Summary Online Social Networks are an integral part of the modern society. Social networking sites are at the top rank in terms of usage out of the billions of websites hosted in the internet. The way in which criminal activities are interleaved to these networks are beyond our imagination. Securing these networks from cyber criminals and malicious attackers are a major concern nowadays. There are various security threats happening to the Online Social Network accounts, and account compromise is one among it. Account compromise can cause collateral damage to the user and to a mass community. Misusing the personal information of the users and exploiting their trust relationships within the network lead to serious problems. These compromised accounts are becoming a safe zone for criminals to launch attacks. The threats caused by these compromised accounts include spreading malware, spy advertisements, and espionage. In most cases, it becomes too late to detect that an account is compromised. Detection of these accounts is important in order to reduce the impact of damage. The detection of compromised Online Social Network account is the process of confirming whether the account used by an individual or by an organization has lost its control or not. This survey reviews the various methods that are found in the literature toward detecting compromised Online Social Network Accounts.

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With the increasing popularity of the internet of things (IoT), fog computing has emerged as a unique cutting-edge approach along with cloud computing. This study proposes an approach for data integrity verification in fog computing that does not require metadata stored on the user side and can handle big data efficiently. In the proposed work, fuzzy clustering is used to cluster IoT data; dynamic keys are used to encrypt the clusters; and dynamic permutation is used to distribute encrypted clusters among fog nodes. During the process of data retrieval, fuzzy clustering and message authentication code (MAC) are used to verify the data integrity. Fuzzy clustering and dynamic primitives make the proposed approach more secure. The security analysis indicates that the proposed approach is resilient to various security attacks. Moreover, the performance analysis shows that the computation time of the proposed work is 50 times better than the existing tag regeneration scheme.