Tshedimoso Makhene
Cyberattacks often exhibit recognizable patterns that can help in early detection and prevention. Recognizing these indicators early can significantly mitigate potential damages.
Unusual network activity
Traffic spikes
Sudden, unexplained increases in network traffic can be a red flag. Such spikes might indicate data exfiltration or a Distributed Denial-of-Service (DDoS) attack, where overwhelming traffic is directed at a system to disrupt its services.
Unauthorized access attempts
Repeated failed login attempts, especially from unfamiliar IP addresses, can signal brute-force attacks aiming to crack passwords. Monitoring tools can help detect and block these malicious efforts.
See also: Common password attacks and how to avoid them
Example
An example of unusual network activity could be a Distributed Denial of Service (DDoS) attack. In this scenario, an organization might experience a sudden and massive spike in traffic directed at their website or online service. This traffic comes from numerous compromised devices worldwide, all coordinated by cybercriminals.
The goal of a DDoS attack is to overwhelm the server or network, making it unable to handle legitimate traffic, leading to service disruption or downtime.
Recently, a botnet, Eleven11bot, was discovered by Nokia researchers. This bot launches DDoS attacks that target telecommunications service providers and online gaming servers. Eleven11bot has infected 86,400 Internet of Things (IoT) devices worldwide, with infections heavily concentrated in the United States, the United Kingdom, Mexico, Canada, and Australia.
Anomalous user behavior
Irregular login patterns
Logins occurring at odd hours or from unexpected locations may suggest compromised credentials. Implementing multi-factor authentication (MFA) can add an extra layer of security.
Sudden privilege escalation
If a user account unexpectedly gains elevated permissions, it could indicate malicious activity. Regular audits of user privileges are essential to maintain security.
Go deeper: What are privilege escalation attacks?
Example
Credential stuffing is a cyberattack where attackers use automated systems to attempt large-scale logins with stolen username and password pairs. These attacks exploit users' tendency to reuse passwords across multiple sites. Such unauthorized attempts often result in irregular login patterns, including access from unfamiliar locations and at odd times. Implementing MFA can significantly reduce the success rate of these attacks by requiring additional verification steps beyond just the password.
A recent example of a credential stuffing attack is the Snowflake data breach. The attackers created a tool called "rapeflake" to automate this process which resulted in approximately 165 organizations being affected. Snowflake's CISO, Brad Jones, believes the breach is the result of ongoing identity-based attacks with the intent to obtain customer data. The company has not identified evidence suggesting a vulnerability, misconfiguration, or breach of Snowflake's platform.
Go deeper: Snowflake faces massive data breach impacting 200 companies
Suspicious file and system changes
Unauthorized modifications
Unplanned changes to system files or configurations can be a sign of malware attempting to alter system behavior. File integrity monitoring tools can alert administrators to such changes.
Presence of unknown programs
The appearance of unfamiliar applications or processes may indicate malware installation. Regular system scans can help identify and remove these threats.
Email and phishing indicators
Unsolicited attachments or links
Unexpected emails containing attachments or links can be phishing attempts designed to deliver malware or steal credentials. Educating users about phishing tactics is vital for prevention.
See also: HIPAA Compliant Email: The Definitive Guide
Spoofed sender addresses
Emails that appear to come from trusted sources but have slight variations in the sender's address can deceive recipients into divulging sensitive information. Vigilance and verification are key defenses.
Read also:
Example
A recent example involves an AI-generated video of YouTube's CEO, Neal Mohan, used to trick content creators. Scammers created a realistic video falsely announcing changes to YouTube's monetization policies, aiming to steal credentials. YouTube has warned users about this tactic, emphasizing that official communications would not be shared through private videos.
Denial-of-service (DoS) attack patterns
Service disruptions
Experiencing frequent service outages or slowdowns can result from DoS attacks, where attackers overwhelm a system with excessive requests. Implementing rate limiting and robust firewall rules can help mitigate these attacks.
For instance, in March 2025, the social media platform X (formerly Twitter) experienced significant outages attributed to a massive cyberattack. Elon Musk, the platform's owner, claimed that the attack involved substantial resources, potentially implicating a large coordinated group or a nation. Cybersecurity experts noted that the attack likely involved a Mirai variant botnet using compromised devices worldwide.
