The growing deployment of IoT devices has led to unprecedented interconnection and information sharing. However, it has also presented novel difficulties with security. Using intrusion detection systems (IDS) that are...
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ISBN:
(数字)9798350372120
ISBN:
(纸本)9798350372137
The growing deployment of IoT devices has led to unprecedented interconnection and information sharing. However, it has also presented novel difficulties with security. Using intrusion detection systems (IDS) that are based on artificial intelligence (AI) and machinelearning (ML), this research study proposes a unique strategy for addressing security issues in Internet of Things (IoT) networks. This technique seeks to address the challenges that are associated with these IoT networks. The use of intrusion detection systems (IDS) makes this technique feasible. The purpose of this research is to simultaneously improve the present level of security in ecosystems that are connected to the Internet of Things (IoT) while simultaneously ensuring the effectiveness of identifying and mitigating possible threats. The frequency of cyber assaults is directly proportional to the increasing number of people who rely on and utilize the internet. Data sent via a network is vulnerable to interception by both internal and external parties. Either a human or an automated system may launch this attack. The intensity and effectiveness of these assaults are continuously rising. The difficulty of avoiding or foiling these types of hackers and attackers has increased. There will occasionally be individuals or businesses offering IDS solutions who have extensive domain expertise. These solutions will be adaptive, unique, and trustworthy. IDS and cryptography are the subjects of this research. There are a number of scholarly articles on IDS. An investigation of some machinelearning and deep learning techniques was carried out in this research. To further strengthen security standards, some cryptographic techniques are used. Problems with accuracy and performance were not considered in prior research. Furthermore, further protection is necessary. This means that deep learning can be even more effective and accurate in the future.
This paper introduces our solution for Track 2 in AI City Challenge 2022. The task is Tracked-Vehicle Retrieval by Natural Language Descriptions with a real-world dataset of various scenarios and cameras. We mainly fo...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487405
This paper introduces our solution for Track 2 in AI City Challenge 2022. The task is Tracked-Vehicle Retrieval by Natural Language Descriptions with a real-world dataset of various scenarios and cameras. We mainly focus on developing a robust natural language-based vehicle retrieval system to address the domain bias problem due to unseen scenarios and multi-view multi-camera vehicle tracks. Specifically, we apply CLIP [16] to effectively extract both visual and textual representations for contrastive representation learning. Furthermore, for new scenarios in the test set, we pro-pose a novel Domain Adaptive Training method that utilizes information from labeled data and transfers it to the unseen domain by generating pseudo labels. By using this simple and effective strategy, we not only bridge the domain gap between the training set and test set, but also require less computational cost and data compared to previous top performance methods. Finally, we employ a context-sensitive post-processing method to address model’s uncertainty and eliminate the wrong retrieved vehicle track. Taking one step further, we also investigate the impact of different text formats and the number of pseudo labels data for the fine-tuning process. Our proposed method has achieved 3rd place in the AI City Challenge 2022, yielding a competitive performance of 47.73% MRR accuracy on the private test set, which verified the effectiveness of the proposed solution.
The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted r...
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ISBN:
(数字)9798331506582
ISBN:
(纸本)9798331506599
The ongoing advancement of information technology has led to the emergence of a multitude of network attack methodologies and techniques. As a prevalent form of network attack, it is imperative to undertake targeted research on the deliberate disruption of network traffic. Two issues currently impede the efficacy of machinelearning and deep learning models in detecting brute force attacks. First, the presence of noise in the traffic data reduces the accuracy and robustness of detection. Second, when an attacker modifies the frequency, intensity, time, and other parameters of their attack to evade detection, the characteristics of the attack traffic become more complex and diverse. This, in turn, results in a notable decline in detection accuracy. In order to address the aforementioned issues, this paper presents a novel detection model that integrates the capabilities of an artificial neural network (ANN) and an auto-encoder (AE). The proposed model leverages the ANN's proficiency in recognizing intricate data patterns and the AE's effectiveness in removing noise, thereby facilitating precise detection of brute-force attacks. The experimental results demonstrate that the proposed detection model exhibits an enhanced accuracy rate of 99.93% in CICIDS-2017, exhibiting superior overall detection performance compared to existing algorithmic models such as AE and DAE. Additionally, it demonstrates a reduced false alarm rate and effective mitigation of the impact of noise and interference on attack data.
At present, the national treatment and prevention measures for industrial exhaust gas pollution have transitioned from industrial upgrading, capacity reduction, and energy structure regulation to the stage of precise ...
ISBN:
(纸本)9781450385046
At present, the national treatment and prevention measures for industrial exhaust gas pollution have transitioned from industrial upgrading, capacity reduction, and energy structure regulation to the stage of precise positioning and precise treatment due to the phenomenon of stealthy and excessive discharge of exhaust gas in industrial parks, which has brought serious threats to the safety of the atmospheric environment. In addition, the exhaust gas emission of industrial parks is characterized by randomness, abruptness, complex gas composition and large gas emission. Therefore, traditional pollution location methods such as emission inventory method and grid method are not effective. Based on the above problems, this paper proposes an air pollution gas location strategy based on neural network multi-layer perceptron algorithm. Through patternrecognition, fuzzy linear discriminant function is applied, and the feature space is segmented by hyperplane discriminant boundary and the fuzzy area is retained. Determine the orientation of the fuzzy discriminant surface by measuring the weight vector of a specific neuron and formulate a method to initialize the initial weight of the network on the hypersphere. Determine the weight initialization hypersphere by measuring the distance from the origin to the discriminant surface and the offset and then to further determine the specific location information of the polluted gas. After proposing the control strategy, this paper performed a specific simulation verification on the MATLAB platform. The verification results show that the algorithm strategy can greatly reduce the learning time of the neural network, improve the convergence performance of the network, and significantly improve the accuracy of polluting gas positioning.
Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. Rough set theory is closely related to knowledge technology in a variety of ...
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Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. Rough set theory is closely related to knowledge technology in a variety of forms such as knowledge discovery, approximate reasoning, intelligent and multiagent system design, knowledge intensive computations .
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