Titlebook: Handbook of Big Data Analytics and Forensics; Kim-Kwang Raymond Choo,Ali Dehghantanha Book 2022 Springer Nature Switzerland AG 2022 cyber

断言

Detection of Enumeration Attacks in Cloud Environments Using Infrastructure Log Data,ignificant number of enterprises are migrating to cloud-based environments to save costs and resources. This indicates that traditional approaches to confront cyber threats are not effective in the cloud environment. Considering the vast size and distributed nature of cloud computing systems, deep l

极肥胖

Cyber Threat Attribution with Multi-View Heuristic Analysis,. The APT groups are the nation-state actors or well-resourced groups that target to compromise and exploit individuals and public or private organizations. If the source of the malware can be identified at an early stage, then it will significantly help the cybersecurity specialists to know what th

平常

Thymus

Repetitions

Evaluating Performance of Scalable Fair Clustering Machine Learning Techniques in Detecting Cyber Ain the machine learning techniques. Over the years, many fair machine learning algorithms have been established to reduce the discrimination factor in machine learning. The fair variants of machine learning techniques such as fair clustering models provide a solution to the biased data analysis prob

ARBOR

Fuzzy Bayesian Learning for Cyber Threat Hunting in Industrial Control Systems,usly seen data. This is most effectively accomplished by machine learning algorithms which are designed to detect abnormal activity, because a system under attack is likely to exhibit anomalous behavior. Due to the fact that anomalous behavior is not guaranteed to be caused by an attacker, false pos

GRE

Cyber-Attack Detection in Cyber-Physical Systems Using Supervised Machine Learning,on of the computer with traditional physical infrastructure can improve the efficiency of such facility-based systems. However, it increases the scope of attack from physical security to a cybersecurity perspective. Thus, it becomes critical for authorities of such systems to be able to identify the

MILL

Evaluation of Scalable Fair Clustering Machine Learning Methods for Threat Hunting in Cyber-Physica used, unsupervised machine learning technique to detect malware from behavior data of control systems. Clustering algorithms can be susceptible to amplifying biases that may be present in the input datasets. Recent works in fair clustering attempt to solve this problem by making them balanced with

Haphazard

SUGAR

Evaluation of Machine Learning Algorithms on Internet of Things (IoT) Malware Opcodes,th all technological devices, it is highly prone to malicious attacks such as malware. Several methods have been developed to mitigate these attacks. One of the methods is the use of opcodes to classify malware. These opcodes are generated from disassembled malware programs. Different supervised mac