Preventing Advanced Persistent Threats
Cybersecurity is responsible for securing information through the implementation of particular policies to carry out technological infrastructure in both government and private organizations. However, security drawbacks and vulnerabilities can arise when the software is pirated or not updated and applications are used, which provides opportunities for an intruder. Advanced Persistent Threats (APTs) is a type of attack conducted by skilled attackers who have various tools, such as zero-day vulnerabilities and denial-of-service (DoS) attacks, which traditional solutions struggle to handle due to the complexity of modern threats.
APTs primarily target large organizations and government sectors, selectively attacking less critical areas. They infiltrate high-profile systems, establish outgoing connections, and insert malware to access and extract as much data as possible. By obtaining unauthorized access, they filter confidential information and launch malicious attacks by exploiting privileged access. Attackers also exploit users' web browsing details, security breaches, browser vulnerabilities, and various elements such as cookies, plugins, and JavaScript, allowing them to steal valuable data.
To achieve their goals, APTs follow multiple stages, including network footprinting, scanning, and lateral movement within the network to obtain target system credentials and identify other vulnerable systems. Attackers conduct in-depth research on the target organization, utilizing social engineering techniques and collecting user credentials based on personal interests. Notable APT attacks in the past include Titan Rain, Hydraq, Stuxnet, and Carbanak.
Defending against APT attacks requires monitoring CPU usage, disk activity, memory utilization, and employing protocols for packet monitoring and log analysis. However, there are several challenges in combating APTs, including identifying sophisticated attackers, dealing with prolonged attacks, addressing infrastructure-based vulnerabilities, and mitigating risks posed by internal employees.
Machine Learning (ML), a subfield of Artificial Intelligence (AI), offers automated computational processes and generalization of sample data to tackle complex problems that are difficult to solve using traditional programming methods. ML utilizes mathematical and statistical functions to describe data dependencies and analyze incoming and outgoing data. It has multiple applications that assist decision-makers in addressing daily challenges.
By applying ML models such as Support Vector Machines (SVM) and K-Nearest Neighbors (K-NN), the stages of an APT attack and its lifecycle can be categorized, enabling the defense against APT techniques. ML models simplify the identification of similarities between APT attacks, differentiating between safe and malicious activities.
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Written by NIELIT KOHIMA