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A Primer on Intelligent Defense Mechanism to Counter Cloud Silent Attacks

机译:应对云静默攻击的智能防御机制入门

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Cloud security is a key to both Cloud clients and vendors, and this has prodded intensified massive research around this domain. The contractual continuous Cloud security research is as well fuelled by the abrupt popping up of attacks that even extend to silent or stealth. Moreover these silent or stealth attacks are characterised by low observability along with bad traceability resulting in their difficulty detection, traceability and prevention. Due to the nature of silent or stealth attacks that have difficulty detection, traceability and prevention calibre, it means traditional and/or ordinary security mechanisms won’t be sufficient to compact such attacks. In turn the nearby remedy to tackle these attacks is to incorporate method(s) that can curb trends that has hidden patterns. This then calls for techniques that have capabilities for revealing hidden patterns, thus Hidden Markov Model will find its projected usage in the proposed model. As a way to offer maximum security when dealing with these sophisticated kinds of attacks, a conjuncture of cryptography will also be fused in the proposed model. A logical series of actions will be executed such that once a possible attack is sensed, then a proactive action is triggered to avoid an invasion by this attack. The intelligence is derived from the previously learning by Hidden Markov Model, although continuous active learning is still guaranteed as this algorithm carries the Machine Learning algorithm standards, along with the proactive action is triggered.
机译:云安全是云客户和供应商的关键,这促使人们对该领域进行了大量研究。突如其来的突然出现的攻击激起了合同的持续性云安全性研究,这些攻击甚至扩展到了静默或隐身。此外,这些无声或隐身攻击的特点是可观察性低,可追溯性差,导致难以检测,可追溯和难以防范。由于无声或隐身攻击具有难以检测,可追溯性和防御能力的性质,这意味着传统和/或普通的安全机制不足以压缩此类攻击。反过来,解决这些攻击的附近方法是合并可以遏制具有隐藏模式的趋势的方法。然后,这就需要具有揭示隐藏模式功能的技术,因此“隐马尔可夫模型”将在建议的模型中找到其预期的用法。作为在处理这些复杂类型的攻击时提供最大安全性的一种方法,在建议的模型中还将融合加密技术。将执行一系列逻辑操作,以便一旦感测到可能的攻击,便会触发主动操作,以避免此攻击的入侵。智能是从先前的隐马尔可夫模型学习中获得的,尽管由于该算法符合机器学习算法标准,而且仍会触发主动动作,因此仍可以保证连续主动学习。

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