Consumer Internet of Things (CIoT) is an emerging technology that took the traditional consumer electronics to next level with smarter devices and higher connectivity. However, the rapid boom of this technology has captured a great deal of hacker’s attention in the past decade. Due to resource restricted nature of CIoT devices and limited computational abilities, security professionals are unable to strengthen the security and privacy measures. Since the existing security methods suffers from data imbalance and overfitting issues, this paper proposes a distributed and dynamic weighted boosting method named Federated-Boosting for the accurate and timely detection of cyber-attacks in CIoT. Specifically, a weighting strategy is designed to overcome the data imbalance issue by dynamically adjusting the weights of weaker class samples. Additionally, a regularized loss function is designed that helps in controlling the overfitting of the model and provide enhance performance and generalization. Finally, a time-based and performance-based dynamic aggregation scheme is designed using distributed training model that keeps the privacy of the CIoT devices intact to the local server. Extensive experiments on two real-world datasets validates the superior performance of Federated-Boosting model against state-of-the-art detection models.

Automated malware detection is a prominent issue in the world of network security because of the rising number and complexity of malware threats. It is time-consuming and resource intensive to manually analyze all malware files in an application using traditional malware detection methods. Polymorphism and code obfuscation were created by malware authors to bypass the standard signature-based detection methods used by antivirus vendors. Malware detection using deep learning (DL) approaches has recently been implemented in an effort to address this problem. This study compares the detection of IoT device malware using three current state-of-the-art CNN models that have been pretrained. Large-scale learning performance using GNB, SVM, DT, LR, K-NN, and ensemble classifiers with CNN models is also included in the results. In light of the findings, a pretrained Inception-v3 CNN-based transfer learned model with fine-tuned strategy is proposed to identify IoT device malware by utilizing color image malware display of android Dalvik Executable File (DEX). Inception-v3 retrieves the malware’s most important features. After that, a global max-pooling layer is applied, and a SoftMax classifier is used to classify the features. Finally, gradient-weighted class activation mapping (Grad-CAM) along the t-distributed stochastic neighbor embedding (t-SNE) is used to understand the overall performance of the proposed method. The proposed method achieved an accuracy of 98.5% and 91%, respectively, in the binary and multiclass prediction of malware images from IoT devices, exceeding the comparison methods in different evaluation parameters.

The latest advancements in artificial intelligence (AI) technologies, including machine and deep learning models, in prediction, recommending, and automating processes have greatly impacted IoT devices in general, and protect them from cyberattacks in particular. Blockchain also has features that assist in creating more secure IoT devices due to its abilities of traceability, acceptability, and trust. This paper studies the current advancements in the IoT and blockchain, their architectures, and their effect on security. The paper proposes a novel framework that takes into consideration the advantages and benefits of machine/deep learning models and blockchain in order to provide a solution that makes IoT devices more secure. This framework is based on the IoT four-layer architecture, and it aims to enhance the way IoT devices detect and recognise cyberattacks using blockchain and machine/deep learning algorithms. Machine and deep learning algorithms are responsible for detecting security attacks in the IoT, based on their patterns. The blockchain platform is responsible for verifying whether a specific request is secure, and it also uses cryptography to sign all new requests in order to recognise them in future requests. The MQTTset dataset, which is contains data associated with intrusion detection cases, has been used to implement a case study that aims to prove the validity of this framework. Various machine and deep learning algorithms have been used in this case study which have all achieved high results with regard to precision, recall, accuracy, and F1 performance measurements. Such results have proven the validity and reliability of the proposed framework to detect and predict new attacks before their requests are processed within a particular IoT system.

Replay attack is considered a common attacking technique that is used by adversaries to gain access to confidential information. Several approaches have been proposed to prevent replay attack in security-critical systems such as Automated Teller Machines (ATM) systems. Among those approaches is a recent one called the Mutual Chain Authentication Protocol for the Saudi Payments Network transactions (MCAP). This protocol aims to allow Saudi banking systems to overcome existing weaknesses in the currently used Two-Factor Authentication (2FA) protocols. In this paper, we analyze and verify the recent MCAP authentication protocol against replay attacks. Therefore, we examine the mutual authentication between the ATM Terminal, Sponsoring Banks (SBAT), Saudi Payments Network (SPAN) and the Issuing of Financial Bank (CIFI). The paper also provides a formal analysis of the MCAP to conduct formal proofs of the MCAP protocols against replay attacks.

Many existing studies have shown that the causes of most of system attacks are not related to coding vulnerabilities that apply to individual systems, issues related to the run-time environment, or the technology in place. In fact, they are caused by issues associated with how systems within organizations are structured. Therefore, it is necessary to examine security with regard to all components that influence the organization’s systems, including data, processes and even employees. The most promising approach to achieving this goal is Enterprise Architecture (EA). The main goal of this project is to develop a framework based on the concepts of well-established EA frameworks such as TOGAF and Zachman and their compositional layers (e.g., application, information and process). This framework will be combined with a data flow analysis of the principles that trace the potential information flow between high- and low-security enterprise components. Therefore, this paper studies various enterprise architecture frameworks and shows how to develop an enterprise architecture framework that considers the organization’s information security from the perspective of information flow. This framework will have various layers, each with a set of security metrics that quantify the organization’s relative security based on the specifications of that layer. The defined framework will be capable of defining Enterprise Architecture security-related principles and metrics. These principles and metrics will eventually be used to define how to develop secure enterprise systems based on the enterprise architecture with regard to security-critical information flow within any given organization. The defined framework will also be capable of providing guidance for information security architects by recognizing certain parts of the organization that are less secure than others.

With the increasing demand for developing high-quality and more reliable systems, the process of developing trustworthy computer software is a challenging one. In this paper, we review various approaches to producing more secure systems. This includes established general principles for designing secure systems. It also provides an introduction to general software quality measurements including existing software security metrics. This paper also includes a comparison of the various security metrics for developing secure systems (i.e., architectural, design, and code-level metrics). Lastly, the paper examines the approach of refactoring, illustrates its objectives, and shows how refactoring is generally used for enhancing the quality of existing programs from the perspective of information security. At the end of this paper, we provide a discussion of these three approaches and how they can be used to provide guidance for future secure software development processes