Related: What is the difference between a DoS or a DDoS attack?
Abnormal data access patterns
Mass data access or transfer
Unusually large data transfers, especially by users who don't typically handle such volumes, can indicate data exfiltration attempts. Monitoring and setting thresholds can help detect these anomalies.
Access to sensitive information
Users accessing data beyond their typical scope of work may suggest compromised accounts or insider threats. Regular reviews of access logs are essential.
Indicators of data exfiltration
Uncommon data destinations
Data being sent to unfamiliar external IP addresses or countries without business relations can signal exfiltration. Implementing data loss prevention (DLP) tools can monitor and block unauthorized transfers.
Encrypted traffic anomalies
While encryption is standard for security, unexpected encrypted traffic, especially to unknown destinations, can be suspicious. Analyzing traffic patterns helps in identifying potential threats.
Example
In August 2023, Tesla faced a major data exfiltration incident when two former employees illegally leaked 75,000 employee records to the German newspaper Handelsblatt. The breach included personal employee details, customer complaints, and internal reports. Tesla detected the abnormal data transfer after noticing unusual access patterns from the employees' accounts. The company revoked access, pursued legal action, and implemented stricter monitoring of data transfers to prevent similar incidents in the future.
Endpoint compromise and malware indicators
Disabled security features
If antivirus or firewall settings are altered or disabled without authorization, it may indicate malware attempting to evade detection. Regularly verifying the status of security tools ensures they function correctly.
Unexpected system behavior
Systems acting erratically, such as frequent crashes or unauthorized software installations, can be signs of compromise. Prompt investigation is necessary to address potential threats.
Insider threat indicators
Unusual privileged activities
Privileged users performing actions outside their typical responsibilities, like accessing sensitive data unrelated to their role, can indicate malicious intent. Continuous monitoring of privileged accounts is crucial.
Data hoarding
Employees downloading or collecting large amounts of data without a clear business need may be preparing for data theft. Implementing strict data access policies can prevent such activities.
External threat intelligence
Threat feeds and alerts
Subscribing to threat intelligence feeds provides information on emerging threats and vulnerabilities, allowing proactive defense measures. Integrating these feeds into security systems enhances situational awareness.
Dark web monitoring
Monitoring dark web forums and marketplaces can reveal if organizational data or credentials are being traded, indicating a potential breach. Specialized services can assist in this surveillance.
Advanced persistent threats (APTs)
Multi-stage attacks
Advanced persistent threats (APTs) involve sophisticated, prolonged attacks where adversaries gain network access and remain undetected to steal data over time. Recognizing patterns such as consistent low-level intrusions can help identify APTs.
For example, the cyber kill chain model outlines the stages of such attacks, from reconnaissance to data exfiltration. Understanding this model aids in identifying and disrupting these threats.
Learn more: The 3 stages of an APT attack
Supply chain attacks
Third-party compromises
Attackers may infiltrate less-secure networks of third-party vendors to access the primary target.
A notable example of a supply chain attack is the 2020 SolarWinds cyberattack. In this incident, attackers compromised SolarWinds' Orion software, a widely used network management tool, by injecting malicious code into its updates. The breach affected numerous organizations, including U.S. government agencies and private companies.
Watch: Aaron Collins: Solar Winds and Microsoft Exchange Server Attacks
FAQS
How can organizations defend against cyberattacks?
- Implement multi-factor authentication (MFA).
- Regularly monitor system logs for anomalies.
- Use endpoint detection and response (EDR) solutions.
- Conduct frequent cybersecurity awareness training for employees.
- Maintain offline backups to prevent data loss from ransomware.
What should I do if I suspect a cyberattack?
- Disconnect affected systems from the network immediately.
- Alert the IT/security team and follow the incident response plan.
- Check logs for suspicious activity.
- Notify stakeholders and, if necessary, law enforcement.
How does HIPAA help protect patient data?
The Health Insurance Portability and Accountability Act (HIPAA) sets guidelines for handling protected health information (PHI). It requires:
- Data encryption for stored and transmitted health records.
- Strict access controls to prevent unauthorized access.
Breach notification rules to inform patients of compromised data.
