A new approach to the reconstruction of contour lines extracted from topographic maps

It is known that after segmentation and morphological operations on topographic maps, gaps occur in contour lines. It is also well known that filling these gaps and reconstruction of contour lines with high accuracy is not an easy problem. In this paper, a nontrivial semi-automatic approach to solve this problem is proposed. The main idea of the proposed approach is based on local and geometric properties such as (1) parabolic and opposite directions, (2) the differences of y-ordinate of end points, (3) changing the directions of x-axis and y-ordinate to the nearest clockwise direction and (4) avoiding the use of the second end point of a small piece of any contour line in the same mask if its other end point is used. The proposed approach was implemented on the base of many topographic maps with different resolutions and complexity. The obtained results show that the proposed approach increases accuracy and performance. (c) 2012 Elsevier Inc. All rights reserved

Siber Güvenlik ve Savunma

Kötü amaçlı yazılımlar dinamik olup zaman zaman saldırı biçimini ve hedefini değiştirerek sürekli gelişme gösteren yazılımlardır. Bu yazılımları tespit etmek ve bulaştıkları sistemlerle olan etkileşimlerini anlamak için analiz edilmeleri gerekmektedir. Kötü amaçlı yazılım analizi bu yazılımların nasıl çalıştığını anlamak, tespit etmek ve yayılmasını engellemek amacıyla yapılan çalışmaları kapsamaktadır. Bu bölümde bu yazılımların nasıl analiz ve tespit edileceğiyle ilgili güncel bilgiler bulunmaktadır. Her ne kadar yeni teknik ve yöntemler kullanılsa da bütün kötü amaçlı yazılımları %100 başarı oranıyla analiz ve tespit etmek mümkün görünmemektedir

Investigation of Possibilities to Detect Malware Using Existing Tools

Malware stands for malicious software, which is installed on a computer system without the knowledge of the system owner. It performs malicious actions such as stealing confidential information and allowing remote code execution, and it can cause denial of service. Recently, malware creators started to publish new malware, which can bypass anti-malware software, intrusion detection systems (IDS) and sandbox execution. Due to this evasion, the protection of computer networks and computerized systems against these programs has become one of the biggest challenges in the information security realm. This paper proposes a methodology to learn the well-known malware analysis and detection tools, to implement these tools on well-known malware and benign programs and to compare the obtained results. Further, this research will suggest to users how to analyze and detect existing and unknown malware. In a test case, 100 malware and 100 benign program samples were collected from different sources and analyzed under different versions of Windows machines. The test results indicated that it is almost impossible to detect malware by only using one tool. Using static and dynamic analysis tools together increased accuracy and the detection rate. The test results also showed that dynami

Mitigating Cyber Security Attacks by being Aware of Vulnerabilities and Bugs

Because the Internet makes human lives easier, many devices are connected to the Internet daily. The private data of individuals and large companies, including health-related data, user bank accounts, and military and manufacturing data, are increasingly accessible via the Internet. Because almost all data is now accessible through the Internet, protecting these valuable assets has become a major concern. The goal of cyber security is to protect such assets from unauthorized use. Attackers use automated tools and manual techniques to penetrate systems by exploiting existing vulnerabilities and software bugs. To provide good enough security; attack methodologies, vulnerability concepts and defence strategies should be thoroughly investigated. The main purpose of this study is to show that the patches released for existing vulnerabilities at the operating system (OS) level and in software programs does not completely prevent cyber-attack. Instead, producing specific patches for each company and fixing software bugs by being aware of the software running on each specific system can provide a better result. This study also demonstrates that firewalls, antivirus software, Windows Defender and other prevention techniques are not sufficient to prevent attacks. Instead, this study examines different aspects of penetration testing to determine vulnerable applications and hosts using the Nmap and Metasploit frameworks. For a test case, a virtualized system is used that includes different versions of Windows and Linux OS

A Hybrid Method to the Reconstruction of Contour Lines from Scanned Topographic Maps

This paper addresses with the problem of contour line reconstruction extracted from scanned topographic maps since contour lines play a significant role on construction of Digital Evaluation Models (DEMs) and 3D simulations in serious fields. In this way, a semi-automatic hybrid method based on simple geometrical properties is proposed. The proposed hybrid method is designed by combining the two previously presented methods such as advanced and highly advanced methods in the literature. The contribution of the proposed hybrid method is to increase the accuracy of the advanced method and is to improve the run-time of highly advanced method in the process of reconstruction. The effectiveness of the algorithm is demonstrated by comparing it with the five popular methods in the literature. The implementation results show that the proposed hybrid method outperforms the others and can be efficiently employed in reconstruction process of contour lines

Using a Subtractive Center Behavioral Model to Detect Malware

In recent years, malware has evolved by using different obfuscation techniques; due to this evolution, the detection of malware has become problematic. Signature-based and traditional behavior-based malware detectors cannot effectively detect this new generation of malware. This paper proposes a subtractive center behavior model (SCBM) to create a malware dataset that captures semantically related behaviors from sample programs. In the proposed model, system paths, where malware behaviors are performed, and malware behaviors themselves are taken into consideration. This way malicious behavior patterns are differentiated from benign behavior patterns. Features that could not exceed the specified score are removed from the dataset. The datasets created using the proposed model contain far fewer features than the datasets created by n-gram and other models that have been used in other studies. The proposed model can handle both known and unknown malware, and the obtained detection rate and accuracy of the proposed model are higher than those of the known models. To show the effectiveness of the proposed model, 2 datasets with score and without score are created by using SCBM. In total, 6700 malware samples and 3000 benign samples are tested. The results are compared with those derived from n-gram and models from other studies in the literature. The test results show that, by combining the proposed model with an appropriate machine learning algorithm, the detection rate, false positive rate, and accuracy are measured as 99.9%, 0.2%, and 99.8%, respectively

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

The final stage of the production process in the industry is quality control. Quality control answers the question of is there a defect on the surface of the products. Frequently the quality control is performed manually. The disadvantages of manual quality control are high error rate (low accuracy), low product rate (low performance) and high expense rate (high cost). The solution is automatic quality control using machine vision systems. These systems classify the products and segment the defects on their surfaces by processing the images taken by cameras during the production process in real-time. Some products like military cartridge cases have metallic, cylindrical, non-uniform texture and highly reflective surface. So, the quality of images is very important. Another factor that affects the accuracy is the non-uniform texture of the product surface. Distinguishing the product non-uniform texture from defect texture is a challenging problem. In previous works, this problem has been tried to be solved with image processing and deep learning techniques and the accuracy of 97% and 96% have been obtained, appropriately. According to NATO standards, the accuracy of the classification of the military cartridge cases should be above 99%. In this work, the methodology for classification of the military cartridge cases and segmentation of the defects on their surfaces with non-uniform texture is proposed to increase the accuracy. In scope of the proposed methodology the datasets with non-defective, defective, and labeled/masked image classes of the cartridge cases were created, the deep learning models to classify the military cartridge cases and segment the defects on their surfaces were proposed, implemented, and obtained results were evaluated using the metrics such as Accuracy, Precision, Recall, F1-Score, Jaccard Index (JI) and Mean Intersection over Union (mIoU). Obtained results showed that the proposed methodology increased the accuracy of classification to 100% with the DenseNet169 model and the F1-Score of segmentation to 92.1% with Improved U-Net and ResUnet models