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The Internet of Things (IoT) ushers in a new era of communication that depends on a broad range of things and many types of communication technologies to share information. This new age of communication will be characterised by the following characteristics: Because all of the IoT's objects are connected to one another and because they function in environments that are not protected, it poses a significantly greater number of issues, constraints, and challenges than do traditional computing systems. This is due to the fact that traditional computing systems do not have as many interconnected components. Because of this, it is imperative that security be prioritised in a new approach, which is not something that is currently present in conventional computer systems. The Wireless Sensor Network, often known as WSN, and the Mobile Ad hoc Network are two technologies that play significant roles in the process of building an Internet of Things system. These technologies are used in a wide variety of activities, including sensing, environmental monitoring, data collecting, heterogeneous communication techniques, and data processing, amongst others. Because it incorporates characteristics of both MANET and WSN, IoT is susceptible to the same kinds of security issues that affect those other networks. An assault known as a Delegate Entity Attack (DEA) is a subclass of an attack known as a Denial of Service (DoS). The attacker sends an unacceptable number of control packets that have the appearance of being authentic. DoS assaults may take many different forms, and one of those kinds is an SD attack. Because of this, it is far more difficult to recognise this form of attack than a simple one that depletes the battery's capacity. One of the other key challenges that arise in a network during an SD attack is that there is the need to enhance energy management and prolong the lifespan of IoT nodes. This is one of the other significant issues that arise in a network when an SD attack is occurs. It is recommended that you make use of a Random Number Generator with Hierarchical Intrusion Detection System, abbreviated as RNGHID for short. The ecosystem of the Internet of Things is likely to be segmented into a great number of separate sectors and clusters. The HIPS system has been partitioned into two entities, which are referred to as the Delegate Entity (DE) and the Pivotal Entity, in order to identify any nodes in the network that are behaving in an abnormal manner. These entities are known, respectively, as the Delegate Entity and the Pivotal Entity (PE). Once the anomalies have been identified, it will be possible to pinpoint the area of the SD attack torture and the damaging activities that have been taken place. A warning message, generated by the Malicious Node Alert System (MNAS), is broadcast across the network in order to inform the other nodes that the network is under attack. This message classifies the various sorts of attacks based on the results of an algorithm that employs machine learning. The proposed protocol displays various desired properties, such as the capacity to conduct indivisible authentication, rapid authentication, and minimum overhead in both transmission and storage. These are only a few of the desirable attributes. [ABSTRACT FROM AUTHOR]

Distributed denial of service (DDoS) attacks are the most common means of cyberattacks against infrastructure, and detection is the first step in combating them. The current DDoS detection mainly uses the improvement or fusion of machine learning and deep learning methods to improve classification performance. However, most classifiers are trained with statistical flow features as input, ignoring topological connection changes. This one-sidedness affects the detection accuracy and cannot provide a basis for the distribution of attack sources for defense deployment. In this study, we propose a topological and flow feature-based deep learning method (GLD-Net), which simultaneously extracts flow and topological features from time-series flow data and exploits graph attention network (GAT) to mine correlations between non-Euclidean features to fuse flow and topological features. The long short-term memory (LSTM) network connected behind GAT obtains the node neighborhood relationship, and the fully connected layer is utilized to achieve feature dimension reduction and traffic type mapping. Experiments on the NSL-KDD2009 and CIC-IDS2017 datasets show that the detection accuracy of the GLD-Net method for two classifications (normal and DDoS flow) and three classifications (normal, fast DDoS flow, and slow DDoS flow) reaches 0.993 and 0.942, respectively. Compared with the existing DDoS attack detection methods, its average improvement is 0.11 and 0.081, respectively. In addition, the correlation coefficient between the detection accuracy of attack flow and the four source distribution indicators ranges from 0.7 to 0.83, which lays a foundation for the inference of attack source distribution. Notably, we are the first to fuse topology and flow features and achieve high-performance DDoS attack intrusion detection through graph-style neural networks. This study has important implications for related research and development of network security systems in other fields. [ABSTRACT FROM AUTHOR]

