Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted. Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.
翻译:基于恶意软件的检测技术依靠灰色的恶意软件图像,倾向于根据灰色灰色灰色灰色图像的分布对恶意软件进行分类。尽管机器学习技术显示的进步和有希望的结果,但攻击者可以通过生成对抗性样本来利用弱点。对立样本是通过智能制作生成的。基于软件的对抗性攻击和防御大多存在。为了保护对手,基于机器学习和灰色图像的现有恶意软件检测需要预先处理对抗性数据。这可以造成额外的间接费用,并可延长实时恶意软件的检测。因此,作为替代方法,我们探索基于对抗对手的防御(恢复随机访问记忆)RRAM(恢复随机访问记忆)。因此,这些样本的目的是解决上述关键系统安全问题。上述挑战是通过展示设计一个可靠和稳健的认知系统的拟议技术来解决的。首先,提出了一种用于检测隐性恶意软件的新技术。该技术使用恶意二手图像,然后从实时的恶意软件检测器检测到实时的恶意软件检测。因此,我们探索了以对抗对手为主的软性内部系统结构的模型,并且用不同的磁性变变变变变的系统结构来演示了以学习模型的系统。