A Survey of Intrusion Detection Techniques

Lakshminarayana, Deepthi Hassan; Philips, James; Tabrizi, Nasseh

doi:10.1109/icmla.2019.00187

Cited by 24 publications

(12 citation statements)

References 46 publications

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“…Machine learning (ML), a subset of artificial intelligence, is the collective term for all techniques and algorithms that allow computers to automatically learn from vast information using mathematical models [ [25] , [26] , [27] , [28] , [29] ]. Trends and interest in applying machine learning and deep learning-based anomaly detection to handle cyber-attack challenges have increased recently.…”

Section: Data Driven Methods Of Anomaly Detectionmentioning

confidence: 99%

Detection and mitigation of coordinated cyber-physical attack in CPPS

Sree Varshini,

Latha

2024

Heliyon

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Section: Data Driven Methods Of Anomaly Detectionmentioning

confidence: 99%

Detection and mitigation of coordinated cyber-physical attack in CPPS

Sree Varshini,

Latha

2024

Heliyon

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“…𝑥 𝑛 ) in The closest possible state to the actual value of the output y is used. Therefore, M is a specific observation of a multi-input-one-output data pair such that [36]: (10) 𝑦 𝑖 = 𝑓(𝑥 𝑖1 . 𝑥 𝑖2 .…”

Section: -2-5-gmdh Deep Neural Network (Gmdh-dnn)mentioning

confidence: 99%

SAutoIDS: A Semantic Autonomous Intrusion Detection System Based on Cellular Deep Learning and Ontology for Malware Detection in cloud computing

Nazoksara,

Etminan,

Hosseinzadeh

et al. 2024

Preprint

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Cloud computing (CC) is an online technology that has attracted the attention of many users and organizations today. Users send their requests through mobile to CC to perform a process. User requests are exposed to hacker attacks and malware risks. Penetration of malware in mobile devices causes loss of information or theft of mobile data. Today, various methods have been proposed to malware detection. In this paper, a semantic autonomous intrusion detection system (SAutoIDS) based on the ontology and cellular automata (CLA) and group method of data handling deep neural network (GMDH-DNN) is proposed to malware detection. The Semantic Multi-Level Approach (SMLA) processes of the data and transformed into semantic values based on a semantic level. The ontology method selects optimal features from malware data. Then the semantic data are divided into training (80%) and testing (20%). Training data are implemented to the GMDH-DNN for creating the model and CLA to optimize the GMDH model. Finally, testing data are entered into the optimized GMDH model and malwares are detected. We have used CICMalDroid2020 dataset to evaluate the SAutoIDS. By implementing the SAutoIDS, it was observed that the accuracy, precision, and recall improved by 21.96%, 22.41%, and 22.15%, compared to other methods.

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“…Based on the functionality, IDSs can be Networkbased, Host-based, and distributed based (Alom et al, 2015;Karatas et al, 2018). Similarly, based on detection methods, intrusion discovery systems can be operating as (i) rule-based (also called anomaly-based), (ii) signature-based (also called misuse-based) while analyzing and detecting attacks, and (iii) hybrid (Gümüşbaş et al, 2020;Karatas et al, 2018;Macas and Wu, 2020;Chaudhary et al, 2020;Lakshminarayana et al, 2019).…”

Section: Intrusion Detection Systems (Ids)mentioning

confidence: 99%

“…Deep Learning algorithms can identify known and unknown attacks, it can manage incomplete, inconsistent, and composite data (Geluvaraj et al, 2019). The authors (Lakshminarayana et al, 2019;Kim and Aminanto, 2017) studied various DL algorithms and then classified DL algorithms into Generative (Unsupervised), Discriminative (Supervised), and Hybrid. Table 8 describes some of the DL techniques under these categories (Sarker, 2022).…”

Section: Deep Learning Solutions To Cyber Securitymentioning

confidence: 99%

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

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Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation's defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study's key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

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A Survey of Intrusion Detection Techniques

Cited by 24 publications

References 46 publications

Detection and mitigation of coordinated cyber-physical attack in CPPS

Detection and mitigation of coordinated cyber-physical attack in CPPS

SAutoIDS: A Semantic Autonomous Intrusion Detection System Based on Cellular Deep Learning and Ontology for Malware Detection in cloud computing

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

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