Intelligent deception techniques against adversarial attack on the industrial system

Kumari, Suchi; Yadav, Riteshkumar Jayprakash; Namasudra, Suyel; Hsu, Ching‐Hsien

doi:10.1002/int.22384

Cited by 32 publications

(12 citation statements)

References 38 publications

(69 reference statements)

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“…They detect misuse of attempts either by a legal person or by third parties [4]. Break-in security vulnerabilities, misuse of the system are attacks that the IDS can identify [5][6][7][8][9]. Analysis of the transmitted packet in a network and through the collection of data, IDS worked [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Network Intrusion Detection

Nebhen¹,

Mehmood²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

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Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model's performance. Next, we use various Support Vector Machine (SVM) types to detect intrusion and the Adaptive Neuro-Fuzzy System (ANFIS) to categorize probe, U2R, R2U, and DDOS attacks. The validation of the proposed method is calculated through Fine Gaussian SVM (FGSVM), which is 99.3% for the binary class. Mean Square Error (MSE) is reported as 0.084964 for training data, 0.0855203 for testing, and 0.084964 to validate multiclass categorization.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Network Intrusion Detection

Nebhen¹,

Mehmood²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

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show abstract

“…Researchers have effectively responded to the need for protecting information by proposing many encryption techniques [1][2][3][4][5][6][7][8][9][10][11][12]. These techniques can be categorized either by how they process their input (stream or block ciphers) or by the shared information between the communicating parties (symmetric or asymmetric).…”

Section: Introductionmentioning

confidence: 99%

Light and Secure Encryption Technique Based on Artificially Induced Chaos and Nature-Inspired Triggering Method

Al-Muhammed

Zitar

2022

Symmetry

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Encryption is the de facto method for protecting information, whether this information is locally stored or on transit. Although we have many encryption techniques, they have problems inherited from the computational models that they use. For instance, the standard encryption technique suffers from the substitution box syndrome—the substitution box does not provide enough confusion. This paper proffers a novel encryption method that is both highly secure and lightweight. The proposed technique performs an initial preprocessing on its input plaintext, using fuzzy substitutions and noising techniques to eliminate relationships to the input plaintext. The initially encrypted plaintext is next concealed in enormously complicated codes that are generated using a chaotic system, whose behavior is controlled by a set of operations and a nature-inspired triggering technique. The effectiveness of the security of the proposed technique is analyzed using rigorous randomness tests and entropy.

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“…Deceptive defense [83] ideas have emerged in recent years, and combining deceptive defense methods with MTD could be a future research trend. There is already a trend of combining deceptive defense with intelligence [81], where deceptive defense provides misleading information by actively exposing false intelligence of the protected system, thus allowing the attacker to move the attack in the direction of favoring the defender by actively creating and reinforcing the observation and identification of deceptive information.…”

mentioning

confidence: 99%

A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive

Sun¹,

Zhu²,

Fei³

et al. 2023

Applied Sciences

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Represented by reactive security defense mechanisms, cyber defense possesses a static, reactive, and deterministic nature, with overwhelmingly high costs to defend against ever-changing attackers. To change this situation, researchers have proposed moving target defense (MTD), which introduces the concept of an attack surface to define cyber defense in a brand-new manner, aiming to provide a dynamic, continuous, and proactive defense mechanism. With the increasing use of machine learning in networking, researchers have discovered that MTD techniques based on machine learning can provide omni-bearing defense capabilities and reduce defense costs at multiple levels. However, research in this area remains incomplete and fragmented, and significant progress is yet to be made in constructing a defense mechanism that is both robust and available. Therefore, we conducted a comprehensive survey on MTD research, summarizing the background, design mechanisms, and shortcomings of MTD, as well as relevant features of intelligent MTD that are designed to overcome these limitations. We aim to provide researchers seeking the future development of MTD with insight into building an intelligently affordable, optimized, and self-adaptive defense mechanism.

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Intelligent deception techniques against adversarial attack on the industrial system

Cited by 32 publications

References 38 publications

A Hybrid Approach for Network Intrusion Detection

A Hybrid Approach for Network Intrusion Detection

Light and Secure Encryption Technique Based on Artificially Induced Chaos and Nature-Inspired Triggering Method

A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive

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