Mehrnoosh Monshizadeh

Research activity per year

Education/Academic qualification

Tekn. toht., tietoliikennetekniikka

Award Date: 29 May 2023

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1 Similar Profiles

10 Conference article in proceedings
4 Chapter
4 Article
3 Patent
More
- 1 Doctoral Thesis

A Deep Intrusion Detection Model for Network Traffic Payload Analysis
Hojjatinia, S., Monshizadeh, M. & Khatri, V., 2023, 2023 31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023. Begusic, D., Rozic, N., Radic, J. & Saric, M. (eds.). IEEE, 7 p. (SoftCOM).
Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
Machine Learning Techniques to Detect Known and Novel Cyber-attacks
Monshizadeh, M., 2023, Aalto University. 181 p.
Research output: Thesis › Doctoral Thesis › Collection of Articles

Open Access
A deep density based and self-determining clustering approach to label unknown traffic
Monshizadeh, M., Khatri, V., Kantola, R. & Yan, Z., Nov 2022, In: Journal of Network and Computer Applications. 207, 18 p., 103513.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
11 Citations (Scopus)

71 Downloads (Pure)
Ethernet Communication over IP Transport for Industrial and Private Cellular Network
Khatri, V., Monshizadeh, M. & Tiirikainen, K., 2022, 2022 30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022. Begusic, D., Rozic, N., Radic, J. & Saric, M. (eds.). IEEE, (SoftCOM).
Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
1 Citation (Scopus)
Improving Data Generalization with Variational Autoencoders for Network Traffic Anomaly Detection
Monshizadeh, M., Khatri, V., Gamdou, M., Kantola, R. & Yan, Z., 2021, In: IEEE Access. 9, p. 56893-56907 15 p., 9399440.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
16 Citations (Scopus)

299 Downloads (Pure)

Researchers from Nokia Bell Laboratories Detail New Studies and Findings in the Area of Electronics (A Deep Density Based and Self-determining Clustering Approach To Label Unknown Traffic)
Monshizadeh, M.
18/11/2022
1 item of Media coverage
Press/Media: Media appearance

Mehrnoosh Monshizadeh

Personal profile

Education/Academic qualification

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

A Deep Intrusion Detection Model for Network Traffic Payload Analysis

Machine Learning Techniques to Detect Known and Novel Cyber-attacks

A deep density based and self-determining clustering approach to label unknown traffic

Ethernet Communication over IP Transport for Industrial and Private Cellular Network

Improving Data Generalization with Variational Autoencoders for Network Traffic Anomaly Detection

Researchers from Nokia Bell Laboratories Detail New Studies and Findings in the Area of Electronics (A Deep Density Based and Self-determining Clustering Approach To Label Unknown Traffic)

Mehrnoosh Monshizadeh

Personal profile

Education/Academic qualification

Fingerprint

Collaborations and top research areas from the last five years

Research output

A Deep Intrusion Detection Model for Network Traffic Payload Analysis

Machine Learning Techniques to Detect Known and Novel Cyber-attacks

A deep density based and self-determining clustering approach to label unknown traffic

Ethernet Communication over IP Transport for Industrial and Private Cellular Network

Improving Data Generalization with Variational Autoencoders for Network Traffic Anomaly Detection

Press/Media

Researchers from Nokia Bell Laboratories Detail New Studies and Findings in the Area of Electronics (A Deep Density Based and Self-determining Clustering Approach To Label Unknown Traffic)