generate an alert if the combined size (in bytes) of the unique DNS names and TXT response data for a single IP address within the search depth window exceeds the specified threshold (the traffic_size parameter).
6. Within the event regeneration window, group under the initial alert all subsequent alerts associated with the same client IP address. This avoids creating duplicate event records by incrementing the aggregation counter (Total appearances).

“DNS data tunneling via TXT records” rule triggering event

The primary value of NAD technology in this scenario lies in noise reduction – by minimizing false positives – and faster investigation times. A DNS tunnel rarely presents itself as a single, blatantly malicious request. Instead, it leaves behind a behavioral footprint: repetition, length, domain structure, unusual record types, numerous subdomains branching off an unchanging root domain, and anomalous host behavior. KATA consolidates these indicators into a single alert, presenting the analyst with an actionable attack hypothesis rather than a set of fragmented DNS events.

Prebuilt rules for detecting network anomalies in KATA

KATA users should note that Network Anomaly Detection (NAD) rules are not enabled by default. Rules must be added manually using the procedure described in the preceding sections. This design ensures that analysts can fine-tune rules to fit specific network infrastructures using variables.

Analysts have three ways of creating new rules:

1. Adding a rule from a prebuilt template and adjusting custom variables. In this case, the rule is classified as a system rule.
2. Adding a rule from a prebuilt template and modifying its underlying SQL query (which requires enabling the “Unlock all template values” option) to create a custom rule based on the template. When modified this way, the rule transitions from a system rule to a custom rule.
3. Authoring a custom rule from scratch, which requires a basic understanding of ClickHouse SQL queries and familiarity with the product documentation.

As of this publication, the product ships with 59 prebuilt NAD rule templates (with additional templates delivered via product updates). KATA supports running up to 200 active rules simultaneously.

Prebuilt rules are divided into six categories:

• Large Data Transfers: tracking abnormally large network sessions across various protocols during regular hours, at night, or over weekends.
• Suspicious Connections: detecting suspicious connections that may indicate hazardous activity, shadow IT, evasion of attack detection mechanisms, and other threats.
• Domain Attacks: detecting classic attacks targeting domain network infrastructures using offensive tooling.
• Reconnaissance Activity: identifying suspicious activity within domain protocol sessions (Kerberos, DCE/RPC, LDAP, DNS) resembling domain reconnaissance.
• Connections to Suspicious Resources: detects actions that violate security policies, potential data exfiltration beyond the perimeter, and unauthorized internet access originating from secured network segments.
• C2 Communication: identifies network sessions characteristic of a potential C2 communication channel or tunnel.

The table below lists the rule templates for detecting network anomalies in KATA:

Conclusion

The examples of Kerberoasting and DNS tunneling clearly demonstrate why modern security defenses cannot rely solely on looking for known signatures and indicators of compromise. Both attack techniques abuse protocols that operate inside corporate networks every day. At the individual event level, they may look like legitimate activity, yet in behavioral context, they stand out as clear indicators of compromise.

NAD directly addresses this gap. Instead of relying purely on signature matches across Kerberos or DNS traffic, it highlights deviations from established baselines: who initiated the activity, how frequently it recurred, which services or domains were targeted, and why that matters for a specific infrastructure.

As a result, analysts gain a clear, actionable starting point for investigation. This capability is especially valuable for spotting the signs of APT group activity, which runs stealthily and is designed to blend in with legitimate operations. The importance of this capability will only grow: as attack techniques evolve, detecting suspicious activity at its earliest stages – before it escalates into critical service compromise or a data breach – becomes increasingly vital.

What helps some professionals stand out when organisations are constantly searching for people who can improve performance? The answer often lies in having the right skills and recognised credentials. Six Sigma Green Belt Training is widely valued because it teaches professionals how to identify problems and support business success. Employers across many industries look for individuals who can contribute to measurable improvements. Earning a Six Sigma Green Belt Certification can strengthen both technical and professional capabilities.

In this blog, we will explore the key benefits of gaining this respected certification.

