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Network security is a critical aspect of any organization's overall security posture. As technology advances, so do the methods used by hackers and other malicious actors to carry out an attack. We will take a closer look at some specific network security notifications that can help organizations deal with some of the most common threats inside networks:
Indicators of Compromise (IoC) are a valuable tool for hackers seeking to identify potential targets and exploit vulnerabilities in their systems. These tell-tale signs of a compromised system can include anything from a suspicious file name to an IP address or domain name associated with a known malware or attack vector.
The use of IoCs provides hackers with a range of opportunities to facilitate their attacks. For example, attackers can use an IoC to identify vulnerable systems that are running outdated software or have not been patched against known vulnerabilities. Alternatively, IoCs can be used to identify specific users or organizations that are likely to have valuable data or other resources worth targeting.
Once hackers have identified potential targets using IoCs, they can then employ a variety of tactics to exploit vulnerabilities and gain access to the target's system or network. This can include sending phishing emails with malicious links or attachments, using social engineering tactics to trick users into revealing sensitive information, or exploiting vulnerabilities in software or hardware.
The third notification category is Domain Name Servers (DNS). DNS servers are critical infrastructure components of the internet that translate human-readable domain names into IP addresses that computers can use to connect with each other. While DNS servers are essential for the functioning of the internet, they can also be used for hacking in a variety of ways.
One way that DNS servers can be used for hacking is by conducting DNS cache poisoning attacks. In a cache poisoning attack, a hacker exploits a vulnerability in a DNS server's software to inject fake DNS records into the server's cache. When a user attempts to visit a website that is associated with the fake DNS record, they are redirected to a malicious site that may be used to steal their information or infect their device with malware.
Another way that DNS servers can be misused for a cyberattack is by conducting DNS tunneling. In a DNS tunneling attack, a hacker uses a DNS server to transmit data between a compromised device and a remote command-and-control server. The hacker can use the DNS server to bypass firewalls and other network security measures, making it difficult for cybersecurity staff to detect and block the attack.
An Onion Router, also known as “Tor”, is a privacy-focused tool that is designed to provide anonymous and secure communication over the internet. While the primary purpose of Tor is to protect users' privacy and security, it can also be used by hackers to conduct illegal activities, such as cyberattacks and data theft.
One way that an Onion Router can be used for hacking is by concealing the identity and location of the attacker. By routing their traffic through multiple layers of encryption and relays, hackers can effectively hide their IP address and make it difficult for law enforcement agencies to trace their activities.
Another way that an Onion Router can be used for hacking is by accessing hidden services on the Tor network. The Tor network includes a range of websites and services that are not accessible through the regular internet, which can be used by hackers to host and distribute malicious content or to communicate with other hackers.
It can also be used as the perfect ground to exploit vulnerabilities in the network itself. While the Tor network is designed to be secure, it is not immune to attacks, and hackers can exploit vulnerabilities in the Tor software to gain access to sensitive information or to compromise the security of the network.
Simple Network Management Protocol (SNMP) is a powerful tool used by network administrators to manage and monitor network devices like routers, switches, and servers. However, if not properly secured, it can also be used by hackers as a means of gaining unauthorized access to a network.
One way that SNMP can be exploited by hackers is by taking advantage of vulnerabilities in the protocol itself. By using specialized tools, hackers can identify SNMP-enabled devices on a network and exploit known weaknesses in the SNMP protocol to gain unauthorized access.
Another tactic used by hackers is to leverage SNMP traps, which are messages sent by SNMP-enabled devices to a central server when certain events occur, such as a device going offline or experiencing high CPU usage. By monitoring these traps, hackers can identify potential targets and launch attacks against vulnerable devices on the network.
Hackers are also able to use the information collected through SNMP monitoring to launch further attacks. For instance, they may use SNMP to extract sensitive information like passwords or configuration files, which can be used to gain unauthorized access to other systems on the network.
Known threat detection is an essential part of Raw Data Processor (RDP) module. It analyzes raw data to generate connection-oriented protocol analysis metadata and looks for known threats in the traffic. Since the module has access to both raw data and the metadata generated by itself, known threats can be detected by correlating data at packet, connection, file, and application levels, across the entire network.
A frequent pattern in the detection of known threats is to count one or more events for a specific host and define a threshold over which a notification should be generated. For example, ransomware will often keep track of which files it has encrypted by appending a ransomware-specific extension to the file name. The counting approach can be used to generate two useful metrics: Looking at how many files each host renames across SMB shares, and how many times does each host append a ransomware-related suffix to a file across SMB shares.
The first metric allows detection of previously unseen malware but may potentially cause false positives if there is a legitimate reason to rename many files at once; the second only allows detection of known ransomware but has a smaller chance of false positives since this would require the extensions of legitimate software to collide with a ransomware-related extension. This trade-off calls for two different thresholds; a higher one for the generic rename count, while the extension-specific counter can be equipped with a lower threshold.
Counting incidents of known threats over a certain period, known as a "time frame," can now be fine-tuned to an impressive degree using just two basic parameters: "threshold" and "time frame." With the addition of a new feature, users can customize these parameters for each notification type directly in the front-end. This simple yet powerful approach to threat detection yields impressive results, making it easier than ever to stay ahead of potential security breaches.
While the above notifications do not offer the same level of sophisticated network-specific analysis that machine learning (ML) does, they complement each other well and work well in practice. They can also help by giving context to threats detected by ML.
Artificial Intelligence and ML can be a valuable tool for analyzing and detecting potential security threats that are indicated by IoC or SNMP notifications. By training machine learning algorithms on vast amounts of historical data, algorithms can identify patterns and anomalies in network behavior that may indicate a security breach or potential threat.
For example, machine learning algorithms can be used to analyze network traffic for unusual spikes in activity or abnormal patterns of behavior, such as unusual login attempts or unusual data transfer activity. If a pattern or behavior is identified as potentially malicious, the machine learning algorithm can then trigger an alert or notification to the appropriate security team or administrator.
In the case of SNMP notifications, machine learning can also be used to analyze the content of the messages and identify patterns or anomalies that may indicate a security threat. By training machine learning algorithms on historical SNMP trap data, these algorithms can learn to identify patterns in the content of the traps that are associated with specific types of security threats, such as malware infections.
Overall, machine learning can provide a powerful tool for analyzing and identifying potential security threats based, allowing organizations to respond quickly and effectively to potential security breaches and minimize the damage caused by malicious actors.
