Intrusion Detection System Techniques A Review

Omar Fitian Rashid; Noor Thamer Mahmood; Zahraa A. H. Alobaidy

Details

Publication Date

Fri Sep 23 2022

Journal Name

Specialusis Ugdymas

Volume

1

Issue Number

43

Choose Citation Style

Statistics

View publication

28

Statistics

Intrusion Detection System Techniques A Review

Anomaly

Artificial neural networks

Bio-inspired computing

Intrusion detection

Misuse

Omar Fitian Rashid

Noor Thamer Mahmood

Zahraa A. H. Alobaidy

...Show More Authors

With the high usage of computers and networks in the current time, the amount of security threats is increased. The study of intrusion detection systems (IDS) has received much attention throughout the computer science field. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection. This paper presents an overview of different intrusion detection systems and a detailed analysis of multiple techniques for these systems, including their advantages and disadvantages. These techniques include artificial neural networks, bio-inspired computing, evolutionary techniques, machine learning, and pattern recognition.

Publication Date

Sun Jan 10 2016

Journal Name

British Journal Of Applied Science & Technology

The Effect of Classification Methods on Facial Emotion Recognition ‎Accuracy

Facial emotions

feature selection

data clustering

modified K-Means clustering algorithm

LDA algorithm

Statistical classifier

Neural Network

Support Vector Machine (SVM)

Suhaila N.

...Show More Authors

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

View Publication Preview PDF

(2)

Publication Date

Tue Jul 01 2025

Journal Name

المجلة العراقية للعلوم الاقتصادية

Using the Hybrid ARDL–GRU Model in Investigating the Dynamic Relationship between Dinar Deposits and US Dollar Payments at the Central Bank of Iraq

Central Bank of Iraq

Dinar Deposits

US Dollar Payments

Time Series Analysis

Autoregressive Distributed Lag (ARDL)

GRU Neural Network

Hybrid ARDL–GRU Model

Abdulrazzaq Tallal

Omar Abdulmohsin

...Show More Authors

This research examines the relationship between dinar deposits and U.S. dollar payments at the Central Bank of Iraq using monthly data for the period 2016–2025. The ARDL model, the GRU neural network, and a hybrid ARDL–GRU model are applied. The results show that dollar payments are stationary at level, while dinar deposits become stationary after first differencing, with a significant positive long-run cointegrating relationship. The linear ARDL model has limited ability to capture sudden shocks, whereas the hybrid ARDL–GRU model achieves superior forecasting performance both in-sample and out-of-sample. The findings confirm the Central Bank of Iraq’s efficiency in managing domestic and foreign liquidity and maintaining market stab

View Publication Preview PDF

Publication Date

Sat May 31 2025

Journal Name

3rd International Scientific Conference For Human And Social Studies And Epistemological Challenges

Frankenstein Complex in Daniel H. Wilson's Robopocalypse ( ): Artificial Intelligence Conspiracies

With the advent of technology

the fear of losing control has consistently accompanied humanity. While humans have discovered

invented

and created many remarkable things

they have also been haunted by the possibility of their creations turning against them. This fear

often referred to as the Frankenstein Complex

derives its name from Mary Shelley's Frankenstein (􀀔􀀛􀀔􀀛)

in which Dr. Frankenstein's creation ultimately destroys its maker. The Frankenstein Complex embodies the subconscious fear of artificial intelligence (AI) surpassing and potentially dominating humankind. This apprehension has inspired numerous literary works

including Daniel H. Wilson’s novels. The present study examines the widespread fear of AI

which has fueled conspiracy theories in literature. These theories have not only amplified public anxiety but also hindered understanding among those with limited knowledge of AI and robotics. Dr. Wilson

a robotics expert

uses his imagination as a driving force for both his writing and his inventions. The research focuses on incidents leading to the rise of robots

the concept of the New War

how it was fought

and how it concluded. By comparing

contrasting

and analyzing literary elements and devices with prominent conspiracy theories

the study highlights the interplay between fiction and scientific development. The researchers conclude that while many conspiracy theories contain elements of truth

they often inspire scientists to explore them further. Additionally

some conspiracy theories warrant serious consideration. The study recommends acknowledging the Frankenstein Complex while maintaining faith in the potential benefits of artificial intelligence.

Ansam

Mayada

Zahraa

...Show More Authors

1 2 3 4 ... 751 752 753 754