Adaptive Cyber Defense Research Articles

Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches

In fifth Generation (5G) networks, protection from internal attacks, external breaches, violation of confidentiality, and misuse of network vulnerabilities is a challenging task. Various approaches, especially deep-learning (DL) prototypes, have been adopted in order to counter such challenges. For 5G network defense, DL module are recommended here in order to symptomize suspicious NetFlow data. This module behaves as a virtual network function (VNF) and is placed along a 5G network. The DL module as a cyber threat-symptomizing (CTS) unit acts as a virtual security scanner along the 5G network data analytic function (NWDAF) to monitor the network data. When the data were found to be suspicious, causing network bottlenecks and let-downs of end-user services, they were labeled as “Anomalous”. For the best proactive and adaptive cyber defense system (PACDS), a logically organized modular approach has been followed to design the DL security module. In the application context, improvements have been made to input features dimension and computational complexity reduction with better response times and accuracy in outlier detection. Moreover, key performance indicators (KPIs) have been proposed for security module placement to secure interslice and intraslice communication channels from any internal or external attacks, also suggesting an adaptive defense mechanism and indicating its placement on a 5G network. Among the chosen DL models, the CNN model behaves as a stable model during behavior analysis in the results. The model classifies botnet-labeled data with 99.74% accuracy and higher precision.

Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems. In this paper, we investigate a problem where multiagent systems sensing and acting in an environment contribute to adaptive cyber defense. We present a learning strategy that enables multiple agents to learn optimal policies using multiagent reinforcement learning (MARL). Our proposed approach is inspired by the multiarmed bandits (MAB) learning technique for multiple agents to cooperate in decision making or to work independently. We study a MAB approach in which defenders visit a system multiple times in an alternating fashion to maximize their rewards and protect their system. We find that this game can be modeled from an individual player’s perspective as a restless MAB problem. We discover further results when the MAB takes the form of a pure birth process, such as a myopic optimal policy, as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.

SPM: A Novel Hierarchical Model for Evaluating the Effectiveness of Combined ACDs in a Blockchain-Based Cloud Environment

Cloud computing provides blockchain a flexible and cost-effective service by on-demand resource sharing, which also introduces additional security risks. Adaptive Cyber Defense (ACD) provides a solution that continuously changes the attack surface according to the cloud environments. The dynamic characteristics of ACDs give defenders a tactical advantage against threats. However, when assessing the effectiveness of ACDs, the structure of traditional security evaluation methods becomes unstable, especially when combining multiple ACD techniques. Therefore, there is still a lack of standard methods to quantitatively evaluate the effectiveness of ACDs. In this paper, we conducted a thorough evaluation with a hierarchical model named SPM. The proposed model is made up of three layers integrating Stochastic Reward net (SRN), Poisson process, and Martingale theory incorporated in the Markov chain. SPM provides two main advantages: (1) it allows explicit quantification of the security with a straightforward computation; (2) it helps obtain the effectiveness metrics of interest. Moreover, the hierarchical architecture of SPM allows each layer to be used independently to evaluate the effectiveness of each adopted ACD method. The simulation results show that SPM is efficient in evaluating various ACDs and the synergy effect of their combination, which thus helps improve the system configuration accordingly.

Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry.

As the likelihood and impact of cyber-attacks continue to grow, organizations realize the need to invest in specialized methods to protect their digital data and the information they circulate or manage. Due to its broad use, game theory has evolved into a concept that can be applied practically while analyzing and modifying existing cyber protection methods to arrive at the best possible conclusions. This study presents an innovative hybrid model that combines game theory and advanced machine learning methods for adaptive cyber defense strategies. Specifically, a repetitive game methodology is implemented to analyze cyber-attacks and model behaviors and study how defenders and attackers make decisions in a competing field. Based on Bayesian inference, the proposed method can predict the next steps in the game to produce the appropriate countermeasures and implement the best cyber defense strategies that govern an organization. The suggested system introduced to the academic literature for the first time was successfully tested in a particular application scenario involving the digital music industry and coping with impending cyber-attacks.

