At the Core of Cybersecurity are: Risks, Costs, Benefits and Threat Models
As humans we are driven by risks and threats, and where we are continually weighing-up costs and benefits. A threat is the actual thing that could actually cause harm, loss or damage, whereas a risk is the likelihood of a specific threat happening. Those who are successful in their lives are often those people who can best understand threats and assess risks. IBM is one company who has managed to succeed within the computer industry for over 100 years, and where they have continually faced with new threats from competitors and from the rise of new technologies. Each time they have generally managed to understand the risks that they face and overcome them. In the 1960s, for example, IBM had a lead in the market place for mainframe computers, but the 1970s saw the rise of the microprocessor and the personal computer (PC). And so IBM addressed this by adopting the rise of the PC and eventually leading with their own standard. As the development of the PC quickened, they again they found their leadership under threat and decided to concentrate on high-end workstations and mainframe computers.
In our lives, too, we expose ourselves through vulnerabilities, and which are our weaknesses, and which could be exploited by others. Within Cyber intelligence we must thus need to continually understand our threats and vulnerabilities, and weigh up the risks involved. With finite budgets for computer security, and we must thus focus on those things which will bring the most benefit to the organisation. A major challenge is always to carefully define costs and benefits. A CEO might not want to invest in a new firewall if the justification is that it will increase the throughput of traffic. Whereas a justification around the costs of a data breach, and an associated loss of brand reputation might be more acceptable for investment.
Threat analysis is growing field and involves understanding the risks to the business, how likely they are to happen, and their likely cost to the business. Figure 1 shows a plot of the cost of risks against the likelihood. If there are low costs, it is likely to be worth defending against. Risks which are not very likely, and which have a low cost, and also a risk which has a high cost, but is highly likely, are less likely to be defended against. At the extreme, a high risk which has a low likelihood and which has high costs to mitigate against is probably not worth defending against. The probabilities of the risks can be analysed either using previous experience, estimates, or from standard insurance risk tables. Figure 2 outlines an example of this.
The investment in cybersecurity must often be justified, especially in the benefits that it brings to an organisation. For audit/compliance reasons, a company must often prove that the match the key regulatory requirements within their market place. Regulations such as GDPR, and acts such as Gramm-Leach-Bliley (GLB), Sarbanes-Oxley (SOX), and the Computer Fraud and Abuse, are often a key driver for investments in cybersecurity, as a failure to comply with these can lead to significant fines or even criminal charges. The GLB Act outlines the mechanisms that financial intuitions can use to share customer data. And, due to the financial scandals of Enron, WorldCom, and Tyco, SOX was passed in 2002, and which defines the methods used to implement corporate governance and accountability. One driver for cyber intelligence is thus the ability to gather the required information for auditors to review.
As previously defined, there are many other costs that an organisation may face, including the loss of business, brand damage, and a reduction in shareholder confident. One method of understanding the cost of risk is to determine the single loss expectancy, which is calculated from:
ALE = AV x ARO
and Where ALE is the Annual Loss Expectancy, ARO is the Annualized Rate of Occurrence, and V is the value of the particular asset. For example, if the likelihood of a denial-of-service on a Web-based database is once every three years, and the loss to sales is $100K, the ALE will be:
ALE = $100K x 1/3 = $33K per annum
This formula assumes that there is a total loss for the asset, and for differing levels of risk, an EF (Exposure Factor) can be defined as the percentage of the asset damage. The formula can then be modified to:
ALE = AV x ARO x EF
The major problem in defining risk, and in implementing security policies, is that there is often a lack of communication on security between business analysts and information professionals, as they both tend to look at risk in different ways. Woloch  highlights this with:
Get two risk management experts in a room, one financial and the other IT, and they will NOT be able to discuss risk. Each puts risk into a different context … different vocabularies, definitions, metrics, processes and standards.
At the core of Cyber intelligence is a formalisation of the methodology used to understand and quantify risks. One system for this is CORAS (A Framework for Risk Analysis of Security Critical Systems) and which has been developed to understand the risks involved. A key factor of this framework is to develop an ontology (as illustrated in Figure 3) where everyone speaks using the same terms. For example:
A THREAT may exploit a VULNERABILITY of an ASSET in the TARGET OF INTEREST in a certain CONTEXT, or a THREAT may exploit a VULNERABILITY opens for a RISK which contains a LIKELIHOOD of an UNWANTED INCIDENT.
In this way, all of those in an organisation, no matter their role, will use the same terminology in describing threats, risks and vulnerabilities.
For risk management, it is understood that not all threats can be mitigated against, and they will be carefully managed and monitored. Figure 4 shows the methodology used by CORAS in managing risks, and where a risk might be accepted if the cost to mitigate against it is too high. Network sensors can thus then be set up to try and detect potential threats, and to deal with them as they occur. For risk avoidance, systems are set up so that a threat does not actually occur on the network. An example of risk management is where a company might not setup their firewalls to block a denial-of-service (DoS) attack, as it might actually block legitimate users/services, and could thus install network sensors (such as for Intrusion Detection Systems) to detect when a DoS occurs. With risk avoidance, the company might install network devices which make it impossible for a DoS attack to occur.
The importance of clearly defining threats allows us to articulate both the threat itself, and also define clearly the entities involved with an incident. Figure 5 shows an example of defining the taxonomy used within a security incident, and where:
A [Threat] is achieved with [Attack Tools] for [Vulnerabilities] with [Results] for given [Objectives].
