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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Tekno : Jurnal Teknologi Elektro dan Kejuruan Annual Research Seminar JURNAL MEDIA INFORMATIKA BUDIDARMA KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Tekno Kompak Jurnal Mantik Mulia International Journal in Science and Technical International Journal of Engineering, Science and Information Technology Jurnal Rekayasa Elektro Sriwijaya Jurnal Indonesia Sosial Teknologi Jurnal Pendidikan Indonesia (Japendi) Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Iwan Pahendra
Teknik Elektro, Universitas Sriwijaya, Palembang, Indonesia
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Shellcode classification analysis with binary classification-based machine learning Semendawai, Jaka Naufal; Stiawan, Deris; Anto Saputra, Iwan Pahendra; Shenify, Mohamed; Budiarto, Rahmat
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp923-932

Abstract

The internet enables people to connect through their devices. While it offers numerous benefits, it also has adverse effects. A prime example is malware, which can damage or even destroy a device or harm its users, highlighting the importance of cyber security. Various methods can be employed to prevent or detect malware, including machine learning techniques. The experiments are based on training and testing data from the UNSW_NB15 dataset. K-nearest neighbor (KNN), decision tree, and Naïve Bayes classifiers determine whether a record in the test data represents a Shellcode attack or a non-Shellcode attack. The KNN, decision tree, and Naïve Bayes classifiers reached accuracy rates of 96.26%, 97.19%, and 57.57%, respectively. This study's findings aim to offer valuable insights into the application of machine learning to detect or classify malware and other forms of cyberattacks.
L1-Minimization Sinyal Pergerakan Doppler Objek Untuk Pemulihan Sinyal Pada Radio Detection And Ranging Array Puspa Kurniasari; Iwan Pahendra Anto Saputra; Melia Sari
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 18 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v18n2.2604

Abstract

Sinyal yang ditransmisikan dari suatu pemancar radio detection and ranging adalah berupa gelombang elektromagnetik yang diarahkan ke arah objek dan memiliki cakupan wilayah sebaran gelombang untuk mendeteksi objek melalui sinyal pantulan dari objek ke bagian penerima radio detection and ranging. Media wireless yang digunakan untuk transmisi sinyal ke objek dipengaruhi gangguan dari derau sehingga sinyal terima pantulan objek tidak maksimal dalam penerimaan di radio detection and ranging receiver. Pergerakan objek dan perpindahan posisi objek menghasilkan respon atau tanggapan dari radio detection and ranging. Pada penelitian ini, sinyal pergerakan doppler objek dioptimasi melalui pemulihan L1-minimization dan pemulihan sinyal terima dari perangkat radio detection and ranging array berhasil dilakukan. Pengujian perangkat array juga berhasil dilakukan terhadap objek di jarak satu meter, dua meter dan tiga meter dengan masing-masing pergerakan objek ke arah kanan dan ke kiri dari posisi awal objek terhadap perangkat array sejauh 50 cm dan 100 cm. Array pada perangkat transceiver radio detection and ranging diletakkan sejajar menghadap objek. Hasil pengukuran kinerja pemulihan sinyal menggunakan L1-minimization dan estimasi sinyal menghasilkan amplitudo tegangan sinyal hasil paling baik yaitu 359725.8655 V pada jarak 50 cm pergerakan objek ke arah kanan perangkat array sedangkan mean square error yang dihasilkan 1.653 % pada rasio signal to noise 226773.8723 dB serta 290737.6723 dB sebagai hasil dari rasio peak signal to noise.
Shellcode Classification with Machine Learning Based on Binary Classification Jaka Naufal Semendawai; Deris Stiawan; Iwan Pahendra
Jurnal Indonesia Sosial Teknologi Vol. 6 No. 2 (2025): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v6i2.3233

Abstract

The Internet can link one person to another using their respective devices. The internet itself has both positive and negative impacts. One example of the internet's negative impact is malware that can disrupt or even kill a device or its users; that is why cyber security is required. Many methods can be used to prevent or detect malware. One of the efforts is to use machine learning techniques. The training and testing dataset for the experiments is derived from the UNSW_NB15 dataset. K-Nearest Neighbour (KNN), Decision Tree, and Naïve Bayes classifiers are implemented to classify whether a record in the testing data is Shellcode or non-Shellcode attack. The KNN, Decision Tree, and Naïve Bayes classifiers achieve accuracy levels of 96.82%, 97.08%, and 63.43%, respectively. The results of this research are expected to provide insight into the use of machine learning in detecting or classifying malware or other types of cyber attacks.
Klasifikasi Shellcode Dengan Machine Learning Berbasis Klasifikasi Biner Jaka Naufal Semendawai; Deris Stiawan; Iwan Pahendra
Jurnal Pendidikan Indonesia Vol. 5 No. 11 (2024): Jurnal Pendidikan Indonesia (Japendi)
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v5i11.6291

Abstract

Internet dapat menghubungkan satu orang dengan orang lain dengan menggunakan perangkat masing-masing. Internet sendiri memiliki dampak positif dan negatif. Salah satu contoh dampak negatif dari internet adalah adanya malware yang dapat mengganggu atau bahkan merusak perangkat atau penggunanya; itulah mengapa keamanan siber diperlukan. Banyak cara yang dapat dilakukan untuk mencegah atau mendeteksi malware. Salah satunya adalah dengan menggunakan teknik machine learning. Dataset pelatihan dan pengujian untuk eksperimen ini berasal dari dataset UNSW_NB15. K-Nearest Neighbour (KNN), Decision Tree, dan Naïve Bayes diimplementasikan untuk mengklasifikasikan apakah sebuah record pada data testing merupakan serangan Shellcode atau non-Shellcode. Classifier KNN, Decision Tree, dan Naïve Bayes mencapai tingkat akurasi masing-masing sebesar 96.82%, 97.08%, dan 63.43%. Hasil dari penelitian ini diharapkan dapat memberikan wawasan mengenai penggunaan machine learning dalam mendeteksi atau mengklasifikasikan malwares atau jenis serangan siber lainnya
Co-Authors Abdul Haris Dalimunthe Abdul Haris Dalimunthe Abdul Haris Dalimunthe Ahmad Firman Aprimivi Manda Deris Stiawan Deris Stiawan Desi Windi Sari Desi Windisari Dian Palupi Rini Dian Palupi Rini Ermatita Ermatita - Ermatita Ermatita Ermatita Ermatita Eva Damila Eva Darnila Eva Darnila eva darnila, eva darnila Fajriana, Fajriana Fajriana, Fajriana Fuadi, Wahyu Jaka Naufal Semendawai Jaka Naufal Semendawai Jaka Naufal Semendawai Khairunisa Khalid Mawardi Kurniasari, Puspa Maryana Maryana Mauliza Mauliza Mauliza Mauliza, Mauliza Melia Sari Melia Sari, Melia Miftahul Falah Miftahul Falah Mohamed Shenify Muhammad Fakhri Muhammad Sadli Muhammad Sadli Muhammad Sadli, Muhammad Mutammimul Ula Nadia Thereza Nadia Thereza Nadia Thereza Nadia Thereza Puspa Kurniasari Puspa Kurniasari Rahmat Budiarto Richki Hardi Sari, Melia Semendawai, Jaka Naufal Sinambela Marzuki Wahyu Fuadi Wahyu Fuadi Zaenal Husin