Biblio
Ransomware is one of the most serious threats which constitute a significant challenge in the cybersecurity field. The cybercriminals use this attack to encrypts the victim's files or infect the victim's devices to demand ransom in exchange to restore access to these files and devices. The escalating threat of Ransomware to thousands of individuals and companies requires an urgent need for creating a system capable of proactively detecting and preventing ransomware. In this research, a new approach is proposed to detect and classify ransomware based on three machine learning algorithms (Random Forest, Support Vector Machines , and Näive Bayes). The features set was extracted directly from raw byte using static analysis technique of samples to improve the detection speed. To offer the best detection accuracy, CF-NCF (Class Frequency - Non-Class Frequency) has been utilized for generate features vectors. The proposed approach can differentiate between ransomware and goodware files with a detection accuracy of up to 98.33 percent.
Cybersecurity has become an emerging challenge for business information management and critical infrastructure protection in recent years. Artificial Intelligence (AI) has been widely used in different fields, but it is still relatively new in the area of Cyber-Physical Systems (CPS) security. In this paper, we provide an approach based on Machine Learning (ML) to intelligent threat recognition to enable run-time risk assessment for superior situation awareness in CPS security monitoring. With the aim of classifying malicious activity, several machine learning methods, such as k-nearest neighbours (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF), have been applied and compared using two different publicly available real-world testbeds. The results show that RF allowed for the best classification performance. When used in reference industrial applications, the approach allows security control room operators to get notified of threats only when classification confidence will be above a threshold, hence reducing the stress of security managers and effectively supporting their decisions.
Advanced persistent threats (APT’s) are stealthy threat actors with the skills to gain covert control of the computer network for an extended period of time. They are the highest cyber attack risk factor for large companies and states. A successful attack via an APT can cost millions of dollars, can disrupt civil life and has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. Attacks of APT’s are executed in several stages as pointed out in the Lockheed Martin cyber kill chain (CKC). Each of these APT stages can potentially be identified as patterns in network traffic. Using the "APT-2020" dataset, that compiles the characteristics and stages of an APT, we carried out experiments on the detection of anomalous traffic for all APT stages. We compare several artificial intelligence models, like a stacked auto encoder, a recurrent neural network and a one class state vector machine and show significant improvements on detection in the data exfiltration stage. This dataset is the first to have a data exfiltration stage included to experiment on. According to APT-2020’s authors current models have the biggest challenge specific to this stage. We introduce a method to successfully detect data exfiltration by analyzing the payload of the network traffic flow. This flow based deep packet inspection approach improves detection compared to other state of the art methods.
This article describes attacks methods, vectors and technics used by threat actors during pandemic situations in the world. Identifies common targets of threat actors and cyber-attack tactics. The article analyzes cybersecurity challenges and specifies possible solutions and improvements in cybersecurity. Defines cybersecurity controls, which should be taken against analyzed attack vectors.
In recent days, Enterprises are expanding their business efficiently through web applications which has paved the way for building good consumer relationship with its customers. The major threat faced by these enterprises is their inability to provide secure environments as the web applications are prone to severe vulnerabilities. As a result of this, many security standards and tools have been evolving to handle the vulnerabilities. Though there are many vulnerability detection tools available in the present, they do not provide sufficient information on the attack. For the long-term functioning of an organization, data along with efficient analytics on the vulnerabilities is required to enhance its reliability. The proposed model thus aims to make use of Machine Learning with Analytics to solve the problem in hand. Hence, the sequence of the attack is detected through the pattern using PAA and further the detected vulnerabilities are classified using Machine Learning technique such as SVM. Probabilistic results are provided in order to obtain numerical data sets which could be used for obtaining a report on user and application behavior. Dynamic and Reconfigurable PAA with SVM Classifier is a challenging task to analyze the vulnerabilities and impact of these vulnerabilities in heterogeneous web environment. This will enhance the former processing by analysis of the origin and the pattern of the attack in a more effective manner. Hence, the proposed system is designed to perform detection of attacks. The system works on the mitigation and prevention as part of the attack prediction.
Malware is one of the threats to information security that continues to increase. In 2014 nearly six million new malware was recorded. The highest number of malware is in Trojan Horse malware while in Adware malware is the most significantly increased malware. Security system devices such as antivirus, firewall, and IDS signature-based are considered to fail to detect malware. This happens because of the very fast spread of computer malware and the increasing number of signatures. Besides signature-based security systems it is difficult to identify new methods, viruses or worms used by attackers. One other alternative in detecting malware is to use honeypot with machine learning. Honeypot can be used as a trap for packages that are suspected while machine learning can detect malware by classifying classes. Decision Tree and Support Vector Machine (SVM) are used as classification algorithms. In this paper, we propose architectural design as a solution to detect malware. We presented the architectural proposal and explained the experimental method to be used.
Software Defined Networking (SDN) is very popular due to the benefits it provides such as scalability, flexibility, monitoring, and ease of innovation. However, it needs to be properly protected from security threats. One major attack that plagues the SDN network is the distributed denial-of-service (DDoS) attack. There are several approaches to prevent the DDoS attack in an SDN network. We have evaluated a few machine learning techniques, i.e., J48, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), to detect and block the DDoS attack in an SDN network. The evaluation process involved training and selecting the best model for the proposed network and applying it in a mitigation and prevention script to detect and mitigate attacks. The results showed that J48 performs better than the other evaluated algorithms, especially in terms of training and testing time.