Industrial Internet of Things (IoT)-connected devices are now nearly ubiquitous in the world, and routers are a central point for connecting these Industrial IoT devices. As a router's firmware controls the basic functions of Industrial IoT devices, it is considered the heart of IoT. An Industrial IoT cyberattack can cause huge damage to the connected devices and harm to their owners. Thus, router firmware vulnerability detection has recently become an emerging issue in this domain. As a result, an efficient and precise detection tool is a necessity to this domain. However, the firmware dataset collection is the most challenging step as there are no open-source datasets available online. A manual effort was required to verify the states of samples in both the Common Vulnerabilities and Exposures and the National Vulnerability Database databases as either vulnerable or benign. After verification, 1450 samples were collected. This article investigates the effectiveness of using convolutional neural networks (CNNs) and computer vision techniques to analyze home router firmware. The collected firmware samples were read as an array of byte strings, divided into subarrays based on the image's dimensions, and then layered on top of one another to produce the firmware images. The images were divided by manufacturer and used as inputs for various CNN models to test their accuracy. Three statistical filtering algorithms were used on each manufacturer's set to produce multiple versions of each set, totaling 24 datasets across four manufacturers, with six datasets per manufacturer (four filtered images and two grayscale and RGB images). The image filter algorithms used include local binary pattern (LBP), histogram of oriented gradients (HOG), and Gabor filter used on the LBP and HOG sets. After testing all the combinations of the filtered/normal datasets with the CNN training model, the HOG filter was the most accurate, with an average accuracy of 85.81% across all tests and models, with results as high as 97.94% when used with the appropriate CNN model.

Mapping a binary function taken from a compiled binary to the same function in the original source code has many security applications, such as discovering reused free open source code in malware binaries. To facilitate malware analysis, we present BINDEEP, a framework that learns the semantic relationships among binary functions based on assembly code. It also learns semantic information about the source functions in order to carry out function matching. We demonstrate how BINDEEP can be applied to fingerprint the origin of functions in malware binaries, and then benchmark its performance against that of five competing systems (i.e., RESOURCE, the Binary Analysis Tool (BAT), BinPro, Statistical Machine Translation (SMT), and FOSSIL). The findings show that BINDEEP is more robust and achieves significant improvement over these existing systems when confronted with changes introduced by code transformation methods or the use of different compilers and optimization levels. Furthermore, BINDEEP is able to discover source packages in malware binaries, such as Zeus and Citadel, that match those listed in existing security reports.

Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs) wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node’s sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes’ behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.

Routing in cognitive radio networks (CRNs) faces many limitations that make it challenging. First, traditional routing protocols cannot be directly applied in CRNs because they consider fixed frequency band. Second, cognitive radio enables dynamic spectrum access which causes adverse effects on network performance. Third, effective routing in CR Networks (CRNs) needs local and continual knowledge of its environment. Last, presence of malicious nodes and their misbehaving activities affect the route establishment and therefore reduce the network performance. In this paper, we address such limitations by combining spectrum sensing and routing to propose a novel routing algorithm that uses nodes' behavior during spectrum sensing phase as a routing metric. Through the spectrum sensing phase, nodes behavior is measured through a parameter called belief level (BL), which describes the node's reliability to correctly sense the spectrum and to use spectrum channels accordingly. Moreover, we secure the routing requests and reply messages by encrypting them utilizing the existing cryptography techniques. The proposed approach is designed to maximize security level of paths, minimize the effects of licensed users activity over spectrum channels, and reduce the total channels cost over the best path(s). Evaluation of the proposed approach shows that its performance outperforms many current state-of-the-art routing algorithms used in CRNs in terms of end-to-end delay, packet delivery ratio, and packet loss ratio.