Secure and reliable exchange of information between devices is crucial for any network in the current digital world. This information is maintained on storage devices, routing devices, and communication over the cloud. Cryptographic techniques are used to ensure the secure transmission of data, ensuring the user’s privacy by storing and transmitting data in a particular format. Using encryption, only the intended user possessing the key can access the information. During data or essential transmission, the channel should be secured by using robust encryption techniques. Homomorphic Encryption (HE) techniques have been used in the past for this purpose. However, one of the flaws of the conventional HE is seen either in its slow transmission or fast key decryption. Thus, this paper proposes an optimized Homomorphic Encryption Chinese Remainder Theorem with a Rivest-Shamir-Adleman (HE-CRT-RSA) algorithm to overcome this challenge. The proposed Technique, HE-CRT-RSA, utilizes multiple keys for efficient communication and security. In addition, the performance of the HE-CRT-RSA algorithm was evaluated in comparison with the classical RSA algorithm. The result of the proposed algorithm shows performance improvement with reduced decryption time. It is observed that the proposed HE-CRT-RSA is 3–4% faster than the classical Rivest-Shamir-Adleman (RSA). The result also suggests that HE-CRT-RSA effectively enhances security issues of the cloud and helps to decrease the involvement of intruders or any third party during communication or inside the data/server centers.

Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model's performance. Next, we use various Support Vector Machine (SVM) types to detect intrusion and the Adaptive Neuro-Fuzzy System (ANFIS) to categorize probe, U2R, R2U, and DDOS attacks. The validation of the proposed method is calculated through Fine Gaussian SVM (FGSVM), which is 99.3% for the binary class. Mean Square Error (MSE) is reported as 0.084964 for training data, 0.0855203 for testing, and 0.084964 to validate multiclass categorization.

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During the Covid-19 Pandemic, the usage of social media networks increased exponentially. People engage in education, business, shopping, and other social activities (i.e., Twitter, Facebook, WhatsApp, Instagram, YouTube). As social networking expands rapidly, its positive and negative impacts affect human health. All this leads to social crimes and illegal activities like phishing, hacking, ransomware, password attacks, spyware, blackmailing, Middle-man-attack. This research extensively discusses the social networking threats, challenges, online surveys, and future effects. We conduct an online survey using the google forms platform to collect the responses of social networking sites (SNS) users within Pakistan to show how SNS affects health positively and negatively. According to the collected response, we analyzed that 50% of the users use SNS for education purposes, 17.5% use it for shopping purposes, 58.2% use it for entertainment, 37.1% use it for communication, and 9.8% use it for other purposes. According to the response, the excessive use of SNS affects the health that 9.8% users face the physical threat, 42.8% user faces mental health issues due to excessive or inappropriate use of SN, and 50.5% users feel moral threat using Social sites. Finally, we conclude our paper by discussing the open challenges, conclusions, and future directions.

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