The lifestyle of people in this century will be categorized as Pre-COVID and Post-COVID lifestyle in the future. The pandemic has influenced every nation and every sector. The Internet of Things has helped the humankind to progress even during the pandemic time. Internet of Things has grown drastically with the industry switching their offices to homes. This has also increased the threat landscape and the attacks are also on rise. Several cyber-attacks are possible with Internet of Things devices. However, Supply chain attacks remain the largest threat as reactive measures to mitigate them are extremely difficult. In addition to that, supply chain attacks are difficult to detect in a network. In most of the cases, they remain unnoticed and they are used to launch espionage and ransom ware attacks. The impact of supply chain attacks is also extremely large compared to other attacks. The number of Internet of Things devices are going to grow by leaps and bounds, and it is necessary to take proactive steps in the network to mitigate such attacks. In this article, the elements of Internet of Things supply chain, motivation behind these attacks, vulnerabilities in the supply chain, and recent attacks are discussed in detail. In order to mitigate the supply chain attacks in Internet of Things environment, preventive measures that can be adopted at various levels are also suggested. These measures can be applied as best practices in any smart network environment against supply chain attacks.
Visibility degradation is expected in adverse weather phenomena such as fog, mist, and haze. Object detection and identification in the surveillance feed are challenging in these weather conditions. Outdoor images can naturally be degraded, or one can intentionally degrade using smoke or bright light to hide identification. Haze, fog, and smoke are pixel-based degradation and can vary based on their thickness and distribution property in an image. In our proposed work, we explore the possibility of identifying and removal hazy pixels using the benefits of multi-scale feature-fusion and In-scale feature progression. We proposed a learning-based end-to-end network for single image dehazing. Our proposed architecture consists of three different modules: (1) Coarse Feature-fusion, (2) Fine Feature-fusion, and (3) Reconstruction module. The Coarse feature-fusion module learns broad contextual information, and the Fine feature-fusion module refines the coarse features by focusing on the channel and pixel-based information. Multi-scale feature fusion is used both in the coarse and fine module to benefit the network stage from the previous stage’s output. Extensive experimental results suggest that the proposed approach outperforms other state-of-the-art methods on synthetic homogeneous and non-homogeneous haze data and improves object detection and identification accuracy.
Autonomous intrusion detection systems assess the data intelligently and take strategic decision to detect and mitigate cyber-attacks. These decisions have to be explained and evaluated for the transparency and correctness. Explainable Artificial Intelligent (XAI) methods that explore how features contribute or influence a decision taken using an algorithm can be useful for the purpose. XAI method of Testing with Concept Activation Vectors (TCAV) has been used recently to show the importance of high level concepts for a prediction class in order to deliver explanations in the way humans communicate with each other. This work explores the possibility of using TCAV to evaluate the strategic decision made by autonomous agents. A case study in the context of DoS attack is analysed to show that TCAV scores for various DoS attack classes and normal class of KDD99 data set can be used to evaluate the strategic decisions. The proposed method of analysis provides a quantifiable method to justify the current strategy or change in the strategy if required.
Reversible data hiding (RDH) is an actively emerging area in the domain of Information Security having wide applications in clinical data transmission along with medical images. In our research, we came up with a new RDH scheme to keep clinical data in the medical image to ensure secure data transmission. Histogram shifting-based RDH schemes are widely explored for RDH in images. The conventional histogram shifting-based RDH schemes have two major concerns: low embedding rate and overflow or underflow. In this approach, we discuss a prediction error histogram shifting-based approach with an improved overflow handling technique. The pixels in the images are divided into two different categories: black and white. The classification of the pixels has been carried out based on the checkerboard pattern. As we know that as per the checkerboard pattern, a black pixel will have four 4-neighbourhood pixels (left, right, top and bottom). To predict the black pixel value in the middle we used the average of three pixels out of 4-neighbourhood which are very close to the central pixel value. By considering the predicted pixel value and the actual pixel value, we have computed the prediction error. The histogram of prediction error is generated based on the prediction error corresponds to all the black pixels in the image. The prediction error histogram is considered for further data hiding through the histogram shifting approach. The overflow/underflow is a critical issue in the histogram shifting-based RDH scheme, so we have came up with an improved overflow/underflow handling technique in this approach. We have validated the results after carrying out the proposed scheme on medical and natural images.
Lightweight authentication schemes suitable for resource constrained environments like IoT devices, sensor networks, Radio Frequency IDentification(RFID)tags, contactless smart cards etc., have gained a lot of attention in lightweight cryptographic community. Mostly hash funtions based on block ciphers are widely used for designing lightweight authentication schemes. However, in recent years designing such schemes from a hash function based on permutations using different modes is gaining lot of attention. They are efficient alternatives for the current block-cipher dominated cryptographic practices in pervasive device. We propose a new serverless sponge-permutation based lightweight authentication mechanism for proving RFID Tags authenticity to any legitimate reader and vice-versa.
