With the rapid expansion and ubiquitous presence of the Internet of Things (IoT), the proliferation of IoT devices has reached unprecedented levels, heightening concerns about IoT security. Intrusion detection based on deep learning has become a crucial approach for safeguarding IoT ecosystems. However, challenges remain in IoT intrusion detection research, including inadequate feature representation at the classifier level and poor correlation among extracted traffic features, leading to diminished classification accuracy. To address these issues, we propose a novel transformer-based IoT intrusion detection model, MBConv-ViT (MobileNet Convolution and Vision Transformer), which enhances the correlation of extracted features by fusing local and global features. By leveraging the high correlation of traffic flow, our model can identify subtle differences in IoT traffic flow, thereby achieving precise classification of attack traffic. Experiments based on the open datasets TON-IoT and Bot-IoT demonstrate that the accuracy of the MBConv-ViT model, respectively, 97.14% and 99.99%, is more effective than several existing typical models.

Dermoscopic images suffer from irregular and vague boundaries. New directions established the neutrosophic set (NS) approaches for clustering, and segmenting the dermoscopic images. In this work, an accurate segmentation process was developed by mapping initially the dermoscopic images to the NS domain. Thus, the neutrosophic image was defined by three subsets, namely True (T), Indeterminacy (I) and False (F). For accurate boundary detection and segmentation, different high pass (HP) filter types were used in the definition of I subset and low pass (LP) filter types in the definition of T. These filters form a new way to obtain an NS image for segmenting dermoscopy images. A comparative study was carried on the ISIC2016 skin lesion dermoscopic images dataset using different combinations of NS filter types and sizes. The results depicted the superiority of using an unsharp filter in implementing the I subset and an average filter for the T subset. 96% segmentation accuracy was reported using the proposed design compared to 92% accuracy using the default NS definition.

Container technology enables rapid deployment of computing services, while edge computing reduces the latency of task computing and improves performance. However, there are limits to the types, number and performance of containers that can be supported by different edge servers, and a sensible task deployment strategy and rapid response to the policy is a must. Therefore, by jointly optimizing the strategies of task deployment, offloading decisions, edge cache and resource allocation, this paper aims to minimize the overall energy consumption of a mobile edge computing (MEC) system composed of multiple mobile devices (MD) and multiple edge servers integrated with different containers. The problem is formalized as a combinatorial optimization problem containing multiple discrete variables when constraints of container type, transmission power, latency, task offloading and deployment strategies are satisfied. To solve the NP-hard problem and achieve fast response for sub-optimal policy, this paper proposes an energy-efficient edge caching and task deployment policy based on Deep Q-Learning (DQCD). Firstly, the pruning and optimization of the exponential action space consisting of offloading decisions, task deployment and caching policy is completed to accelerate the training of the model. Then, the iterative optimization of the training model is completed using a deep neural network. Finally, the sub-optimal task deployment, offloading and caching policies are obtained based on the training model. Simulation results demonstrate that the proposed algorithm is able to converge the model in very few iterations and results in a great improvement in terms of reducing system energy consumption and policy response delay compared to other algorithms.

Uncontrolled heap memory consumption, a kind of critical software vulnerability, is utilized by attackers to consume a large amount of heap memory and consequently trigger crashes. There have been few works on the vulnerability fuzzing of heap consumption. Most of them, such as MemLock and PerfFuzz, have failed to consider the influence of data flow. We proposed a heap memory consumption guided fuzzing model named MemConFuzz. It extracts the locations of heap operations and data-dependent functions through static data flow analysis. Based on the data dependency, we proposed a seed selection algorithm in fuzzing to assign more energy to the samples with higher priority scores. The experiment results showed that the MemConFuzz has advantages over AFL, MemLock, and PerfFuzz with more quantity and less time consumption in exploiting the vulnerability of heap memory consumption.

The honeypot system is a critical defense facility in cybersecurity. It captures and analyzes the attack behavior for constructing a more robust defense system. However, considering the overhead caused by analysis, the system cannot support fine-grained analysis. This paper proposes RRPOT, a novel honeypot system based on the record and replay (RnR) framework of virtual machine (VM). By combining the native honeypot system with an RnR framework, the system supports to postpone the time-consuming analysis to the replay stage, thereby enables fine-grained analysis. Moreover, we propose an on-demand record mechanism to reduce performance and storage costs during the record stage and employ an on-demand replay mechanism to improve replay efficiency. Leveraging these approaches, we develop a flexible and efficient honeypot system, and it was proven useful in the practical scenario.

Software vulnerabilities present a significant cybersecurity threat, particularly as software code grows in size and complexity. Traditional vulnerability-mining techniques face challenges in keeping pace with this complexity. Fuzzing, a key automated vulnerability-mining approach, typically focuses on code branch coverage, overlooking syntactic and semantic elements of the code. In this paper, we introduce HotCFuzz, a novel vulnerability-mining model centered on the coverage of hot code blocks. Leveraging vulnerability syntactic features to identify these hot code blocks, we devise a seed selection algorithm based on their coverage and integrate it into the established fuzzing test framework AFL. Experimental results demonstrate that HotCFuzz surpasses AFL, AFLGo, Beacon, and FairFuzz in terms of efficiency and time savings.