Computer system security is a factor that needs to be considered in the era of industrial revolution 4.0, namely by preventing various threats to the system, as well as being able to detect and repair any damage that occurs to the computer system. DDoS attacks are a threat to the company at this time because this attack is carried out by making very large requests for a site or website server so that the system becomes stuck and cannot function at all. DDoS attacks in Indonesia and developed countries always increase every year to 6% from only 3%. To minimize the attack, we conducted a study using Machine Learning techniques. The dataset is obtained from the results of DDoS attacks that have been collected by the researchers. From the datasets there is a training and testing of data using five techniques classification: Neural Network, Naïve Bayes and Random Forest, KNN, and Support Vector Machine (SVM), datasets processed have different percentages, with the aim of facilitating in classifying. From this study it can be concluded that from the five classification techniques used, the Forest random classification technique achieved the highest level of accuracy (98.70%) with a Weighted Avg 98.4%. This means that the technique can detect DDoS attacks accurately on the application that will be developed.

Images (typically JPEG) are used as evidence against cyber perpetrators. Typically the file is carved using standard patterns. Many concentrate on carving JPEG files and overlook the important of thumbnail in assisting forensic investigation. However, a new unique pattern is used to detect thumbnail/s and embedded JPEG file. This paper is to introduce a tool call PattrecCarv to recognize thumbnail/s or embedded JPEG files using unique hex patterns (UHP). A tool called PattrecCarv is developed to automatically carve thumbnail/s and embedded JPEG files using DFRWS 2006 and DFRWS 2007 datasets. The tool successfully recovers 11.5% more thumbnails and embedded JPEG files than PredClus

No abstract

Many digital image watermarking schemes have been developed to embed copyright information into an image. However, an attacker may reuse parts of a watermarked image by cropping out unwanted parts. Several techniques have been designed to overcome this attack but due to their limited redundancy approach, some section of the images can still be retrieved without detectable watermark. In this paper, a new watermarking scheme that is robust against severe cropping using Sudoku is proposed. It is based on Sudoku's permutation property that allows evenly distributed copies of watermark pieces in all parts of the cover image. A valid Sudoku solution is used during the embedding as well as during the detection of the watermark. Using classic 9 x 9 Sudoku, the scheme demonstrated robustness of up to 94% of random cropping.

Digital Forensics is a platform that helps in assisting investigation carried out in computer crimes through the recovery of material that is found in digital devices. Material is recovered through a method known as file carving that helps to recover data from storage media. Moreover, it also retrieves the extract hidden, overwritten or deleted files. File carving enables the file recovery without knowledge about contextual information such as file system metadata. Due to recent expansion in the research of information technology, file carving has become an essential technique for general data recovery and digital forensics investigations. Therefore, considering the important role played by file carving the present study conducted a survey of the previous literature to find out various data carving techniques. Results, from the previous literature helped to provide several algorithms for image construction which are based on graph theoretic techniques. Furthermore, the present study classifies the file carving techniques for JPEG file into two categories namely basic carving technique and advanced carving technique. The paper concludes by providing limitations in the present study which could be helpful for future investigations associated with the fragmentation problem of data files.