The widespread significance of Android IoT devices is due to its flexibility and hardware support features which revolutionized the digital world by introducing exciting applications almost in all walks of daily life, such as healthcare, smart cities, smart environments, safety, remote sensing, and many more. Such versatile applicability gives incentive for more malware attacks. In this paper, we propose a framework which continuously aggregates multiple user trained models on non-overlapping data into single model. Specifically for malware detection task, (i) we propose a novel user (local) neural network (LNN) which trains on local distribution and (ii) then to assure the model authenticity and quality, we propose a novel smart contract which enable aggregation process over blokchain platform. The LNN model analyzes various static and dynamic features of both malware and benign whereas the smart contract verifies the malicious applications both for uploading and downloading processes in the network using stored aggregated features of local models. In this way, the proposed model not only improves malware detection accuracy using decentralized model network but also model efficacy with blockchain. We evaluate our approach with three state-of-the-art models and performed deep analyses of extracted features of the relative model.
翻译:Android IoT 设备的广泛意义在于其灵活性和硬件支持功能,它通过几乎在日常生活各行各业,例如保健、智能城市、智能环境、安全、遥感等等,引进刺激性的应用,使数字世界发生了革命性的变化。这种多功能应用刺激了更多的恶意软件攻击。在本文件中,我们提出了一个框架,不断将多用户经过培训的非重叠数据模型合并为单一模型。具体来说,用于恶意软件探测任务,(一) 我们提议了一个新颖的用户(本地)神经网络(LNNN),在本地分销方面进行培训,(二) 保证模型的真实性和质量,我们提议了一个新的智能合同,使集成过程能够超越布拉克链平台。 LNN模型分析了恶意软件和良性软件的各种固定和动态特征,而智能合同则利用当地模型的储存综合特征,对网络上传和下载程序进行核实。 以这种方式,拟议的模型不仅利用分散的模型网络提高恶意检测准确性,而且还用块链模型的功效。我们用三个最先进的模型评价了我们的方法,并进行了深入分析。