Defending Against Cyber Threats: Leveraging Endpoint Detection and Response

Cyber Threats

Cyberattacks are on the rise as technology is used more and more in daily life. These attacks can steal data, extort money, disrupt critical infrastructure or spread malware. The best defense against these threats is a continuous monitoring system that detects and eradicates threats once they enter the perimeter of your environment. It is where EDR comes in.


Malware is a broad category of malicious applications that damage or impede the normal use of endpoint devices. Examples include viruses, which can alter a device’s operation or accept commands from the developer; exploits, which target vulnerabilities; and worms, which spread from computer to computer. Cybercriminals distribute malware to gain unauthorized access to a system or data, sell victims’ personal information, extort payments or other assets, or cause damage and disruption. To effectively defend against malware, organizations need a comprehensive security solution that can detect threats in the wild. Endpoint threat detection and response systems use modern analytics and machine learning methods to spot emerging trends that point to recognized threats and suspicious activity. After a threat has penetrated the perimeter defenses, it must be detected to be contained and eliminated. It requires a solution that uses sandboxing to confine a malicious file in a safe, secluded environment where it can be closely observed and studied without the risk of triggering an alarm or interfering with other systems.

Man-in-the-Middle (MitM) Attacks

Cybercriminals can use man-in-the-middle attacks to intercept data and steal sensitive information. These attacks typically take advantage of vulnerabilities in web browsers, email, IP addresses, SSL certificates, DNS servers and WiFi networks to spoof the source and destination of an online service or website. Once the attacker has a user’s login credentials, they can hijack the session between the client and server and access their data. This attack can lead to financial loss, personal information theft and reputational damage.

Businesses should use encryption technologies to encrypt all network connections to prevent man-in-the-middle intrusions. Additionally, they should use strong authentication protocols such as multi-factor authentication, which requires two forms of identification for users to log in. It can help prevent attackers from hijacking accounts or stealing valuable data.

Businesses should routinely check network traffic for anomalies to spot indicators of a man-in-the-middle attack. Organizations must identify suspicious activity and put an incident response plan in place to lessen the effect of these threats. Investing in zero-trust technology can also mitigate the risk of man-in-the-middle and other cyberattacks. Zero trust security models require that organizations verify anything trying to connect to their systems, ensuring only trusted and secure connections are granted.

Distributed Denial of Service (DDoS) Attacks

Attackers use DDoS to draw a victim’s focus away from other vulnerabilities while they execute their primary attacks. They may also use them as a feint to keep defenders busy while they attempt to steal data or breach the network. The primary goal of DDoS attacks is to make a target website unusable. It can be accomplished through multiple methods, including sending huge traffic volumes or exploiting network device weaknesses. Attackers can leverage DDoS attack tools or create botnets, a collection of hijacked connected devices like PCs, cellphones, or unsecured IoT devices (like the infamous Mirai botnet) to launch cyberattacks against large targets.

Attackers are also increasingly using DDoS to extort money. They will threaten to launch a DDoS attack against your company and then ask for payment in exchange for not carrying out the threat. This type of extortion is a growing problem for many organizations and is likely to become more common as attackers grow more sophisticated. They may even move beyond simple DDoS attacks and seek to gain access to your system, thereby increasing the duration and severity of an attack.

Advanced Persistent Threats (APTs)

If there’s one thing that keeps cybersecurity professionals awake at night, it’s the prospect of sophisticated attacks designed to penetrate a network and steal data over an extended period. This attack type is called an advanced persistent threat (APT). APTs are usually leveled against high-profile organizations and governments. They use a range of tactics, including custom malware and zero-day exploits, to gain access. Nation-states or well-funded criminal organizations usually back these attacks and aim to evade detection for long periods, collecting data or compromising critical infrastructure. APT actors often organize into groups to share resources, making them more difficult to track. For example, the Russian group Silence APT was linked to a series of attacks against banks in 2015. Defending against APTs requires a prevention-focused approach, leveraging security solutions that can spot unusual data files and excessive outgoing traffic. Keeping software, apps and devices updated regularly also helps to minimize the chances of an APT attack.

Supply Chain Attacks

Supply chain attacks are among the most dangerous cyber threats because hackers can target multiple enterprises by compromising one vendor. They can snoop on the vendor’s customer data, tamper with hardware, or alter software products to push malware into customers’ environments. Attackers increasingly target third-party vendors because they have more networks and data access. This attack piggybacks on the trust relationships between businesses and their partners, bypassing robust enterprise defenses. It can be carried out via malware injected into a trusted application or patch and stolen certificates used to vouch for software product legitimacy. Defending against these attacks requires solutions providing continuous monitoring and behavior-based attack detection. For example, restricting access to privileged accounts can help limit an attacker’s ability to pivot across the network. Using machine learning to detect anomalous behavior can also help to protect against these attacks.