Keyloggers and spyware are the most commonly occurring attacks in companies that suffer major data breaches, according to a report published today by Verizon Business.
The new report, "2009 Supplemental Data Breach Investigations Report: An Anatomy of a Data Breach," offers a look at the 15 most common security attacks and how they typically unfold. The data is extracted from Verizon Business' April 2009 study of its computer forensics service customers, all of whom have experienced a major data breach.
The report taps Verizon Business' detailed investigative records to identify, rank, and profile the most common attacks. For each type of attack, the report provides real-world scenarios, the warning signs, how the attack is orchestrated, how attackers got in, what information they took, what assets the attackers targeted, what industries are commonly affected, and what countermeasures are effective. In total, the report details nearly 150 ways to detect and combat security threats. This latest installment in Verizon's data breach study series is based on the "2009 Verizon Business Data Breach Investigations Report," issued in April. That landmark study analyzed more than 90 forensic investigations involving 285 million compromised records.
"We developed this report to answer a lot of the questions we've received about the report we issued in April," says Wade Baker, research and intelligence analyst at Verizon Business and one of the authors of the report. "We plot out the most common attacks and some of the indicators that can be used to spot them."
The report identifies and ranks by frequency the following top 15 types of attacks:
1. Keylogging and spyware: Malware specifically designed to covertly collect, monitor, and log the actions of a system user.
2. Backdoor or command/control: Tools that provide remote access to or control of infected systems, or both, and are designed to run covertly.
3. SQL injection: An attack technique used to exploit how Web pages communicate with back-end databases.
4. Abuse of system access/privileges: Deliberate and malicious abuse of resources, access, or privileges granted to an individual by an organization.
5. Unauthorized access via default credentials: Instances in which an attacker gains access to a system or device protected by standard preset (widely known) usernames and passwords.
6. Violation of acceptable use and other policies: Accidental or purposeful disregard of acceptable use policies.
7. Unauthorized access via weak or misconfigured access control lists (ACLs): When ACLs are weak or misconfigured, attackers can access resources and perform actions not intended by the victim.
8. Packet sniffer: Monitors and captures data traversing a network.
9. Unauthorized access via stolen credentials: Instances in which an attacker gains access to a protected system or device using valid but stolen credentials.
10. Pretexting or social engineering: A social engineering technique in which the attacker invents a scenario to persuade, manipulate, or trick the target into performing an action or divulging information. 11. Authentication bypass: Circumvention of normal authentication mechanisms to gain unauthorized access to a system.
12. Physical theft of asset: Physically stealing an asset.
13. Brute-force attack: An automated process of iterating through possible username/password combinations until one is successful.
14. RAM scraper: A fairly new form of malware designed to capture data from volatile memory (RAM) within a system.
15. Phishing (and endless "ishing" variations): A social engineering technique in which an attacker uses fraudulent electronic communications (usually email) to lure the recipient into divulging information.
In addition to the extensive threat catalog, the supplemental report includes an appendix that compares Verizon's caseload with DataLossDB, a public database of reported incidents worldwide.
"We developed the appendix to address some questions and concerns that people had expressed about the April report, which showed that internal threats weren't that prevalent," Baker says. "But when we went through the DataLossDB, we found that our numbers and theirs weren't really all that far apart."
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