Unveiling the core of IoT: comprehensive review on data security challenges and mitigation strategies

The Internet of Things (IoT) is a collection of devices such as sensors for collecting data, actuators that perform mechanical actions on the sensor's collected data, and gateways used as an interface for effective communication with the external world. The IoT has been successfully applied to various fields, from small households to large industries. The IoT environment consists of heterogeneous networks and billions of devices increasing daily, making the system more complex and this need for privacy and security of IoT devices become a major concern. The critical components of IoT are device identification, a large number of sensors, hardware operating systems, and IoT semantics and services. The layers of a core IoT application are presented in this paper with the protocols used in each layer. The security challenges at various IoT layers are unveiled in this review paper along with the existing mitigation strategies such as machine learning, deep learning, lightweight encryption techniques, and Intrusion Detection Systems (IDS) to overcome these security challenges and future scope. It has been concluded after doing an intensive review that Spoofing and Distributed Denial of Service (DDoS) attacks are two of the most common attacks in IoT applications. While spoofing tricks systems by impersonating devices, DDoS attacks flood IoT systems with traffic. IoT security is also compromised by other attacks, such as botnet attacks, man-in-middle attacks etc. which call for strong defenses including IDS framework, deep neural networks, and multifactor authentication system

Analyzing and Detecting Internet of Things Malware Using Residual Static Graph- and String-Based Artifacts

Recently, the Internet of Things (IoT) has become wider and adopted many features from social networks and mainly uses sensing devices technologies, causing a rapid increase in production and adoption. However, security and privacy are serious threats that users usually take precautions to protect their devices and information. Thus, understanding the security shortcomings at first stage will educate IoT users to protect their connected things. Understanding IoT software through analysis, comparison (with other types of malware), and detection (from benign IoT) is an essential problem to mitigate security threats. We focus on two central perspectives, the graph and string representations of the software, typically extracted from the software binaries. First, we look into a comparative study of Android and IoT malware through the lenses of graph measurements. We construct the abstract structures of the malware, using Control Flow Graph (CFG) to represent malware binaries, and use them to conduct an in-depth analysis of malicious graphs. Machine Learning (ML) algorithms are actively used in the process of detecting and classifying malicious software. Toward detection, we use different CFG-based features as mentioned above, and augment them with CFGs of the benign dataset and build a detection system. Furthermore, we classify the IoT malware to their corresponding families. However, adversarial ML attacks on malware detectors are proposed in the literature. For example, Adversarial Examples (AEs) on the CFG can be generated by applying small perturbation to the graph features that force the model to misclassification. Thus, we propose Soteria, a CFG-based AEs detector utilizing deep learning with random walks to construct in-depth features. Moreover, we detect the malicious shell commands by extracting and analyzing the malicious commands of IoT malware. We utilize Natural Language Processing (NLP) for feature generation, followed by a deep learning model to detect malicious commands, hence detecting malware samples