A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework

Karyawan dalam perusahaan merupakan sumber daya utama yang dituntut untuk mampu memberikan pelayanan terbaik dan kinerja yang optimal. Soft skill karyawan adalah keterampilan individu karyawan yang dapat menunjang hubungan individu karyawan dengan karyawan lain, meningkatkan kinerja dan membuka prospek karirnya. Penilaian kinerja karyawan yang dilaksanakan oleh perusahaan umumnya hanya untuk penilaian prestasi kerja yakni bagaimana pekerjaan dapat dikerjakan dengan baik, mencapai target yang ditetapkan dan meraih tujuan akhir yang diinginkan ( hard skill ). Penilaian terkait kemampuan soft skill karyawan belum banyak dilakukan. Ada beberapa kriteria yang diterapkan beberapa perusahaan dalam melakukan penilaian kompetensi soft skill , tetapi kriterianya masih berbeda-beda. Penelitian ini membahas penilaian kompetensi soft skill karyawan dengan menerapkan empat kriteria. Keempat kriteria ini adalah kemampuan komunikasi, kemampuan bekerjasama, kejujuran dan kemampuan interpersonal. Analisis data menerapkan metode Analytical Hierarchical Process (AHP), yang memungkinkan perhitungan matematis dengan berbagai kriteria. Hasil penelitian menunjukan nilai rasio konsistensi 0.053 yang berarti kurang dari nilai rasio konsistensi yang digunakan dalam metode AHP yaitu 0.1, sehingga hasil perhitungan tersebut valid, dan dapat digunakan. Penelitian ini menghasilkan penilaian prioritas kompetensi soft skill yang dibutuhkan perusahaan sebagai berikut: Komunikasi 48%, Kerjasama 27%, Kejujuran 16 % dan interpersonal 10%. Hasil penelitian ini membuktikan bahwa metode AHP dapat digunakan pada penilaian kompetensi soft skill karyawan.

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Sistem Pendukung Keputusan dengan Metode AHP untuk Penilaian Kompetensi Soft Skill Karyawan

Poulter previously presented sonagram analyses of underwater sound recordings of various sea mammals, calling attention to the apparent harmonic progression of components of these signals. Other workers have questioned the presence of these harmonics in the original data and have suggested that they may instead be introduced during the analysis. In this paper we use underwater sound recordings of the male killer whale Namu, taken with equipment flat within ±2 dB to beyond 18 342 Hz, the upper limit of the analysis, to show by means of digital spectral analysis methods that a harmonic progression exists. In the signals analyzed, peaks in the estimated spectrum were observed at each integer multiple of the fundamental frequency. In view of the broad frequency response of the recording equipment and the precision of the subsequent digital analysis, we can say with confidence that these harmonics were actually present in the whaler's call. For the analysis, portions of an audio recording were converted to digital form. In the conversion, a bandpass filter was used to attenuate power below 40 Hz and above 10 kHz. Digital analysis techniques similar to those proposed by Bingham, Godfrey, and Tukey were then used. For each time span of data to be analyzed, a windowed Fourier transform was first computed, using a fast Fourier transform program. The power spectrum was next computed, as the squared modulus of the windowed transform, and a correction was made for the attenuation of high frequencies during A-to-D conversion. The autocorrelation function was estimated by computing the inverse transform of the power spectrum. A moderate amount of digital smoothing was then applied to the spectrum to reduce irregularities due to noise. The resulting smoothed spectrum is used as an estimate of the power spectral density function.

Spectral Analysis of the Call of the Male Killer Whale

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.

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Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT

Pengembalian data adalah bagian terpenting dari Digital Forensic. Bagi penyidik untuk menghasilkan bukti di pengadilan adalah hal yang sangat penting, sehingga semua jenis data dari Flash Memory atau Hard Drive yang disembunyikan, dihapus bahkan diformat harus ditemukan. Prinsip dasar dari data jika sudah pernah dicopy pada Flash Memory atau Hard Drive tidak akan pernah hilang secara permanen dan bahkan data yang hilang karena dihapus, diformat cepat ataupun sistem crash. Sehingga pengembalian data sangat memungkinkan. Pada penelitian ini tool yang digunakan adalah FTK Imager dan Autopsy, yang merupakan Tools Opensource yang dapat digunakan pada system operasi Proprietary maupun Opensource. Metode yang digunakan yaitu National Institute Of Standards Technology (NIST). NIST memiliki panduan kerja yang sangat baik dalam kebijakan dan standar untuk menjamin setiap Examiner mengikuti alur kerja yang sama, sehingga pekerjaan mereka terdokumentasikan dan hasilnya dapat di kaji ulang dan dapat dipertahankan ketika pelaporan. Hasil penelitian ini membuktikan tools FTK Imager dan Autopsy dapat mengembalikan data-data yeng telah terhapus, tersembunyi dan terformat.

http://e-prosiding.poliban.ac.id/index.php/snrt/article/download/408/354

Analisis File Carving Pada File System Dengan Metode National Institute Of Standards And Technology (NIST)