Table of Content

Enhanced Career Growth Opportunities

Stronger Problem Solving Skills

Cross Industry Versatility

Higher Earning Potential 

Greater Confidence in the Workplace

Opportunities Across Multiple Industries

Supports a Culture of Continuous Improvement 

Welcome back, aspiring cyberwarriors!

Part of our work involves supporting red team engagements. We review completed tests, size up the risk tied to each vulnerability and build out recommendations for shoring up the infrastructure. This time around, we wanted to pull back the curtain on something special. It’s ATM security. 

This article is written to help with security assessments on ATMs, showing possible vulnerabilities you may find. It covers many things, from running malware bought off a forum, to an insider on the bank’s payroll, to a service technician who understands the machine’s internals and has been handed broad access to the equipment. We also look at whether a hacker could get into the bank’s broader network simply because the perimeter wasn’t locked down well enough.

Nothing here is meant as a tutorial. We’re documenting weaknesses hackers could exploit so that defenders know what to fix, not handing anyone a blueprint. We take no responsibility for how this information is used.

With that out of the way, let’s start with where ATMs came from.

The History of ATMs

London got the world’s first working ATM on June 27, 1967. It was primitive by today’s standards, incapable of checking a balance, which is exactly why withdrawals topped out at 10 pounds, and it dispensed cash only against special vouchers rather than reading a card. 

Nearly six decades later, ATMs look nothing like those early cash dispensers. Now they are multifunctional devices, but the hackers never stopped circling. Part of the appeal is obvious. An ATM sits on a pile of cash and offers quick access to it, and there are simply too many machines scattered across too many places to guard them all closely. A lot of them sit in isolated, low traffic spots that run unattended around the clock, think gas stations. That has shaped decades of security investment, most of it aimed at physical hardening. Today’s units can weigh over half a ton and come loaded with sensors tracking position, internal temperature, and whether a compartment has been pried open.

Here’s the catch, though. The safe holding the cash is genuinely hard to crack, but the compartment housing the control electronics is a different story, and in our assessment, it remains poorly defended. That gap opens the door to logical attacks, ones that skip the crowbar entirely and go after the software instead, and that category has been gaining ground fast.

Cisco Talos has tracked a steady climb in new ATM malware variants since 2009. The raw sample count still looks small next to other malware families, but don’t let that fool you. Europe alone saw logical attacks on ATMs jump 269% in 2020 versus the year prior, and the average payout per incident ballooned nearly a thousandfold across that same window, climbing from roughly a thousand euros to well over a million.

What changed the game was availability. ATM malware used to be a rare, closely guarded tool. Once it started circulating more freely on underground markets, prices fell and so did the skill required to use it. Cutlet Maker, which surfaced in 2017, is a good illustration. It came bundled with a Russian language manual complete with troubleshooting notes for running it against different ATM models.

Fast forward to 2024, and vendors on those same markets were offering ATM malware through subscription pricing, monthly plans included.

Logical attacks have always had one real weakness. They take skill and patience to pull off. That’s why cheap, well documented malware kits have had such an outsized impact on the trend. Their upside for hackers is just as real. They’re far quieter than smashing a machine open, and they often let the same person come back to a compromised ATM again and again. Manufacturers have started fighting back on the hardware side too, with tamper protected cassettes that flood the cash inside with indelible ink the moment someone tries to force them open, ruining the bills instantly.

Brief Attack Statistics

The numbers tell their own story. ATM related crime climbed 600% between 2019 and 2022, with 165% of that increase packed into 2021 and 2022 alone. Physical break ins, which have always driven the bulk of ATM crime, contributed alongside the rise in logical attacks. Germany had 496 ATM explosions recorded in 2022, a record for the country. Zoom out globally, and incidents of that kind blew past 18,000 in 2023.

