5 ML Security Challenges Demanding our Unwavering Attention

As per Gartner, almost 80 percent of every emerging technology will have Artificial Intelligence as the backbone by the end of 2021. Building secure software is a no mean feat. Amid the lingering cybersecurity threats and the potential challenges posed by the endpoint inadequacies, the focus is continuously shifting towards machine learning and the relevant AI implementations for strengthening the existing app and software security standards.

ML algorithms are calling the shots in modern warfare against the cyber attackers.

Based on reports released by IDC, threat intelligence using AI as the underlining technology is expected to attract funding of almost $2.7 billion, by the end of 2020, only behind automated customer servicing.

However, the implementation isn’t completely alienated from security risks. Due to the widespread inclusion of Machine Learning in gaming, SEO ventures, translations, and image classification, the worries are also escalating rapidly.

More than the inherent threats, a majority of the security challenges are
accommodative in nature. This means that the erroneous usage of Machine Learning clubbed with haphazard adoption is the primary reason behind the lingering coercions.

In the subsequent sections, we shall take a closer look at these risks and even chalk out the mitigation strategies for each.

Machine Learning algorithms are massively data-intensive. This means that ML systems rely on data inputs to make predictions and take decisions accordingly. Tricking the system into making misleading predictions by falsifying the inputs is one of the primary security threats. These include optical illusions fed into the machines for projecting datasets that do not exist in the real world.

Eventually, the ML models and systems make decisions based on the unscrupulous feeds, thereby sabotaging the sanctity of the output.

Machine learning systems require internet access for being operational. This association gives cybercriminals a window of opportunity to barge into the systems and mislead the same. However, system manipulation is the worst-case scenario where the algorithms are bypassed and retrained for responding to the fraudulent commands.

Mitigating this threat or any anomaly related to misleading predictions requires impenetrable machine learning algorithms as proposed by the experienced ML engineers. Besides that, establishing data ownership, restricting access, and securing online access are some of the other ways to keep system manipulations to a minimum.

Machine Learning models make use of a feedback process to evolve, learn, and grow iteratively. Therefore, if a certain block of information is corrupted, the entire iterative loop is poisoned. Imagine Google’s RankBrain being fed with erroneous and misleading information?

This loophole would use the algorithm’s self-learning ability and evolve into a much bigger issue, thereby giving out vastly inaccurate results. As pointed out by AliveBetter, Machine learning models like the RankBrain use intent and interpretation modeling, and in the absence of a data and feedback filtering model, the mentioned threats can escalate beyond measure.

With data being the stepping stones for creating learning and training models, it is necessary to ensure confidentiality and data privacy for maintaining the exclusivity of the systems. A machine learning model, if left unattended, becomes a data repository of sorts, which can be subjected to data extraction and mining techniques by professional hackers.

Cybercriminals also resort to Function Extraction threats and attacks, especially on a sub-symbolic level. These attacks clubbed with adversarial outbreaks and malicious input, as mentioned previously, can compromise the safety and sanctity of an intelligent, predictive model. The best way to steer clear of these threats is to opt for top-of-the-line encryption strategies, both at the origin and output. This approach immunes the system against the uncalled data extraction assaults from a combative and even preventative perspective.

Machine learning models rely on a pre-trained system for extracting knowledge and transferring the basics of learning, as per the acquired feedback analysis. A majority of hackers wait for the opportune moment and consider breaking into the node connecting the system and the existing model. A preventative approach is what works best for keeping this security threat out. Transfer learning and knowledge management should consider tried and tested algorithms and systems which aren’t open to extended usage.

Every machine learning system is vulnerable unless we find a way to fix the data security threats. Machine learning isn’t a close-ended resource, and with AI in the picture, it becomes necessary to allow the data sets to evolve via external feedbacks and iterative learning processes. The exposure brings in a host of threats, which in turn corrupts the source code and learning algorithms, in the ways mentioned above.

The disconnect between the ML algorithms and data science, including the lack of data security standards, is one of the reasons why 87 percent of the ML projects fail.

Therefore, to keep the mentioned risks to a minimum, we must conduct detailed architectural threat analysis, fix the open points that support intrusion, and allocate data protection strategies to avoid poisoning and system manipulations.