Biblio

Cyber threats have been a major issue in the cyber security domain. Every hacker follows a series of cyber-attack stages known as cyber kill chain stages. Each stage has its norms and limitations to be deployed. For a decade, researchers have focused on detecting these attacks. Merely watcher tools are not optimal solutions anymore. Everything is becoming autonomous in the computer science field. This leads to the idea of an Autonomous Cyber Resilience Defense algorithm design in this work. Resilience has two aspects: Response and Recovery. Response requires some actions to be performed to mitigate attacks. Recovery is patching the flawed code or back door vulnerability. Both aspects were performed by human assistance in the cybersecurity defense field. This work aims to develop an algorithm based on Reinforcement Learning (RL) with a Convoluted Neural Network (CNN), far nearer to the human learning process for malware images. RL learns through a reward mechanism against every performed attack. Every action has some kind of output that can be classified into positive or negative rewards. To enhance its thinking process Markov Decision Process (MDP) will be mitigated with this RL approach. RL impact and induction measures for malware images were measured and performed to get optimal results. Based on the Malimg Image malware, dataset successful automation actions are received. The proposed work has shown 98% accuracy in the classification, detection, and autonomous resilience actions deployment.

Malicious hackers breach security perimeters, cause infrastructure disruptions as well as steal proprietary information, financial data, and violate consumers' privacy. Protection of the whole organization by using the firm's security officers can be besieged with faulty warnings. Engineers must shift from console to console to put together investigative clues as a result of today's fragmented security technologies that cause frustratingly sluggish investigations. Endpoint Detection and Response (EDR) solutions adds an extra layer of protection to prevent an endpoint action into a breach. EDR is the region's foremost detection and response tool that combines endpoint and network data to recognize and respond to sophisticated threats. Offering unrivaled security and operational effectiveness, it integrates prevention, investigation, detection, and responding in a single platform. EDR provides enterprise coverage and uninterrupted defense with its continuous monitoring and response to threats. We have presented a comprehensive review of existing EDRs through various security layers that includes detection, response and management capabilities which enables security teams to have unified end-to-end corporate accessibility, powerful analytics along with additional features such as web threat scan, external device scan and automatic reaction across the whole technological tower.

Threat Intelligence has been a key part of the success of Intrusion Detection, with several trusted sources leading to wide adoption and greater understanding of new and trending threats to computer networks. Identifying potential threats and live attacks on networks is only half the battle, knowing how to correctly respond to these threats and attacks requires in-depth and domain specific knowledge, which may be unique to subject experts and software vendors. Network Incident Responders and Intrusion Response Systems can benefit from a similar approach to Threat Intel, with a focus on potential Response actions. A qualitative comparison of current Threat Intel Sources and prominent Intrusion Response Systems is carried out to aid in the identification of key requirements to be met to enable the adoption of Response Intel. Building on these requirements, a template for Response Intel is proposed which incorporates standardised models developed by MITRE. Similarly, to facilitate the automated use of Response Intel, a structure for automated Response Actions is proposed.

Significant advancements in Intrusion Detection Systems has led to improved alerts. However, Intrusion Response Systems which aim to automatically respond to these alerts, is a research area which is not yet advanced enough to benefit from full automation. In Security Operations Centres, analysts can implement countermeasures using knowledge and past experience to adapt to new attacks. Attempts at automated Intrusion Response Systems fall short when a new attack occurs to which the system has no specific knowledge or effective countermeasure to apply, even leading to overkill countermeasures such as restarting services and blocking ports or IPs. In this paper, a countermeasure standard is proposed which enables countermeasure intelligence sharing, automated countermeasure adoption and execution by an Intrusion Response System. An attack scenario is created on an emulated network using the Common Open Research Emulator, where an insider attack attempts to exploit a buffer overflow on an Exim mail server. Experiments demonstrate that an Intrusion Response System with dynamic countermeasure knowledge can stop attacks that would otherwise succeed with a static predefined countermeasure approach.

The mass integration and deployment of intelligent technologies within critical commercial, industrial and public environments have a significant impact on business operations and society as a whole. Though integration of these critical intelligent technologies pose serious embedded security challenges for technology manufacturers which are required to be systematically approached, in-line with international security regulations.This paper establish security foundation for such intelligent technologies by deriving embedded security requirements to realise the core security functions laid out by international security authorities, and proposing microarchitectural characteristics to establish cyber resilience in embedded systems. To bridge the research gap between embedded and operational security domains, a detailed review of existing embedded security methods, microarchitectures and design practises is presented. The existing embedded security methods have been found ad-hoc, passive and strongly rely on building and maintaining trust. To the best of our knowledge to date, no existing embedded security microarchitecture or defence mechanism provides continuity of data stream or security once trust has broken. This functionality is critical for embedded technologies deployed in critical infrastructure to enhance and maintain security, and to gain evidence of the security breach to effectively evaluate, improve and deploy active response and mitigation strategies. To this end, the paper proposes three microarchitectural characteristics that shall be designed and integrated into embedded architectures to establish, maintain and improve cyber resilience in embedded systems for next-generation critical infrastructure.

The process for determining how to respond to a cyberattack involves evaluating many factors, including some with competing risks. Consequentially, decision makers in the private sector and policymakers in the U.S. government (USG) need a framework in order to make effective response decisions. The authors' research identified two competing risks: 1) the risk of not responding forcefully enough to deter a suspected attacker, and 2) responding in a manner that escalates a situation with an attacker. The authors also identified three primary factors that influence these risks: attribution confidence/time, the scale of the attack, and the relationship with the suspected attacker. This paper provides a framework to help decision makers understand how these factors interact to influence the risks associated with potential response options to cyberattacks. The views expressed do not reflect the official policy or position of the National Intelligence University, the Department of Defense, the U.S. Intelligence Community, or the U.S. Government.

A low power consumption three-position four-way direct drive control valve based on hybrid excited linear actuator (HELA-DDCV) was provided to meet the requirements of the response time and the power consumption. A coupling system numerical model was established and validated by experiments, which is based on Matlab/Simulink, from four points of view: electric circuit, electromagnetic field, mechanism and fluid mechanics. A dual-closed-loop PI control strategy for both spool displacement and coil current is adopted, and the process of displacement response was analyzed as well as the power consumption performances. The results show that the prototype valve spool displacement response time is less than 9.6ms. Furthermore, the holding current is less than 30% of the peak current in working process, which reduces the power consumption effectively and improves the system stability. Note that the holding current can be eliminated when the spool working at the ends of stroke, and 0.26 J energy is needed in once action independent of the working time.