Losses have kept pace. Banks worldwide absorbed $2.4 billion in direct losses from ATM fraud by the close of 2023. Europe’s share came to 173 million euros, with 67 million of that tied specifically to skimming. The United States handles just 25.29% of global transaction volume yet accounts for 42.32% of global losses. Skimming remains a big part of why, showing up in 45% of all ATM fraud cases in 2023 and costing North America over $900 million, with more than 315,000 cards compromised across at least 3,000 financial institutions.

None of this is happening in a vacuum. The market for ATM protection has grown right alongside the threat. Still, priorities inside most banks remain lopsided. Physical security tends to get the lion’s share of attention, while the operating system, drivers, and control software logic running underneath often get treated as an afterthought. That imbalance carries real consequences. A 2022 RTM Group study found that hackers could breach an ATM’s housing without setting off an alarm in one out of every two attempts, giving them free rein to tamper with the equipment inside.

How an ATM Is Built

Making sense of how these attacks work starts with understanding what happens inside the machine during an ordinary transaction. We’ll walk through that process using one representative configuration, illustrated in the diagram below.

The diagram reflects one specific setup we’re using for illustration, not a universal default, since real world configurations vary by device.

1. User Layer

From where the customer stands, using an ATM is simple. They need to present a card and pick a transaction. That wasn’t always the whole story. Inserting a physical card into a reader used to be the only entry point, and that reliance on the magnetic stripe made skimming and shimming, techniques aimed at stealing card data to produce counterfeit copies, a persistent problem for years.

Contactless cards changed the entry point itself. NFC readers now sit alongside traditional card slots on most machines. 

A PIN code layers on additional protection against someone using a stolen card. Entry happens through an encrypting PIN pad, a combination of physical keypad and cryptographic module that ensures the PIN never travels or gets stored anywhere in plain text. Verification of the resulting encrypted PIN block happens back at the processing center. 

Once identity checks clear, you can withdraw cash, check your balance, transfer funds, and so forth. There’s a full computer running inside the housing, but customers never get anywhere near it directly. Every interaction they have flows through a single banking application running in kiosk mode, locked to full screen.

2. OS Layer

That computer we just mentioned lives inside what’s called the service zone, and this section covers what happens there, setting the cash handling hardware aside for the moment. Physically, the service zone is protected by a thin door and a basic lock. Machines from the same product line frequently share an identical key too, one that’s often available for purchase online with minimal effort.

Beyond the system unit itself, the service zone also houses the ATM’s networking equipment and its wired connections to the card reader, contactless reader, PIN pad, and dispenser, typically running over USB, Ethernet, PCI, or COM interfaces depending on the device.

Windows powers most of these systems, historically through Windows Embedded and increasingly through Windows IoT, a Windows 10 variant built for embedded use.

The kiosk application isn’t the only thing running on that OS. Alongside it sits the ATM’s control software plus a handful of security tools. That can be antivirus protection, Windows AppLocker that keeps unauthorized programs from executing, and a VPN client that maintains a secure tunnel back to the bank’s internal network.

Control software is arguably the most important piece at this layer. Core responsibilities for the control software boil down to managing peripherals and communicating with the processing center, though specific implementations often add more on top of that. Some bundle in software for a monitoring server, letting technicians manage an entire network of self service machines remotely. Others are built in a supervisor mode meant purely for technical staff, offering quick access to diagnostic tools through a hidden menu to simplify physical maintenance visits.

3. Network Layer

Selecting a transaction sets off a verification process handled entirely by the processing center, a server living on the bank’s internal network. That server confirms the card data is legitimate, checks the PIN again before letting the transaction through, rules out any restrictions on the account, and verifies there’s enough balance to cover the request.

Everything exchanged between the ATM and the processing center travels encrypted, usually through a VPN tunnel, protecting against interception or tampering along the way. NDC and DDC are the most common messaging protocols in this exchange, functioning as something of an informal industry standard even before multi-vendor control software became widespread. ISO 8583 and its various offshoots see heavy use as well. 

The processing center isn’t the only thing an ATM talks to. Many machines also maintain a connection to a monitoring server used for remote management, health checks, and pushing updates, and unlike the processing center link, this channel frequently runs without any encryption at all.