The research used watermarking techniques to obtain the image originality. The aims of the research were to identify small area in eggs properly and compared preprocessing, the methods, and the results of image processing. The study has been improved from the previous papers by combined all methods and analysis was obtained.This study was conducted by using centroid and the bounding box for determining the object and the small area of chicken eggs. The segmentation method was used to compare the original image and the watermarked image. Image processing using image data that are subject watermark to maintain the authenticity of the images used in the study will the impact in delivering the desired results. In the identification of chicken eggs using watermark image using several methods are expected to provide results as desired. Segmentation also deployed to process the Image and counted the objects. The results showed that the process of segmentation and objects counting determined that the original image and watermarked image had the same value and recognized eggs. Identification had determined percentage of 100% for all the samples.

https://beei.org/index.php/EEI/article/download/595/891

Segmentation Comparing Eggs Watermarking Image and Original Image

Distributed Denial of Service (DDoS) is a network security problem that continues to grow dynamically and has increased significantly to date. DDoS is a type of attack that is carried out by draining the available resources in the network by flooding the package with a significant intensity so that the system becomes overloaded and stops. This attack resulted in enormous losses for institutions and companies engaged in online services. Prolonged deductions and substantial recovery costs are additional losses for the company due to loss of integrity. The activities of damaging, disrupting, stealing data, and everything that is detrimental to the system owner on a computer network is an illegal act and can be imposed legally in court. Criminals can be punished based on the evidence found with the Forensics network mechanism. DDoS attack classification is based on network traffic activity using the neural network and naive Bayes methods. Based on the experiments conducted, it was found that the results of accuracy in artificial neural networks were 95.23% and naive Bayes were 99.9%. The experimental results show that the naive Bayes method is better than the neural network. The results of the experiment and analysis can be used as evidence in the trial process.

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DDoS Classification Using Neural Network and Naïve Bayes Methods for Network Forensics

Facebook Messenger menjadi media sosial yang populer kedua setelah Whatsapp di tahun 2017. Meningkatnya jumlah pengguna Facebook Messenger tentu membawa dampak positif dan negatif, salah satu efek negatifnya adalah beberapa orang yang menggunakan Facebook Messenger melakukan kejahatan digital. Jika sebuah smartphone android menjadi bukti dalam kasus pidana dan Facebook Messenger terinstall di smartphone tersebut, maka pada aplikasi ini bukti digital dapat diidentifikasi dan dapat diharapkan menjadi pilihan untuk membantu penegakan hukum dalam mengungkap kejahatan digital. Proses identifikasi berdasarkan proses forensic mobile yang berdasarkan metode NIST (National Institute of Standards Technology). NIST memiliki panduan kerja baik itu kebijakan dan standar untuk menjamin setiap examiner mengikuti alur kerja yang sama sehingga pekerjaan mereka terdokumentasikan dan hasilnya dapat di ulang (repeatable) dan dapat dipertahankan (defendable). Penelitian ini menjelaskan gambaran umum bagaimana teknik-teknik yang dapat digunakan untuk mengembalikan bukti digital berupa text,gambar, dan audio pada Facebook Mesenger yang ada di Smartphone Android.

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Analisis Bukti Digital Facebook Messenger Menggunakan Metode Nist

The growth of Android-based smartphone users to access media in communicating using Instagram social media is very fast. Activities are carried out when using Instagram social media in communicating to share information such as sending chat texts and pictures. A large number of Instagram users make this application vulnerable to abuse of Instagram such as pornography crimes from Instagram users. This case can be forensic to get digital evidence in the form of chat text and pictures from Instagram messenger is a feature of Instagram. The investigation in this study uses the National Institute of Standards and Technology (NIST) method which provides several stages of collecting, examining, analyzing, reporting while forensic tools use forensic oxygen and axiom magnets. The results of the recovery and comparison of data result using Oxygen forensics and Axiom Magnets obtained digital evidence in the form of data in the form of images and chat. The data obtained by Magnet Axiom is 100% while forensic oxygen is 84%. These data are the results of the performance of both forensic applications in obtaining digital evidence that has been deleted from the Instagram messenger.

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Forensic Tool Comparison on Instagram Digital Evidence Based on Android with The NIST Method

This Research was conducted to analyze the identification of eggs. The research processes use two tools, namely thermal imaging camera and smartphone camera. The identification process was done by using Matlab prototype tools. The image has been acquired by means of proficiency level, then analyzed and applied several methods. Image acquisition results of thermal imaging camera are processed using morphological dilation and do the complement in black and white (BW). While the digital image uses the merger method of morphological dilation and opening, and it doesn't need to be complemented. Labeling process is done, and the process of determining centroid and bounding box. The process has been done and it can be applied for identifying of chicken eggs with the accuracy rate of 100%. There are different methods of both images is obtained area (pixels) which is equivalent to the difference is very small as 6 x 10 -3 .

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Anton Yudhana

Papers

Segmentation Comparing Eggs Watermarking Image and Original Image

DDoS Classification Using Neural Network and Naïve Bayes Methods for Network Forensics

Analisis Bukti Digital Facebook Messenger Menggunakan Metode Nist

Forensic Tool Comparison on Instagram Digital Evidence Based on Android with The NIST Method

Identity Analysis of Egg Based on Digital and Thermal Imaging: Image Processing and Counting Object Concept