Improving Intrusion Detection Systems Using Artificial Neural Networks

Jasim, Yaser AbdulAali

doi:10.14201/adcaij2018714965

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…A long short-term memory is primarily an advanced RNN that is less vulnerable to the diminishing curve problem, and also better capable of analyzing long-distance relationships in sequence; these features make it useful for NLP applications where sentences and documents are sequential in nature (Yaser, 2018). LSTM is only intended to memorize a portion of the sequence observed thus far as illustrated in Figure 4.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Analyzing Social Media Sentiment: Twitter as a Case Study

Jasim

Saeed

Raewf

2023

ADCAIJ

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This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed to do Twitter sentiment analysis to predict if a tweet is positive or negative using a dataset of tweets. The applied methods were trained using a publicly available dataset of 1,600,000 tweets. Several model architectures were trained, with the best one achieving a (93.91%) success rate in recognizing the tweets' matching sentiment. The model's high success rate makes it a valuable advisor and a technique that might be improved to enable an integrated sentiment analyzer system that can work in real-world situations for political marketing.

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Section: Long Short-term Memorymentioning

confidence: 99%

Analyzing Social Media Sentiment: Twitter as a Case Study

Jasim

Saeed

Raewf

2023

ADCAIJ

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“…Penyerang yang mengganggu sistem dengan masuk sebagai pengguna jaringan yang sah untuk mendapatkan hak istimewa tambahan dari pengguna biasa yang tidak sah dan pengguna sah yang menyalahgunakan hak yang diberikan kepada mereka. Namun IDS mendeteksi serangan atau perilaku abnormal yang terjadi di dalam jaringan dan segera mengeluarkan alarm yang mengetahui orang yang bertanggung jawab atas keamanan di jaringan saat serangan terjadi dan memintanya untuk mengambil tindakan yang diperlukan [5]. Penelitian mengusulkan sistem deteksi intrusi baru yang disebut TR-IDS, yang memanfaatkan fitur statistik dan fitur muatan dengan mengimplementasikan penyematan Word dan jaringan saraf konvolusi teks (Text-CNN) untuk mengekstrak informasi dari muatan secara efektif.…”

Section: Idsunclassified

Sistem Deteksi Intrusi pada Server secara Realtime Menggunakan Seleksi Fitur dan Firebase Cloud Messaging

Riza¹

2022

jsisfotek

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Intrusion detection is one of the fundamental parts of a security tool, such as adaptive security tools, intrusion detection systems, intrusion prevention systems and firewalls. There are various kinds of intrusion detection techniques used, the main problem of this intrusion technique is the performance problem. The accuracy of the intrusion detection technique greatly affects its performance, which needs to be improved to reduce the false alarm rate and increase the detection rate. In solving performance problems, multilayer perceptron, support vector machine (SVM), and other techniques have been used recently. This technique shows limitations and is inefficient for use in large data sets, such as system and network data. Intrusion detection systems are used in analyzing a very large data traffic; thus, an efficient classification technique is needed to overcome these problems. This issue is considered in this paper. The well-known machine learning techniques, namely, SVM, random forest, and extreme machine learning will be applied. These techniques are well known for their ability to classify. NSL knowledge discovery and data mining datasets were used, which were considered as benchmarks in the evaluation of intrusion detection mechanisms. The results show that ELM outperforms other approaches. Utilization of Firebase Cloud Messaging because it can work with multiple platforms in addition to the availability of a file store that can store all logs created by the JALA application.

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“…In the last few years, many researches have investigated the application of NNs to intrusion detection [11]. One of the most recent studies has been prepared by Yaser A. Jasim [12]. In this work, the Backpropagation Neural Network is used due to its ability and speed to recognize packet patterns obtained from the network to detect intrusion of the system.…”

Section: Role Of Artificial Intelligence In Cybersecurity Improvementmentioning

confidence: 99%

Role of Artificial Intelligence in Cybersecurity Improvement

Malik

2021

IJECI

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In recent years, the importance of cyber security and personal privacy protection has increased significantly. To protect computer systems from attacks several solutions have been proposed by researchers and many cybersecurity tools have been developed. However, with the increase in the complexity of hacking attempts, the old traditional ways to counter malware cannot be relied upon. This paper introduces the need for the development of innovative cybersecurity skills and how many security systems like Intrusion Detection Systems (IDS) can be improved using Artificial Intelligence (AI). AI is used by nearly every company around the world to optimize and enhance their system's safety. In this paper, advanced cybersecurity solutions for intrusion detection systems enhanced with the use of artificial intelligence are discussed. The advantages and disadvantages of existing methods are explained and some promising future directions are also discussed.

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Improving Intrusion Detection Systems Using Artificial Neural Networks

Cited by 9 publications

References 3 publications

Analyzing Social Media Sentiment: Twitter as a Case Study

Analyzing Social Media Sentiment: Twitter as a Case Study

Sistem Deteksi Intrusi pada Server secara Realtime Menggunakan Seleksi Fitur dan Firebase Cloud Messaging

Role of Artificial Intelligence in Cybersecurity Improvement

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