4. Firmware Layer

Once the processing center signs off, the control software hands things over to the dispenser for a withdrawal, or the deposit module if cash is going in. These components typically sit inside the most fortified section of the ATM, the safe zone, built from tougher materials and secured with its own dedicated key separate from the service zone. 

The dispenser counts out the required banknotes from the ATM’s cassettes, moves them into position at the dispensing tray, then opens the shutter, the physical flap that blocks access to the cash until it’s ready. Data moving between the control software and the dispenser can be encrypted, and both sides authenticate one another before any exchange begins, a safeguard against device spoofing. All of that encryption and authentication logic lives directly in the dispenser’s own firmware. 

Deposits work differently. Incoming banknotes pass through a validator that checks their authenticity.

ATM Attacks

With the mechanics of an ATM covered, we can turn to the threats themselves. Every attack against these machines falls into one of two broad camps, physical or logical, depending on what the hacker is going after and how they approach it.

Physical attacks go straight after the machine or its components, aiming to extract cash or knock the device out of normal operation without touching a line of code. These predate targeted malware by decades and don’t require much specialized skill. Some don’t even target the machine itself, focusing instead on the people standing in front of it.

Logical attacks operate on a different level entirely. They demand genuine technical skill and preparation, built around exploiting weaknesses in the ATM’s software and network layers. They draw less public attention than physical attacks despite posing a bigger threat to banks, largely because they’re quieter and let a hacker return to the same compromised machine to cash in more than once.

System attacks go after functionality or logic running at the ATM’s OS layer, typically aiming to extract cash or sidestep security controls outright. Black box attacks deserve special attention, where a hacker skips gaining OS access altogether and instead wires their own device directly into the dispenser to control it externally. The same technique can target other peripherals, like the banknote validator.

Network attacks aim at the ATM’s networking components instead, with hackers looking to intercept, forge, or otherwise abuse data in transit, or to seize remote control of the machine. With weak enough safeguards in place, a hacker can forge the responses coming back to the ATM and push through a cash withdrawal even after the processing center rejected it.

Not every attack in this framework ends with cash in hand. A hacker might, say, work to gain remote network access first, then pivot into an OS layer attack from there. 

We have seen cases where compromising a single ATM meant compromising the entire bank because there was no network segmentation in place. Conversely, gaining access to the bank’s internal network could provide a path to ATMs and other critical systems connected to it. Credential reuse and a lack of understanding of Active Directory security can lead to devastating consequences in environments like these.

Summary

ATMs have evolved from simple cash dispensers into complex and networked systems. Their security has evolved unevenly alongside them. Physical hardening has made the cash safe itself genuinely difficult to crack, but the service zone housing the control electronics remains comparatively exposed, and that gap has fueled a steady rise in logical attacks. These attacks demand more skill than a physical break-in, but they’re increasingly accessible because of well-documented malware kits.

Cybersecurity is a vast field, and we offer courses covering a wide range of topics, including Active Directory Hacking, Wi-Fi Hacking, Web Application Hacking, SCADA Security, and much more. Our course library is constantly growing as we continue to add new training, all of which is available through our Member Gold plan. If you want unlimited access to our entire training library, including our most advanced courses, consider upgrading to Subscriber Pro.

The post Pentesting: A Look at ATM Security first appeared on Hackers Arise.

What helps some professionals stand out when organisations are constantly searching for people who can improve performance? The answer often lies in having the right skills and recognised credentials. Six Sigma Green Belt Training is widely valued because it teaches professionals how to identify problems and support business success. Employers across many industries look for individuals who can contribute to measurable improvements. Earning a Six Sigma Green Belt Certification can strengthen both technical and professional capabilities.

In this blog, we will explore the key benefits of gaining this respected certification.

Table of Content

Enhanced Career Growth Opportunities

Stronger Problem Solving Skills

Cross Industry Versatility

Higher Earning Potential 

Greater Confidence in the Workplace

Opportunities Across Multiple Industries

Supports a Culture of Continuous Improvement