Cloud computing security has been a critical issue with its increase in demand. One of the most challenging problems in cloud computing is detecting distributed denial-of-service (DDoS) attacks. The attack detection framework for the DDoS attack is tricky because of its nonlinear nature of interruption activities, atypical system traffic behaviour, and many features in the problem space. As a result, creating defensive solutions against these attacks is critical for mainstream cloud computing adoption. In this novel research, by using performance parameters, perplexed-based classifiers with and without feature selection will be compared with the existing machine learning algorithms such as naïve Bayes and random forest to prove the efficacy of the perplexed-based classification algorithm. Comparing the performance parameters like accuracy, sensitivity, and specificity, the proposed algorithm has an accuracy of 99%, which is higher than the existing algorithms, proving that the proposed algorithm is highly efficient in detecting the DDoS attacks in cloud computing systems. To extend our research in the area of nature-inspired computing, we compared our perplexed Bayes classifier feature selection with nature-inspired feature selection like genetic algorithm (GA) and particle swarm optimization (PSO) and found that our classifier is highly efficient in comparison with GA and PSO and their accuracies are 2% and 8%, respectively, less than those of perplexed Bayes classifier. [ABSTRACT FROM AUTHOR]

With the integration and development of multiple disciplines, as well as a large amount of research on multilevel interconnection networks, wireless sensor networks have become a new access point and have attracted widespread attention worldwide. However, limited by the characteristics of the wireless sensor network itself, the energy problem has repeatedly restricted its function, which needs to be solved urgently. This paper mainly improves the Geographical and Energy-Aware Routing (GEAR) based on cyber-physical systems and energy-aware routing protocols, which reduces the resource consumption of network nodes, achieves network power balance, and further extends the system life cycle. Although the open sharing of the network layer makes CPS more efficient in actual use, it also increases the risks faced by CPS. Among these potential risks, cyberattacks have become the main security problem that CPS must solve with the greatest destructive power. Among them, Denial of Service (DoS) is the simplest and most destructive type of network attack. We can allow the system to reject normal requests for sending and receiving data using the network resources of the communication channel, thereby performing security control. From the perspective of control, this paper studies the security control of CPS in DOS attacks. Firstly, considering the diversity of CPS packet loss factors, assuming that there is an upper limit on the probability characteristics of the total number of packet loss development, the Markov process of inherent packet loss and the Markov process of DoS attack satisfying this constraint are analyzed, and the above two independent packet loss factors are described on this basis. Two independent packet loss factor descriptions are given in the following. In this paper, through the research of wireless sensor networks, it is applied to the cyber-physical system and the cyber-physical system is further improved. [ABSTRACT FROM AUTHOR]

The KDD CUP 1999 intrusion detection dataset was introduced at the third international knowledge discovery and data mining tools competition, and it has been widely used for many studies. The attack types of KDD CUP 1999 dataset are divided into four categories: user to root (U2R), remote to local (R2L), denial of service (DoS), and Probe. We use five classes by adding the normal class. We define the U2R, R2L, and Probe classes, which are each less than 1% of the total dataset, as rare classes. In this study, we attempt to mitigate the class imbalance of the dataset. Using the synthetic minority oversampling technique (SMOTE), we attempted to optimize the SMOTE ratios for the rare classes (U2R, R2L, and Probe). After randomly generating a number of tuples of SMOTE ratios, these tuples were used to create a numerical model for optimizing the SMOTE ratios of the rare classes. The support vector regression was used to create the model. We assigned each instance in the test dataset to the model and chose the best SMOTE ratios. The experiments using machine-learning techniques were conducted using the best ratios. The results using the proposed method were significantly better than those of previous approach and other related work. [ABSTRACT FROM AUTHOR]