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Android malware detection is an important research topic in the security area. There are a variety of existing malware detection models based on static and dynamic malware analysis. However, most of these models are not very successful when it comes to evasive malware detection. In this study, we aimed to create a malware detection model based on a natural language model called skip-gram to detect evasive malware with the highest accuracy rate possible. In order to train and test our proposed model, we used an up-to-date malware dataset called Argus Android Malware Dataset (AMD) since the AMD contains various evasive malware families and detailed information about them. Meanwhile, for the benign samples, we used Comodo Android Benign Dataset. Our proposed model starts with extracting skip-gram-based features from instruction sequences of Android applications. Then it applies several machine learning algorithms to classify samples as benign or malware. We tested our proposed model with two different scenarios. In the first scenario, the random forest-based classifier performed with 95.64% detection accuracy on the entire dataset and 95% detection accuracy against evasive only samples. In the second scenario, we created a test dataset that contained zero-day malware samples only. For the training set, we did not use any sample that belongs to the malware families in the test set. The random forest-based model performed with 37.36% accuracy rate against zero-day malware. In addition, we compared our proposed model’s malware detection performance against several commercial antimalware applications using VirusTotal API. Our model outperformed 7 out of 10 antimalware applications and tied with one of them on the same test scenario.

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<p>Distributed Denial of Service (DDoS) attacks have plagued the Internet for decades. Despite the ever-increasing investments into mitigation solution developments, DDoS attacks are also growing with ever-increasing frequency and magnitude. To identify the root cause of the above-observed trend, in this paper, we perform a systematic analysis of volumetric DDoS detection and mitigation efforts over the last four decades. To that end, we introduce a novel approach for systematizing comparisons for DDoS research resulting in the comprehensive examination of the DDoS literature spanning more than 24,000 papers, articles, and RFCs over 30+ years. Our analysis illustrates common design patterns across seemingly disparate solutions, and reveals insights into which aspects of DDoS solutions correlate with deployment traction and success. Furthermore, we discuss economic incentives and the lack of harmony between synergistic but independent approaches for detection and mitigation. As expected, defenses with a clear cost/benefit rationale are more prevalent than ones that require extensive infrastructure changes. Finally, we discuss the lessons learned which we hope can shed light on future directions that can potentially allow us to turn the tide on the war against DDoS.</p>

No abstract

<p>Distributed Denial of Service (DDoS) attacks have plagued the Internet for decades. Despite the ever-increasing investments into mitigation solution developments, DDoS attacks are also growing with ever-increasing frequency and magnitude. To identify the root cause of the above-observed trend, in this paper, we perform a systematic analysis of volumetric DDoS detection and mitigation efforts over the last four decades. To that end, we introduce a novel approach for systematizing comparisons for DDoS research resulting in the comprehensive examination of the DDoS literature spanning more than 24,000 papers, articles, and RFCs over 30+ years. Our analysis illustrates common design patterns across seemingly disparate solutions, and reveals insights into which aspects of DDoS solutions correlate with deployment traction and success. Furthermore, we discuss economic incentives and the lack of harmony between synergistic but independent approaches for detection and mitigation. As expected, defenses with a clear cost/benefit rationale are more prevalent than ones that require extensive infrastructure changes. Finally, we discuss the lessons learned which we hope can shed light on future directions that can potentially allow us to turn the tide on the war against DDoS.</p>

Understanding the cyberspace and awareness of its effects impacts the lives of all individuals. Thus, the knowledge of cybersecurity in both organizations and private operations is essential. Research on various aspects of cybersecurity is crucial for achieving adequate levels of cybersecurity. The content of this scientific monography provides answers to various topical questions from the organizational, individual, sociological, technical and legal aspects of security in the cyberspace. The papers in the monography combine the findings of researchers from different subareas of cybersecurity, show the effects of adequate levels of cybersecurity on the operations of organizations and individuals, and present the latest methods to defend against threats in the cyberspace from technical, organizational and security aspects.