Biblio

Successful information and communication technology (ICT) may propel administrative procedures forward quickly. In order to achieve efficient usage of TCT in their businesses, ICT strategies and plans should be examined to ensure that they align with the organization's visions and missions. Efficient software and hardware work together to provide relevant data that aids in the improvement of how we do business, learn, communicate, entertain, and work. This exposes them to a risky environment that is prone to both internal and outside threats. The term “security” refers to a level of protection or resistance to damage. Security can also be thought of as a barrier between assets and threats. Important terms must be understood in order to have a comprehensive understanding of security. This research paper discusses key terms, concerns, and challenges related to information systems and security auditing. Exploratory research is utilised in this study to find an explanation for the observed occurrences, problems, or behaviour. The study's findings include a list of various security risks that must be seriously addressed in any Information System and Security Audit.

Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordi-nate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces.

Security analysts conduct continuous evaluations of cyber-defense tools to keep pace with advanced and persistent threats. Cyber agility has become a critical proactive security resource that makes it possible to measure defense adjustments and reactions to rising threats. Subsequently, machine learning has been applied to support cyber agility prediction as an essential effort to anticipate future security performance. Nevertheless, apt and treacherous actors motivated by economic incentives continue to prevail in circumventing machine learning-based protection tools. Adversarial learning, widely applied to computer security, especially intrusion detection, has emerged as a new area of concern for the recently recognized critical cyber agility prediction. The rationale is, if a sophisticated malicious actor obtains the cyber agility parameters, correct prediction cannot be guaranteed. Unless with a demonstration of white-box attack failures. The challenge lies in recognizing that unconstrained adversaries hold vast potential capabilities. In practice, they could have perfect-knowledge, i.e., a full understanding of the defense tool in use. We address this challenge by proposing an adversarial machine learning approach that achieves accurate cyber agility forecast through mapped nefarious influence on static defense tools metrics. Considering an adversary would aim at influencing perilous confidence in a defense tool, we demonstrate resilient cyber agility prediction through verified attack signatures in dynamic learning windows. After that, we compare cyber agility prediction under negative influence with and without our proposed dynamic learning windows. Our numerical results show the model's execution degrades without adversarial machine learning. Such a feigned measure of performance could lead to incorrect software security patching.

Traditionally, the focus of security and ensuring confidentiality, integrity, and availability of data in spacecraft systems has been on the ground segment and the uplink/downlink components. Although these are the most obvious attack vectors, potential security risks against the satellite's platform is also a serious concern. This paper discusses a notional satellite architecture and explores security vulnerabilities using a systems-level approach. Viewing attacks through this paradigm highlights several potential attack vectors that conventional satellite security approaches fail to consider. If left undetected, these could yield physical effects limiting the satellite's mission or performance. The approach presented aids in risk analysis and gives insight into architectural design considerations which improve the system's overall resiliency.