Efficient Early Anomaly Detection of Network Security Attacks Using Deep Learning

Tanwir Ahmad; Dragos Truscan

doi:10.1109/csr57506.2023.10224923

Efficient Early Anomaly Detection of Network Security Attacks Using Deep Learning

Information Technology

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

1 Sitaatiot (Scopus)

44 Lataukset (Pure)

Abstrakti

We present a deep-learning (DL) anomaly-based Intrusion Detection System (IDS) for networked systems, which is able to detect in realtime anomalous network traffic corresponding to security attacks while they are ongoing. Compared to similar approaches, our IDS does not require a fixed number of network packets to analyze in order to make a decision on the type of traffic and it utilizes a more compact neural network which improves its realtime performance. As shown in the experiments using the CICIDS2017 and USTC-TFC-2016 datasets, the approach is able to detect anomalous traffic with high precision and recall. In addition, the approach is able to classify the network traffic by using only a very small portion of the network flows.

Alkuperäiskieli	Englanti
Otsikko	2023 IEEE International Conference on Cyber Security and Resilience (CSR)
Kustantaja	IEEE
ISBN (painettu)	979-8-3503-1171-6
DOI - pysyväislinkit	https://doi.org/10.1109/csr57506.2023.10224923
Tila	Julkaistu - elok. 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	IEEE International Conference on Cyber Security and Resilience - Kesto: 31 heinäk. 2023 → …

Konferenssi

Konferenssi	IEEE International Conference on Cyber Security and Resilience
Lyhennettä	CSR
Ajanjakso	31/07/23 → …

Pääsy asiakirjaan

10.1109/csr57506.2023.10224923

early anomaly detectionHyväksytty kirjoittajan käsikirjoitus, 559 KBLisenssi: Publisher rights policy

https://urn.fi/URN:NBN:fi-fe20230918129575

Viittausmuodot

@inproceedings{8e8bfe09955542938fe5450d7a157e25,

title = "Efficient Early Anomaly Detection of Network Security Attacks Using Deep Learning",

abstract = "We present a deep-learning (DL) anomaly-based Intrusion Detection System (IDS) for networked systems, which is able to detect in realtime anomalous network traffic corresponding to security attacks while they are ongoing. Compared to similar approaches, our IDS does not require a fixed number of network packets to analyze in order to make a decision on the type of traffic and it utilizes a more compact neural network which improves its realtime performance. As shown in the experiments using the CICIDS2017 and USTC-TFC-2016 datasets, the approach is able to detect anomalous traffic with high precision and recall. In addition, the approach is able to classify the network traffic by using only a very small portion of the network flows.",

keywords = "Deep Learning, Anomaly detection, early detection, Intrusion Detection",

author = "Tanwir Ahmad and Dragos Truscan",

year = "2023",

month = aug,

doi = "10.1109/csr57506.2023.10224923",

language = "English",

isbn = "979-8-3503-1171-6",

booktitle = "2023 IEEE International Conference on Cyber Security and Resilience (CSR)",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Cyber Security and Resilience, CSR ; Conference date: 31-07-2023",

}

TY - GEN

T1 - Efficient Early Anomaly Detection of Network Security Attacks Using Deep Learning

AU - Ahmad, Tanwir

AU - Truscan, Dragos

PY - 2023/8

Y1 - 2023/8

N2 - We present a deep-learning (DL) anomaly-based Intrusion Detection System (IDS) for networked systems, which is able to detect in realtime anomalous network traffic corresponding to security attacks while they are ongoing. Compared to similar approaches, our IDS does not require a fixed number of network packets to analyze in order to make a decision on the type of traffic and it utilizes a more compact neural network which improves its realtime performance. As shown in the experiments using the CICIDS2017 and USTC-TFC-2016 datasets, the approach is able to detect anomalous traffic with high precision and recall. In addition, the approach is able to classify the network traffic by using only a very small portion of the network flows.

AB - We present a deep-learning (DL) anomaly-based Intrusion Detection System (IDS) for networked systems, which is able to detect in realtime anomalous network traffic corresponding to security attacks while they are ongoing. Compared to similar approaches, our IDS does not require a fixed number of network packets to analyze in order to make a decision on the type of traffic and it utilizes a more compact neural network which improves its realtime performance. As shown in the experiments using the CICIDS2017 and USTC-TFC-2016 datasets, the approach is able to detect anomalous traffic with high precision and recall. In addition, the approach is able to classify the network traffic by using only a very small portion of the network flows.

KW - Deep Learning

KW - Anomaly detection

KW - early detection

KW - Intrusion Detection

U2 - 10.1109/csr57506.2023.10224923

DO - 10.1109/csr57506.2023.10224923

M3 - Conference contribution

SN - 979-8-3503-1171-6

BT - 2023 IEEE International Conference on Cyber Security and Resilience (CSR)

PB - IEEE

T2 - IEEE International Conference on Cyber Security and Resilience

Y2 - 31 July 2023

ER -

Efficient Early Anomaly Detection of Network Security Attacks Using Deep Learning

Abstrakti

Konferenssi

Pääsy asiakirjaan

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Viittausmuodot