The number of connected devices in the network is growing day by day, and as the number of linked devices grows, so will the number of cyberattacks. All devices connected to the Internet has become a target of cyberattacks as network attack methods have developed. As a result, the security of network data cannot be neglected. To handle the future threats in this way, we employ honeypots, which are conceptual framework traps designed to block unauthorized access to both PCs and data. Every day, a large number of people access the internet throughout the world. Honeypot, also known as Intrusion Detection Technology, is a type of security technology that screens devices to prevent unwanted activities. This article will provide an overview of cyber security as well as a discussion of machine learning, cyber threats, and honeypot system-based techniques. This review paper was the result of a lot of research, and in assessing honeypots, the researchers found that they are becoming more of a concern for experts as an important security tool that can halt or limit system attacks and provide analysts with insights into the origins and behaviours of such attacks.
Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.
Cyber security is a vital concern for companies with internet-based cloud networks. These networks are constantly vulnerable to attack, whether from inside or outside organization. Due to the ever-changing nature of the cyber world, security solutions must be updated regularly in order to keep infrastructure secure. With the use of attack detection approaches, security systems such as antivirus, firewalls, or intrusion detection systems have become more effective. However, conventional systems are unable to detect zero-day attacks or behavioral changes. These drawbacks can be overcome by setting up a honeypot. In this paper, a hybrid Honeynet model deployed in Docker (H-DOC) bait has been proposed that comprises both low interaction and high interaction honeypot to attract the malicious attacker and to analyze the behavioral patterns. This is a form of bait, designed to detect or block attacks, or to divert an attacker's attention away from the legitimate services. It focuses only on the SSH protocol, as it is widely used for remote system access and is a popular target of attacks. The proposed Hybrid H-DOC method identify ransomware activity, attack trends, and timely decision-making through the use of an effective rule and tunes the firewall. The attack detection accuracy of the proposed Hybrid H-DOC method when compared with IDH, Decepti-SCADA, AS-IDS and HDCM is 13.97%, 11.82%, 8.60% and 5.07% respectively.
With the hasty growth of the Internet technologies and the need for increased computational methods, cloud computing has become a new paradigm in the business and IT endeavors. This technology facilitates the enterprises to move their confidential data into the cloud, where their data is stored in a remote data centers and accessed via the internet. These resources are controlled by third parties who may not be trustworthy and provide adequate data security. While this technology has many benefits, the security issues also increases. Various methods have been proposed for designing a secure cloud storage system.However many of the prior works suffers from security, confidentiality, and integrity issues that limit the functionalities of the storage system. Moreover, the existing schemes suffers delay due to the lack of online verification, and increasein the communication cost as data is retrieved back to the owner for every request made by the user. Hence it remains a challenge for constructing an efficient data storage and data retrieval. In this paper, we present the development of a secure and efficient cloud storage system, "SCDS-TM" that supports data confidentiality, data integrity, and data retrieval functionalities. With the use of Elliptic Curve Cryptography and storage correctness proof, weovercame the data confidentiality and data integrity issues. In order to augment the data retrieval functionality, we exploited a keyword search method called "coordinate matching" technique of capturing the related documents based upon the search query.The performance of the proposed method is analyzed based on various factors such as communication overhead,data transfer rate, query execution time, data security level, probability ratio and precision ratio.Thorough analysis of scrutinizing the privacy and efficiency allow our proposed method to be secure, efficient and offers a higher level of security with low communication overhead.
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