Abnormal Behavior Detection to Identify Infected Systems Using the APChain Algorithm and Behavioral Profiling

Abstract

Recent cyber-attacks have used unknown malicious code or advanced attack techniques, such as zero-day attacks, making them extremely difficult to detect using traditional intrusion detection systems. Botnet attacks, for example, are a very sophisticated type of cyber-security threat. Malicious code or vulnerabilities are used to infect endpoints. Systems infected with this malicious code connect a communications channel to a command and control (C&C) server and receive commands to perform attacks on target servers. To effectively protect a corporate network’s resources against such threats, we must be able to detect infected systems before an attack occurs. In this paper, an attack pattern chain algorithm (APChain) is proposed to identify infected systems in real-time network environments, and a methodology for detecting abnormal behavior through network-based behavioral profiling is explained. APChain analyzes the attribute information of real-time network traffic, connects chains over time, and conducts behavioral profiling of different attack types to detect abnormal behavior. The dataset used in the experiment employed real-time traffic accumulated over a period of six months, and the proposed algorithm was developed into a prototype for the experiment. The C&C channel detection accuracy was measured at 0.996, the true positive rate at 1.0, and the false positive rate at 0.003. This study proposes a methodology that can overcome the limitations of conventional security mechanisms and suggests an approach to the detection of abnormal behavior in a real-time network environment.

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

“…CNN’s have been applied to image-based anomaly detection tasks, such as identifying defects in manufacturing processes [ 16 ] and detecting abnormalities in medical images [ 17 ]. RNNs have been applied to time-series-based anomaly detection tasks, such as identifying abnormal behavior in network traffic data [ 18 ] and detecting anomalies in sensor data [ 19 ]. In summary, deep learning methods have shown promising performance in various anomaly detection tasks.…”

Assessment of the actual security of the information system by studying the equivalence of the applied technologies

“…Within the framework of searching for effective solutions to the problems of the first group, studies are underway to create and improve antimalware consisting of subroutines (Zelinka et al 2018). The purpose of the latter is to detect and prevent the spread of viruses and other potentially malicious programs, and to remove them (Meridji et al, 2019) The developed and implemented software and hardware tools are oriented at achieving this goal, to a greater or lesser extent; these tools are based on a number of methods and technologies for detecting viruses that have certain disadvantages, primarily such as the method for matching the definition of a virus in a dictionary (Levy and Shalom, 2020), the method for generating and distributing signatures (i.e., the attack or virus signatures used to detect them) (Al-Asli and Ghaleb, 2019), the method for detecting suspicious (abnormal) program behavior (Seo and Lee, 2018), the emulation-based method for computer virus detection (Bist, 2013), the method for analyzing the sandbox environment (Madan et al, 2022), the white list technology, the heuristic analysis technologies (Rehman et al, 2018), and others.…”