Cybersecurity is a buzzword now, which every enterprise is concerned about. When it comes to preventive cybersecurity measures, it greatly benefits from different modes, which will enable you to predict the attacks and take the needed defense. On the side of the defender, it is promising to do research that suggests a Symbolic Deep Learning (SDL) model, which can be employed for automatic construction of cognitive models based on some fundamental examples for expert decision making. These models can be effectively deployed to offer decision-based support to non-expert users.
The approach of cyberattack modeling is a fair approximation of the unwanted impacts against the corporate It networks and systems. Usually, cyberattack models are used to identify the threats by simulating the attacks to challenge the security environments by using adverse techniques and malware. By modeling these planned attacks, the defenders will get a better insight into the possible tactics of attacks and the objectives of such attacks to take possible preventive measures against vulnerabilities within the given environment.
As we know, lately, cyberattacks are increasing largely and becoming more sophisticated and advanced year by year. This forces the enterprise security admins to put in an enormous effort to protect their critical network and infrastructure assets. There are many possible attacks like viruses, trojans, malware, and other malicious attempts are being tried, which may result in the loss of billions of dollars to enterprises across the globe, including government departments.
On the attacker side, many promising things suggest things like model-tracing having a dynamic parameter that automatically fits the models of live attacks and predicts the performances of individual attackers. This attacker preferences can be exploited to mitigate the risks involved in terms of a successful attack. In this article, we are trying to examine how this cognitive modeling of cyberattack modeling will help cybersecurity professionals and system security admins.
The realm of cybersecurity
Cybersecurity has a lot of things to do with a human agency as with the integrity of computer networks. But, when computer systems and technology fields rapidly evolve, human learning also changes regularly. As this is the case, the research in this field largely focuses on cognitive sciences and provides some breakthrough in terms of long-term security by ensuring a greater ROI by chasing the vulnerabilities in software applications. Cognitive sciences and cognitive modeling are showing great promise now in the field of cybersecurity.
This article will offer some real-time examples of how computational models based on human cognition can be utilized to predict human preferences and cybersecurity behavior. Here, we will largely focus on the classic examples of cognitive modeling. On the other side of the defenders, i.e., from the enterprise point of view, we may aim at constructing the cognitive models for the cyber analysts who work with the IDS or Intrusion Detection Systems. This will further employ the models providing some suggestions to the analysts. From the attacker’s point of view, we may aim at the constructive models of the individual attackers. With the decision biases, we may employ these cybersecurity models for reducing the risk of any attacks becoming successful. A strong database backup is vital in building cybersecurity models, and RemoteDBA.com is all set to offer reliable cybersecurity database support.
The intrusion detection systems offer the analysts with some aggregated logs of network activity with alert records, whereas each of these records may include a certain number of threat-biased features. The job of a cyber analyst here is to either consider the alert as a potential threat or to ignore it as a false alarm. By predicting the cyber analyst’s behavior in various domains, which present some challenges, the traditional approaches in computer science may deploy some machine learning techniques and classifiers. The traditional approaches in computer science may employ some of the machine learning classifiers and the training given on the expert decisions based on the alerts.
On the other hand, deep learning techniques have largely gained popularity in recent times by successfully classifying a big volume of complex data. The real problem here is the availability of labeled data which is often very sparse and comprises a few classic examples. DL also requires some recommendations, which are not quite easily explained and so may not be suited well for the decision-centered software. Also, DL-based recommendations cannot be explained easily and so may not be suited well for the decision-assisted software. Here, Symbolic Deep Learning or SDL can be a better approach to construct some models of expert behavior. The benefit of this approach is that it can address the challenges of developing the most explaining models of cognitive behavior based on small data samples.
Deterring the scope of cyberattacks with modeling
When you try managing risks involved in enterprise cybersecurity, the organizations may try to engage in classic approaches of attack modeling, threat modeling, and risk assessments. Threat modeling is usually dealing with the possibility of negative events occurring. The risk assessment is an approach of assessing the details as to how likely a threat may cause loss to the business. The attack assessment details how likely it is that this threat will cause a loss. The approach of attack modeling usually precisely deals with the vulnerabilities which are exploited to create the loss. In usual practice, the attack modeling and threat concepts are used interchangeably, and some of the modern security approaches may incorporate both elements in an implementation.
In cybersecurity, it is so difficult to protect against any possible attacks without knowing the vulnerabilities and how this works. By modeling the attacks, the defenders may gain better visibility to the severity of the vulnerabilities, which may otherwise go undetected. By simulating the cyberattacks in a controlled environment with the help of some attack simulation tools, organizations will be able to mimic the tactic and techniques of the adversaries across various cyberattack vectors. We can simulate the adverse behaviors across the spectrum. The defenders will be able to adopt the attacker mindset to identify the exact vulnerabilities in the system and the potential security threats to be tackled.
Many such solutions which incorporate the cyberattack modeling may offer remediation guidance also after the vulnerabilities are identified. By launching the simulated attacks by uncovering the security gaps and offering the ranked recommendations, such tools will help the organizations to stay a step ahead of the attackers to foresee and prevent it effectively.
