Journal of Cybersecurity and Information Management

The Internet of Things (IoT) has transformed the way we live and work, with billions of interconnected devices continuously exchanging data. However, the increasing adoption of IoT devices has also made them an attractive target for cybercriminals. Botnets, a network of compromised devices that can be remotely controlled by attackers, are one of the most significant threats to IoT networks. Traditional security solutions are insufficient to combat this threat, as they often rely on signature-based detection methods that can be easily bypassed by attackers. This work proposes an applied deep learning-based approach to secure IoT networks against botnet attacks, based on residual learning architecture that combine convolutional neural network to analyze device behavior and identify abnormal activity patterns that may indicate botnet infection. Our approach is evaluated on real-world BotNet dataset and achieved a high detection rate of botnet activity, outperforming traditional detection methods. The empirical findings show that ours can be used as a tool for developing more advanced and adaptive security solutions to safeguard the IoT galaxy.

  We can bear in mind that each of us has plagiarized a text without realizing that it was plagiarism, Plagiarism can happen in Articles, Papers, Researches, literature, music, software, scientific, newspapers, websites, Master and PHD Thesis and many other fields, So plagiarism has become serious major problem to teachers, researchers and publishers, There are divergent opinions about how to define plagiarism and what makes plagiarism serious. So, the detecting plagiarism is very important, so in this survey we explicate the concept of "plagiarism" and provide an overview of different plagiarism software and tools to solve the plagiarism problem, and will discuss the plagiarism process, types and detection methodologies. We can define that plagiarism is the brief and the description of this sentence "someone used someone else’s mental product (such as its texts, ideas, or privacy). We suggest that what makes plagiarism so reprehensible is that it distorts scientific credit. In addition, intentional plagiarism indicates dishonesty. Moreover, there are a number of possible negative consequences of plagiarism. So we just create a framework for external plagiarism detection in which a some NLP processes are applied to process a set of suspicious and original documents, we have classified the different plagiarism detection techniques based on Lexical, Semantic, Syntactic and grammar analysis algorithms, And all of these algorithms precedes it NLP processing.

In recent years, it has been observed that disclosure of information leads to the risk. Without restrict the accessibility of information providing security is difficult. So, there is a demand of time to fill the gap between security and accessibility of information. In fact, security tools should be usable for improving the security as well as the accessibility of information. Though security and accessibility are not related directly, but some of their factors indirectly affect each other. Attributes play an important role in connecting the gap among security and accessibility. In this paper, finds the main attributes of security and accessibility that impact directly and indirectly each other such as confidentiality, integrity and availability and severity. The significance of every attribute in terms of their weight is important for their effect on the overall security during the big data security life cycle process. To calculate proposed work, researchers used the Fuzzy Analytic Hierarchy Process (Fuzzy AHP).

As useful as it gets to connect devices to the internet to make life easier and more comfortable, it also opens the gates to various cyber threats. The connection of Smart Home devices to the internet makes them vulnerable to malicious hackers that infiltrate the system. Hackers can penetrate these systems and have full control over devices. This can lead to denial of service, data leakage, invasion of privacy, etc. Thus security is a major aspect of Smart home devices. However, many companies manufacturing these Smart Home devices have little to no security protocols in their devices. In the process of making the IoT devices cheaper, various cost-cutting is done on the security protocols in IoT devices. In some way, many manufactures of the devices don’t even consider this as a factor to build upon. This leaves the devices vulnerable to attacks. Various authorities have worked upon to standardize the security aspects for the IoT and listed out guidelines for manufactures to follow, but many fail to abide by them. This paper introduces and talks about the various threats, various Security threats to Smart Home devices. It takes a deep dive into the solutions for the discussed threats. It also discusses their prevention. Lastly, it discusses various preventive measures and good practices to be incorporated to protect devices from any future attacks.