Hydroponics is the soil less agriculture farming, which consumes less water and other resources as compared to the traditional soil‐based agriculture systems. However, monitoring of hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters, nutrition suggestion, and plant diagnosis system. But the recent technological developments are quite useful to solve these problems by adopting the artificial intelligence‐based controlling algorithms in agriculture sector. Therefore, this article focuses on implementation of mobile application integrated artificial intelligence based smart hydroponics expert system, hereafter referred as AI‐SHES with Internet of Things (IoT) environment. The proposed AI‐SHES with IoT consists of three phases, where the first phase implements hardware environment equipped with real‐time sensors such as NPK soil, sunlight, turbidity, pH, temperature, water level, and camera module which are controlled by Raspberry Pi processor. The second phase implements deep learning convolutional neural network (DLCNN) model for best nutrient level prediction and plant disease detection and classification. In third phase, farmers can monitor the sensor data and plant leaf disease status using an Android‐based mobile application, which is connected over IoT environment. In this manner, the farmer can continuously track the status of his field using the mobile app. In addition, the proposed AI‐SHES also develops the automated mode, which makes the complete environment in automatic control manner and takes the necessary actions in hydroponics field to increase the productivity. The obtained simulation results on disease detection and classification using proposed AI‐SHES with IoT disclose superior performance in terms of accuracy, F‐measure with 99.29%, and 99.23%, respectively.

Intrusion Detection System (IDS) is one of the most important component of network defense mechanism. In an attempt to detect network attacks, network traffic features need to be identified and both attack and normal data need to be profiled. This paper proposes a set of network traffic features that can be extracted for Real-Time Intrusion Detection. This paper also proposes Fuzzy Entropy based heuristic for Ant Colony Optimization (ACO) in-order to search for global best smallest set of network traffic features for Real-Time Intrusion Detection Data set. The proposed feature reduction algorithm was tested on standard bench-mark UCI data sets, and found to be efficient. Further the algorithm was applied to Real-Time IDS data set and found to produce promising results

One of the most crucial tool for network defense is an Intrusion Detection Systems (IDS). Several supervised machine learning algorithms exist that deal with high volumes of training data. However, there is a need to design optimized, low cost semi-supervised IDS model that can perform well with little labelled data. The challenge is also to design adaptive learning algorithms that can update themselves with minimum computational overhead in case new training data is to be included. This work demonstrates a hybrid semi-supervised machine learning technique that uses Active learning Support Vector Machine (ASVM) and Fuzzy C-Means (FCM) clustering in the design of an efficient IDS. This algorithm is tested on NSL KDD bench mark IDS data set and found to be promising.

Cyber threat intelligence includes analysis of applications and their metadata for potential threats. Static malware detection of Windows executable files can be done through the analysis of Portable Executable (PE) application file headers. Benchmark datasets are available with PE file attributes; however, there is scope for updating the data and also to research novel attribute reduction and performance improvement algorithms. The existing benchmark dataset contains non-PE header attributes, and few ignored attributes. In this work, a critical analysis was conducted to develop a new dataset called SOMLAP (Swarm Optimization and Machine Learning Applied to PE Malware Detection) with a value addition to the existing benchmark dataset. The SOMLAP data contains 51,409 samples that include both benign and malware files, with a total of 108 pure PE file header attributes. Further research was carried out to improve the performance of the Malware Detection System (MDS) by feature minimization using swarm optimization tools, viz., Ant Colony Optimization (ACO), Cuckoo Search Optimization (CSO), and Grey Wolf Optimization (GWO) wrapped with machine learning tools. The dataset was evaluated, and an accuracy of 99.37% with an optimized set of 12 features (ACO) proves the efficiency of the dataset, its attributes, and the algorithms used.

Android occupies a major share in the mobile application market. Android mobiles have become an easy target for the attackers. The main reason is the user ignorance in the process of installing and usage of the apps. Android malware can be detected based on the permissions it requests from the user. Several machine learning algorithms are being used in the detection of android malware based on the list of permissions enabled for each app. This paper makes an attempt to study the performance of some of the machine learning algorithms, viz., naïve Bayes, J48, Random Forest, Multi-class classifier and Multi-layer perceptron. Google play store 2015 and 2016 app data are used for normal apps and standard malware data sets are used in the evaluation. Multi-class classifier was found to be outperforming the other algorithms in terms of classification accuracy. Naïve Bayes classifier has outperformed as far as model construction time is concerned.