A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models.

The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters.

A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security.

Ensuring the privacy and trustworthiness of smart city-Internet of Things (IoT) networks have recently remained the central problem. Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. However, it limits the critical problems of high time complexity, computational cost, difficulty to understand, and reduced level of security. Therefore, the proposed work intends to implement a group of novel methodologies for developing an effective Cyborg intelligence security model to secure smart city systems. Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. The original contribution of this work is to develop a novel Cyborg intelligence framework for protecting smart city networks from modern cyber-threats. In this system, a combination of unique and intelligent mechanisms are implemented to ensure the security of smart city networks. It includes QIDI for data filtering, CSOM for feature optimization and dimensionality reduction, and RMML for categorizing the type of intrusion. By using these methodologies, the overall attack detection performance and efficiency have been greatly increased in the proposed cyborg model. Here, the main reason of using CSOM methodology is to increase the learning speed and prediction performance of the classifier while detecting intrusions from the smart city networks. Moreover, the CSOM provides the optimized set of features for improving the training and testing operations of classifier with high accuracy and efficiency. Among other methodologies, the CSOM has the unique characteristics of increased searching efficiency, high convergence, and fast processing speed. During the evaluation, the different types of cyber-threat datasets are considered for testing and validation, and the results are compared with the recent state-of-the-art model approaches.

Binary Hunter-Prey Optimization with Machine Learning-Based Cybersecurity Solution on Internet of Things Environment.

Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter-prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.

Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression.

Prostate cancer is the main cause of death over the globe. Earlier detection and classification of cancer is highly important to improve patient health. Previous studies utilized statistical and machine learning (ML) techniques for prostate cancer detection. However, several challenges that exist in the investigation process are the existence of high dimensionality data and less number of training samples. Metaheuristic algorithms can be used to resolve the curse of dimensionality and improve the detection rate of artificial intelligence (AI) techniques. With this motivation, this article develops an artificial intelligence based feature selection with deep learning model for prostate cancer detection (AIFSDL-PCD) using microarray gene expression data. The AIFSDL-PCD technique involves preprocessing to enhance the input data quality. In addition, a chaotic invasive weed optimization (CIWO) based feature selection (FS) technique for choosing an optimal subset of features shows the novelty of the work. Moreover, the deep neural network (DNN) model can be applied as a classification model to detect the existence of prostate cancer in the microarray gene expression data. Furthermore, the hyperparameters of the DNN model can be effectively adjusted by the use of RMSprop optimizer. The design of CIWO based FS technique helps for reducing the computational complexity and improve the classification accuracy. The experimental results highlighted the betterment of the AIFSDL-PCD approach on the other techniques with respect to distinct measures.

Deep Learning Approaches for Prognosis of Automated Skin Disease.

Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.