Kill chain model
Within cybersecurity, we see many terms used within military operations, including demilitarized zones (DMZs), defence-in-depth and APT (Advanced Persistent Threat). Another widely used term is the kill chain where military operations would attack a specific target, and then look to destroy it. A defender will then look to break the kill chain and understand how it might be attacked. An example of the kill chain approach is “F2T2EA”, where we Find (a target), Fix (on the location of the target), Track (the movement of the target), Engage (to fix the weapon onto the target), Assess (the damage to the target). A core of this approach is the provision of intelligence around the finding, tracking and assessment of the target.
One of the most used cybersecurity models to understand threats is the kill chain model, and was first proposed by Lockheed Martin. Yadav et al  define that the technical nature of key stages of an attack, include Reconnaissance, Weaponize, Delivery, Exploitation, Installation, and Act on Objective (Figure 6). So let’s say that Eve wants to steal the academic records of a university student (Carol). She might perform a reconnaissance activity and find out that Bob is an academic related to Carol’s programme of study.
Eve might then determine that Bob runs Windows 10 on his computer, and will then move to weaponization. For this Eve selects a backdoor trojan which fakes the login process for his university site. Eve does this by scrapping the university login system. Next, she picks a suitable delivery mechanism, and decides that a spear phishing method which will trick Bob into logging into the fake Web site. Eve then tries a different phishing email each day and for each attempt, she monitors for any activity of Bob putting in his university login details and his password. Once he is fooled into putting in his username and password, Eve then logs the IP address of his computer, and remotely logs into it. She then installs a backdoor program, and which captures his keystrokes. Eve then monitors his activities, until she sees him logging into the university results system, and where she can capture his login details for this system, and then she can act on her objective and steal Carol’s results.
The first stages of an attack is likely to involve some form of reconnaissance, and which can either be passive scanning or active scanning. Within active reconnaissance, an attack may use discovery tools to determine servers, networking devices, IP address ranges, and so on. These tools will typically leave a trace on the network, and which could be detected for the reconnaissance activities. Typically an organization would have standard signature detection methods to detect the scanning of IP addresses, TCP ports, and in the discovery of networked services. A company could then black-list, or lock-down, the IP address which sourced the scan.
With passive scanning an attacker might use open source information to better understand their target. This increasingly involves Open-Source Intelligence (OSINT) Reconnaissance. Increasingly, too, we all leave traces of our activities across the Internet, and as we do, we leak information that could be useful for an attacker. A spear-phishing attack may thus be targeted against a person who has leaked information about their next-of-kin, or on their normal work times. Eve, for example, might know that Carol has a friendship with Trent, and that Carol also uses Pinterest. She then finds out that Carol always starts work at 9am, and that she has been associated with a given IP address. On checking her Twitter account, Eve sees that Carol attended a rock concert the night before. Eve then sends Carol an email just before 9am of:
Trent here. Hope you had a great time at the concert. Here are some photos from that I took [here].
Eve then sets up a fake Pinterest site, and which asks for Carol’s login details. Carol then enters her password, but it is rejected, and then Eve’s fake Web page forwards Carol to the correct Pinterest site, and she logs in. Everything looks okay, and Carol just thinks that she has entered the wrong password in the first login attempt. But Eve now sees Carol’s username, password and IP address. If Carol uses the same password for many of her accounts, Eve can then move through sites which she is likely to use, and use the Pinterest-sourced password. Thus Eve has used a targeted spear-phishing attack, and where she had determined something about Carol, and then targeted her with something that she thinks Carol will be tricked with.
Many criticise the kill chain model in cybersecurity as it does not cover all of the possible attacks, and is the limited number in the number of stages. The MITRE ATT&CK(TM) extends these phases into: Initial Access, Execution, Persistence, Privilege Escalation, Defense Evasion, Credential Access, Discovery, Lateral Movement, Collection, Command and Control, Exfiltration, and Impact, and the splits these up into techniques used in each phase . Figure 7 outlines that the initial access phase could be achieved those methods such as Drive-by Compromise, and Exploit Public-Facing Application, and which can then be used as a knowledge base for the tactics and techniques used. Within each of the techniques, the framework outlines real-life examples, detection methods, and possible mitigations.
Peter Polis  then brought together the approaches of the kill chain model and the MITRE ATT&CK(TM) knowledge base to created Unified Kill Chain (UKC) model, and which defines 18 unique attack phrases. These are split into stages of an initial foothold and which pivots to network propagation and then with access onto an action (Figure 8). The reconnaissance phases involves: Weaponization; Delivery; Social Engineering; Exploitation; Persistence; Defense Evasion and Command & Control (Figure 9), the network discovery phase involves Discovery; Privilege Escalation; Execution; Credential Access; and Lateral Movement, with an action phrase of Collection; Exfiltration; Target Manipulation; and Objectives.
I repeat, at the core of cybersecurity are: risks, costs, benefits and threat models. We need common definitions for our definitions, and in defining a common knowledge base. The Unified Kill Chain model goes some way to achieving this.
 B. Woloch, “New dynamic threats requires new thinking: moving beyond compliance”,” Computer Law & Security Review, vol. 22, no. 2, pp. 150-156, 2006.
 T. Yadav and A. M. Rao, Technical aspects of cyber kill chain,” in International Symposium on Security in Computing and Communication. Springer, 2015, pp. 438-452.
 P. Pols, Unifed kill chain (ukc),” 2019. [On-line]. Available: https://www.csacademy.nl/images/scripties/2018/Paul-Pols — -The-Unied-Kill-Chain.