Data security analysis for DDoS defense of cloud based networks

Hussein, Mohammed; Zainal, Nasharuddin; Jaber, Aws Naser

doi:10.1109/scored.2015.7449345

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Dilansir dari Arbor Network bahwa teknologi cloud sangat rentan terhadap serangan DDoS, frekuensinya meningkat tajam di beberapa tahun terakhir, dan biasanya ditargetkan ke port terutama layanan web HTTTP tipe GET seperti TCP SYN, ICMP (Internet Control Message Protocol), NTP (Network Time Protocol) atau SNMP (Simple Network Management Protocol) yang menimbulkan dampak kerugian ekonomi dan hilangnya kepercayaan terhadap ISP (Intenet Service Provider) [12] Tentu saja hal tersebut menimbulkan kekhawatiran sehingga [6] mengusulkan honeypot sebagai sebuah cara yang praktis untuk melindungi aset dari serangan dan penyalahgunaan, serta teknik data mining menjadi langkah yang tepat untuk memprediksi dan mengetahui vulnerabilty-kerentanan terhadap arus anomali yang mencurigakan di dalam trafficlalulintas berdasarkan data log honeypot. Data mining dapat membantu dalam menilai kesamaan karakteristik, prediksi [5], dan menemukan pola, menguraikan penemuan dalam sumber data yang mencakup database, dan gudang data pada sistem dinamis. Di dalam data mining dikenal sebuah metode clustering-kekelompokan, clustering adalah proses pengelompokkan beberapa data ke dalam cluster-kelompok atau gugus yang memiliki kemiripan paramater dan objek satu sama lain dan yang tidak memiliki sifat yang sangat tidak mirip dengan obyek lain maka berada pada cluster yang berbeda [11].…”

Section: Pendahuluanunclassified

The Analysis Network Forensics Using Honeypot on Public Cloud Computing Service Network

Wulandari¹

2018

JTekInfULM

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This research presents visualization in dashboard using AWN campus honeypot log data connected to IHP (Kemkominfo) Jakarta on public cloud computing service network to categorize time stamp in data. Package attack data is divided into three categories namely morning, noon, and night based on Time Western Indonesia (WIB). DDoS attacks attacked several ports 21, 80, 135, and 445. K -means clustering method is implemented in this research to get categorization result of time of effective attack to know DDoS attack attack and cyber profilling which is expected to help monitoring process of anticipation of vulnerability cloud network of ddos / cyber crime attacks. The results of this study indicate that the method used to obtain results in accordance with the objectives.

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Section: Pendahuluanunclassified

The Analysis Network Forensics Using Honeypot on Public Cloud Computing Service Network

Wulandari¹

2018

JTekInfULM

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“…Tentu saja hal tersebut menimbulkan kekhawatiran sehingga [6] mengusulkan honeypot sebagai sebuah cara yang praktis untuk melindungi aset dari serangan dan penyalahgunaan, serta teknik data mining menjadi langkah yang tepat untuk memprediksi dan mengetahui vulnerabiltykerentanan terhadap arus anomali yang mencurigakan di dalam traffic jaringan berdasarkan data log honeypot. Data mining dapat membantu dalam menilai kesamaan karakteristik, prediksi [5], dan menemukan pola, menguraikan penemuan dalam sumber data yang mencakup database, dan gudang data pada sistem dinamis. Di dalam data mining dikenal sebuah metode clustering-kekelompokan, clustering adalah proses pengelompokkan beberapa data ke dalam cluster-kelompok atau gugus yang memiliki kemiripan paramater dan objek satu sama lain dan yang tidak memiliki sifat yang sangat tidak mirip dengan obyek lain maka berada pada cluster yang berbeda [11].…”

Section: Pendahuluanunclassified

The Use of K-Means Algorithm on Datalog Honeynet for Profiling Ddos Attacks

Wulandari¹

2017

JTekInfULM

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Cloud computing are admired for excellence, flexible benefits, and can be used together in various aspects and areas of business. According to Arbor Network that cloud technology is very vulnerable to DDoS attacks and the frequency of attacks has increased sharply in recent years.This research is the result of cooperation with AWN University and Indonesia Honeypot Project (IHP) Ministry of Communications and Informatics. The results suggest that honeynet as a way to protect and monitor the security of public cloud computing networks, and analyze its datalog through the k-means method approach to recognize cyber profilling DDoS attacks based on the timestamp recorded in the data.

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“…However, owing to constantly evolving cyber attacks, it is still highly challenging to protect IT systems against threats and malicious behaviors in networks. Because of various network intrusions and malicious activities, effective defenses and security considerations were given high priority for finding reliable solutions [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles

Lee

Kim

et al. 2019

IEEE Access

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One of the major challenges in cybersecurity is the provision of an automated and effective cyber-threats detection technique. In this paper, we present an AI technique for cyber-threats detection, based on artificial neural networks. The proposed technique converts multitude of collected security events to individual event profiles and use a deep learning-based detection method for enhanced cyber-threat detection. For this work, we developed an AI-SIEM system based on a combination of event profiling for data preprocessing and different artificial neural network methods, including FCNN, CNN, and LSTM. The system focuses on discriminating between true positive and false positive alerts, thus helping security analysts to rapidly respond to cyber threats. All experiments in this study are performed by authors using two benchmark datasets (NSLKDD and CICIDS2017) and two datasets collected in the real world. To evaluate the performance comparison with existing methods, we conducted experiments using the five conventional machine-learning methods (SVM, k-NN, RF, NB, and DT). Consequently, the experimental results of this study ensure that our proposed methods are capable of being employed as learning-based models for network intrusion-detection, and show that although it is employed in the real world, the performance outperforms the conventional machine-learning methods.

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Data security analysis for DDoS defense of cloud based networks

Cited by 5 publications

References 22 publications

The Analysis Network Forensics Using Honeypot on Public Cloud Computing Service Network

The Analysis Network Forensics Using Honeypot on Public Cloud Computing Service Network

The Use of K-Means Algorithm on Datalog Honeynet for Profiling Ddos Attacks

Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles

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