Novel Network Testbed of Adaptive Cyber Defense System Design for Web Data security

In recent generations, the cyber security scenario has transposed significantly from conventional response based security mechanisms to forward security strategies. And this transformation is still continuing which is shifting it from visionary security strategies to cyber immunity which eliminates the cyber risks by introducing uncompromising and adaptive security measures. In the process of developing new security algorithms/ procedures, accurate modeling and effective simulation play a vital part for the robustness and effectiveness of proposed system. It's also necessary to break down the deportment of proposed system against multiple types of known cyber attacks. This paper focuses on the being network testbeds for an effective analysis and monitoring while proposing a new network testbed for examining new security generalities like cyber impunity. The proposed network testbed is designed to incorporate the techniques and procedures of Nature inspired Cyber Security to accommodate the adaptive responses against the sophisticated and ever advancing cyber attacks. The proposed testbed provides customizable analytical tool to design, test and examine the new security algorithms through a rich set of attack scripts. It also allows inventors to design, execute, and evaluate their defensive techniques with library support.

Adaptive Cyber Defense Against Multi-Stage Attacks Using Learning-Based POMDP

Growing multi-stage attacks in computer networks impose significant security risks and necessitate the development of effective defense schemes that are able to autonomously respond to intrusions during vulnerability windows. However, the defender faces several real-world challenges, e.g., unknown likelihoods and unknown impacts of successful exploits. In this article, we leverage reinforcement learning to develop an innovative adaptive cyber defense to maximize the cost-effectiveness subject to the aforementioned challenges. In particular, we use Bayesian attack graphs to model the interactions between the attacker and networks. Then we formulate the defense problem of interest as a partially observable Markov decision process problem where the defender maintains belief states to estimate system states, leverages Thompson sampling to estimate transition probabilities, and utilizes reinforcement learning to choose optimal defense actions using measured utility values. The algorithm performance is verified via numerical simulations based on real-world attacks.

A nonproprietary language for the command and control of cyber defenses – OpenC2

The fact that cyber attacks are getting increasingly sophisticated and performed at machine speed motivated the development of OpenC2. This paper presents Open Command and Control (OpenC2), a suite of specifications that enable command and control of cyber defense systems and components at machine speed and in a manner that is agnostic of the underlying technologies utilized or of any other aspects of particular implementations. OpenC2 provides the means to introduce standardized interfaces to cyber defense systems, enabling interoperability and allowing seamless integration, communication, and operation between decoupled blocks that perform cyber defense functions. The suit of specifications includes a semantic language that enables machine-to-machine communication for purposes of command and control of cyber defense components, actuator profiles that specify the subset of the OpenC2 language and may extend it in the context of specific cyber defense functions, and transfer specifications that utilize existing protocols and standards to implement OpenC2 in particular environments. Fundamentally, OpenC2 addresses the acting part of the Integrated Adaptive Cyber Defense (IACD) framework and is designed to be technology agnostic, concise, abstract, and extensible. Ultimately, OpenC2 is a building block for enabling coordinated defense in cyber-relevant time, shifting traditional monolithic cyber response approaches to more granular, flexible, and adaptive.

Cyber Deception: Overview and the Road Ahead

Since the concept of deception for cybersecurity was introduced decades ago, several primitive systems, such as honeypots, have been attempted. More recently, research on adaptive cyber defense techniques has gained momentum. The new research interests in this area motivate us to provide a high-level overview of cyber deception. We analyze potential strategies of cyber deception and its unique aspects. We discuss the research challenges of creating effective cyber deception–based techniques and identify future research directions.

Cybersecurity: From Months to Milliseconds

Computer technology is the nexus of our critical infrastructures, yet it remains extremely vulnerable to cyberattacks. A proposed Integrated Adaptive Cyber Defense architecture promises to create a healthy cyber ecosystem by automating many risk decisions and optimizing human oversight of security processes too complex or important for machines alone to solve.

Adaptive cyberdefense for survival and intrusion tolerance

While providing some resistance against cyberattacks, current approaches to securing networked and distributed information systems are mainly concerned with static prevention measures. For example, signature-based systems can only detect known attacks and tend to provide brittle, all-or-nothing protection. New work in survivability and intrusion tolerance focuses on augmenting existing information systems with adaptive defenses. A middleware-based survivability toolkit lets applications use network-and host-based mechanisms in their own defense.