Innovative signature based intrusion detection system: Parallel processing and minimized database

Almutairi, Abdullah H.; Abdelmajeed, Nabih T

doi:10.1109/fads.2017.8253208

Cited by 23 publications

(6 citation statements)

References 2 publications

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“…Although this technique works reasonably well for recurrent attacks and can achieve better accuracy compared to anomaly-based detection systems, it will not be useful to detect new attacks since their signatures have not yet been identified. For example, [84] introduced a parallel processing technique for a small database with the most frequent signature and updating agent. To assist with a simultaneous search of both small databases, containing fewer signatures, and the complimentary database, whose size is larger since it stores old signatures, which are infrequently used; the authors introduced a multi-reading technique.…”

Section: No Areas Examplesmentioning

confidence: 99%

An In-Depth Examination of Artificial Intelligence-Enhanced Cybersecurity in Robotics, Autonomous Systems, and Critical Infrastructures

Santoso,

Finn

2024

IEEE Trans. Serv. Comput.

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Recent developments in cutting-edge robotics have been constantly faced with increased cyber-threats, not only in terms of the quantity or the frequency of attacks, but also when it comes to the quality and the severity of the intrusions. This paper provides a systematic overview and critical assessment of state-of-the-art scientific developments in the security aspects of robotics, autonomous systems, and critical infrastructures. Our review highlights open research questions addressing significant research gaps and/or new conceptual frameworks, considering recent advancements in artificial intelligence (AI) and machine learning. Thus the contributions of this paper can be summarised as follows. We first compare and contrast the benefits of multiple cutting-edge AI-based learning algorithms (e.g., fuzzy logic and neural networks) relative to traditional model-based systems (e.g. distributed control and filtering). Subsequently, we point out some specific benefits of AI algorithms to quickly learn and adapt the dynamics of non-linear systems in the absence of complex mathematical models. We also present some potential future research directions (open challenges) in the field. Lastly, this review also delivers an open message to encourage collaborations among experts from multiple disciplines. The implementation of multiple AI algorithms to tackle current security issues in robotics will transform and create novel, hybrid knowledge for intelligent cybersecurity at the application level.

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Section: No Areas Examplesmentioning

confidence: 99%

An In-Depth Examination of Artificial Intelligence-Enhanced Cybersecurity in Robotics, Autonomous Systems, and Critical Infrastructures

Santoso,

Finn

2024

IEEE Trans. Serv. Comput.

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“…They concluded that it supports application development for Contiki OS, but did not import the IDS modules to Contiki OS to test its performance in real world IOT environment. In another work Almutairi et al [3] introduced a solution in parallel processing environment with the help of most frequent features and an updating agent. They proved that this module can be used for both host and network based IDS.…”

Section: Literature Surveymentioning

confidence: 99%

<strong>A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks</strong>

Bandyopadhyay¹,

Chowdhury²,

Banerjee³

et al. 2020

Preprint

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. In today’s world, cyber attack is one of the major issues concerning the organizations that deal with technologies like cloud computing, big data, IoT etc. In the area of cyber security, intrusion detection system (IDS) plays a crucial role to identify suspicious activities in the network traffic. Over the past few years, a lot of research has been done in this area but in the current scenario, network attacks are diversifying in both volume and variety. In this regard, this research article proposes a novel IDS where a combination of information gain and decision tree algorithm has been used for the purpose of dimension reduction and classification. For experimental purpose the NSL-KDD dataset has been used. Initially out of 41 features present in the dataset only 5 high information gain valued features are selected for classification purpose. The applicability of the selected features are evaluated through various machine learning based algorithms. The experimental result shows that the decision tree based algorithm records highest recognition accuracy among all the classifiers. Based on the initial classification result a novel methodology based on decision tree has been further developed which is capable of identifying multiple attacks by analyzing the packets of various transactions in real time.

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“…Network anomaly detection has typically consists of comparative analysis of known threats against current activity on a network [5], [6]. However, while highly accurate and effective, it is possible for such mechanisms to fail in the case of a "Black Swan" or "Zero-Day" attack.…”

Section: Related Workmentioning

confidence: 99%

2D2N: A Dynamic Degenerative Neural Network for Classification of Images of Live Network Data

Flanagan

Fallon

Jacob

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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The detection of new, novel attacks on organizational networks is a problem of ever-increasing relevance in today's society. Research in the area is focused on the detection of "Zero-Day" and "Black Swan" events through the use of machine learning technologies. Where previous technologies needed a known example of malicious behavior to detect a similar event, recent advances in anomaly detection on network activity has shown promise of detecting novel attacks. In a real word environment however, novel behavior occurs relatively frequently as users utilize new software applications and new standards in networking. Changes such as these, while of notable importance to network security technicians, may not present themselves as an imminent threat to a network. This paper proposes a novel method for the detection and classification of changes in networking behavior. Through the use of a Dynamic Degenerative Neural Network (2D2N), changes in recognizable user activity are dynamically classified and stored for future reference. Through the use of a time-based entropy function, infrequent activity can be analyzed and given precedence over frequent activity. This aids in the classification of abnormal activity for fast, efficient assessment by the relevant persons in an organization. The proposed method enables the detection, classification and scoring of any and all user activity on a network. Evaluation of the proposed method is based upon live data gathered from a large, multinational organization.

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Innovative signature based intrusion detection system: Parallel processing and minimized database

Cited by 23 publications

References 2 publications

An In-Depth Examination of Artificial Intelligence-Enhanced Cybersecurity in Robotics, Autonomous Systems, and Critical Infrastructures

An In-Depth Examination of Artificial Intelligence-Enhanced Cybersecurity in Robotics, Autonomous Systems, and Critical Infrastructures

<strong>A Decision Tree Based Intrusion Detection System for Identification of Malicious Web Attacks</strong>

2D2N: A Dynamic Degenerative Neural Network for Classification of Images of Live Network Data

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