No abstract

Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security. Malware is malicious software that affects a system as it can steal sensitive information, slow its speed, cause frequent hangs, and disrupt operations. The most common malware types are adware, computer viruses, spyware, trojans, worms, rootkits, key loggers, botnets, and ransomware. Malware detection is critical for a system's security. Many security researchers have studied the IoT malware detection domain. Many studies proposed the static or dynamic analysis on IoT malware detection. This paper presents a survey of IoT malware evasion techniques, reviewing and discussing various researches. Malware uses a few common evasion techniques such as user interaction, environmental awareness, stegosploit, domain and IP identification, code obfuscation, code encryption, timing, and code compression. A comparative analysis was conducted pointing various advantages and disadvantages. This study provides guidelines on IoT malware evasion techniques.

Every day, hundreds of thousands of new malware programs are developed and spread worldwide in cyberspace. Most of these malware programs are malware variants such as polymorphic and metamorphic malware, which are created from older versions of malware and able to change their structures and function flows to circumvent security solutions. The accuracy of malware variant detection is a crucial challenge. Many existing malware variant detections use static features extracted from the physical structure of malware file, such as opcodes and function flows. Unfortunately, the static features are subject to obfuscation and code shelling using simple obfuscation techniques. Although a malware variant can change its structure and function flows, it is widely believed that the malware variant cannot hide its malicious behavioral patterns during the runtime. Accordingly, dynamic, or behavioral analysisbased features were suggested by many studies to detect malware variants accurately. However, most of these studies are solely dependent on application-programmable interface calls (or API calls), which is not enough to accurately distinguish between malware and benign due to API-based obfuscation techniques. Therefore, a malware variant detection model that combines different behavioral activities can improve detection accuracy while reducing the false-negative rate. To this end, this study proposed a Deep-Ensemble and Multifaceted Behavioral Malware Variant Detection Model using Sequential Deep Learning and Extreme Gradient Boosting Techniques. Different behavioral features were extracted from the dynamic analysis environment. Then, a feature extraction algorithm that can automatically extract effective representative patterns has been designed and developed to extract the hidden representative features of the malware variants using a sequential deep learning model. These features have been fed into a developed extreme gradient boosting-based classifier for decision making. Extensive experiments have been carried out to validate the proposed scheme. The results were compared to the other related techniques in the field. The results show that the proposed model is reliable, as it improves the detection rate while reducing the false-negative rate.INDEX TERMS Malware detection, malware variants, multifaceted behavioral features, deep ensemble learning, sequential deep learning.

In recent years, smart city services have moved the existence of people from the physical to the virtual world (cyberspace), e.g., online banking, e-commerce, telemedicine, etc. Along with the benefits of smart cities, the problems of the physical world are also moved to the cyber world, like cyberbullying in online social networks (OSN). Automated cyberbullying detection techniques need to be designed to remove the potential tragedies in OSNs. The recent advent of artificial intelligence (AI) models like machine learning and deep learning (DL) models can be employed for the detection of cyberbullying in the OSN. With this motivation, this paper develops an AI-enabled cyberbullying-free OSN (AICBF-ONS) technique in smart cities. The proposed AICBF-ONS technique involves chaotic salp swarm optimization (CSSO)-based feature selection technique to derive a useful set of features from the OSN data. In addition, stacked autoencoder model is used as a classification model to allocate appropriate class labels of the OSN data. To improve the detection performance of the SAE model, a parameter tuning process take place using the mayfly optimization (MFO) algorithm. An extensive experimental analysis ensured the supremacy of the proposed AICBF-ONS technique.