Software Attacks

When an assailant avails itself of the auxiliary software or safety vulnerabilities provided by an IoT system, software attacks are launched. There are various software attacks, including:

Viruses, Worms, Trojan Horses, Spyware, and Adware

This dangerous software tampers with data or steals information by adversely infecting the system or even launching DoS. These attacks are vulnerable to resource destruction in any IoT system. A high-level synthesis (HLS) and lightweight framework are the proposed countermeasures for these software attacks. It is important to mitigate these attacks, in order to secure any IoT system. A lightweight framew'ork has been developed that integrates three safety features to avoid Trojans from being introduced to IoT-based devices. Initially, in order to assure a trusted transmission among untrusted nodes, vendor diversity is established. Then, in order to prevent unwanted parties from accessing communications, message encryption is established. Last, legal nodes are allowed to validate the encryption status and content of messages by mutual auditing. The HLS is introduced in order to prevent hardware Trojans. HLS designs a security-enhanced hardware that directly prevents Trojans from being injected into the network.

Malware

Malware is software that affects the data present in IoT devices, which further results in the contamination of data or cloud centers [28]. In reality, it is the most common software attack launched in any IoT system. Malware infects the data, which is the main effect of this software attack. A lightweight neural network framework and malware image classification system (MICS) are the proposed countermeasures for this software attack. An MICS is software that classifies malware and represents images globally and locally. Before capturing hybrid local and global malware features to perform malware family classification, the MICS first converts the suspicious program into a gray-scale image. A lightweight conventional neutral network framework is an alternate solution, which can classify malware samples gathered from two distinct households. In this case, program binaries are converted to gray-scale images that detect malware with high accuracy.

Data Attacks

In order to exert pressure on certain computing resources which maintains the features of data collectivity and managing data connection required by IoT devices, the development and evolution of the IoT is extremely important [29]. As the fundamental root of each and everything that the IoT could offer to users, cloud computing steps into the action. With the help of cloud computing, virtual servers, a database example, and the creation of data pipelines that help IoT solutions to run could become effortless. The cloud ensures better safety by supplying firmware and other software techniques, better management, and different procedures, in order to assure data security. Some of the major data attacks include:

Data Inconsistency

In the central database, when an attack is made on data ethics that leads to the incompatibility of data and its storage, this is known as data inconsistency. As the name suggests, the inconsistency in data is the main effect of this attack. Blockchain architecture and a chaos-based scheme are the proposed countermeasures for this data attack. Chaos-based schemes, along with a chaos-based privacy preserving cryptographic scheme, also use a message authentication code (MAC), in order to provide the safe transmission of data within a home with smart features. This scheme guarantees data integrity by using a logistic map to create symmetric keys, to provide a safe mean of communication. In the case of remote semi-trusted data storage, block- chain is used, based on a three-level split [30].

Unauthorized Access

In the IoT, it is extremely important to prevent unauthorized users from getting access control and to ensure authorized users complete access. Unauthorized users can achieve access to private data if they are given complete access control. The violation of data privacy is the main effect of this data attack. Blockchain-based attribute based encryption (ABE) and privacy-preserving ABE are the proposed countermeasures for this attack. It becomes extremely difficult to acquire access to private data, because of the wireless sensor network’s (WSN) open wireless channel, although the WSN plays an important role in the IoT. For that reason, access control is extremely important and must be incorporated, either with or without the use cryptography. A blockchain- based infrastructure, with the help of ABE, has been proposed. The proposed scheme works in such a way that the privacy of the transaction data is protected, supports integrity, and supports nonrepudiation. In order to preserve the IoT system, this scheme provides end-to-end privacy and directs the privacy of shared data by obtruding access control in the blockchain.

Data Breach

When an unauthorized user gets access to private or confidential data, this is known as a data breach. It is also known as memory leakage. Data leakage is the main effect of this data attack. Two-factor authentication, dynamic privacy protection (DPP), and improved secure directed diffusion (ISDD) are the proposed countermeasures for this attack. In recent years, the privacy of the user’s personal data has been at risk, due to data breaches. A lightweight authentication scheme has been proposed by the work [31]. This acts as a security safeguard to ensure proper transmission between multiple IoT devices. A DPP model has been devised by some authors in other work [32], with the help of dynamic programming. This model enables the resource-constrained devices in order to attain optimal solutions for securitypreserving levels. In order to avoid any chance of privacy leakage, content-oriented data pairs (CODP) and optimal data alternatives (ODA) are used. An (ISDD) is a protocol proposed by some authors, which assures data confidentiality in IoT systems.

Table 14.3 summarizes major attacks that may harm the IoT device and its functions.

TABLE 14.3

IoT Devices Are Prone to the Following Attacks