Uncovering the Hidden Risks Of Cybersecurity
By Tom Seest
What Lies Beneath the Surface Of Cybersecurity?
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Cybersecurity practitioners refer to covert information as data that is not intended for public view or sharing. This type of data can be exploited to circumvent security measures.
Criminals typically create covert channels by exploiting malicious programs with administrator access to a system, as well as different communication protocols and shared computing/storage resources.
What Lies Beneath the Surface Of Cybersecurity?
Table Of Contents
Uncovering the Secrets of Covert Channels
A covert channel is a communication channel that can be used to send or receive information in an undiscretionary fashion, circumventing nondiscretionary security policies. This may occur due to errors in the interpretation or TCB specification of a security policy model; such channels could range from fundamental (Examples 3 and 4) to specific (Examples 1 and 2).
Fundamental channels are defined as “flaws in the specification of a TCB or interpretation of security policy models in any operating system that cause covert communication.” Unfortunately, the term “covert communication” isn’t included in DeFinition 5, which does not differentiate between fundamental channels and interpretation/TCB specification flaws. However, by focusing on model implementation it may help distinguish different types of covert channels or flaws in security policy models.
Alternatively, you may identify potential covert channels by performing syntactic flow analysis on formal specifications or source code. Several tools have been developed that apply this technique to formal specifications and code; examples include SRI Hierarchical Development Methodology (HDM) [Feiertag80, Rushby84] and Enhanced HDM (EHDM)[Feiertag80, Eckmann87], Ina Flo tool of Formal Development Methodology (FDM)[Eckmann87] and Gypsy tools[McHugh and Good85 and McHugh & Good85 and McHugh and Ackers87].
It is essential to note that a potential covert channel does not always originate from an illegal flow. Most systems allow these activities to occur naturally over time; for instance, one process can alter UNIX “rmdir” variables to indicate it no longer an empty directory, while another processes may discover files or directories inside that upgraded directory and transmit some information back to their original user process.
Flow conditions are one of the simplest and most accurate methods for covert channel identification, though it has some practical drawbacks. For instance, it cannot be applied to systems where formal specifications or source code analysis isn’t necessary, such as class B2-B3 systems; additionally, manual application requires more effort than automated tools for large systems.
Uncovering the Secrets of Covert Channels
Uncovering the Secrets of Noisy Channels in Cybersecurity
A noisy channel is a type of communication medium that may introduce errors in the transmission of data. These errors could lead to corrupted or missing packets. Utilizing an optimized protocol can reduce these risks and guarantee your communications arrive intact.
Noisy Channel protocol is a special type of communication technology developed to combat the issues caused by noise on an Internet connection. It comprises various components, such as flow control, error detection and correction – with one notable exception: stop-and-wait.
The best part is that it works on any network, even those without ethernet or other bandwidth-intensive communication protocols. Its small footprint and cost efficiency make it the ideal solution for cybersecurity, IT, data protection and telephony applications alike.
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Uncovering the Secrets of Noisy Channels in Cybersecurity
Uncovering the Secrets of Special Covert Channels?
Covert channels are special forms of information-transfer mechanisms that can potentially leak sensitive data to untrusted processes. They exist across a variety of operating systems and rely on using variables not typically seen as objects or data variables. Often internal to the kernel and/or trusted processes, these variables’ security levels change dynamically as flows between labeled objects (e.g., files or directories) take place.
Therefore, all syntactic flow-analysis methods that rely on assigning variables specific security levels and access classes may miss illegal flows involving covert channels. This issue is especially acute for systems interpreting nondiscretionary security models.
One way to eliminate many covert channels is to disable dynamic sharing of resources and preallocate all objects in a shared memory area on an individual security-level basis. Unfortunately, this method requires extensive code modifications and may lead to performance losses.
Another way to reduce the bandwidth of a covert channel is to synchronize the sender and receiver processes. On UNIX systems this can be accomplished through group scheduling of senders and receivers when multiple processors are available. Furthermore, understanding how synchronization works between them helps determine what minimum delay must be achieved.
The performance of covert channel primitives is significantly affected by the amount of time taken to initialize the TCB environment for altering and viewing data variables. This is especially true for channels that include both altering and viewing functions, since those same functions may take different amounts of time when set or read from a variable as opposed to viewing its value.
Unfortunately, it is difficult to determine a minimum delay that can be achieved in all circumstances without thoroughly analyzing each case. Nonetheless, most channels of interest in practice can be represented as two-state graphs as shown in Figure 4-1.
No matter whether they are noiseless or noisy, most covert channel scenarios in practice can be modeled as a finite-state machine if one makes the assumption of zero time lag and no other processes other than sender and receiver present during operation. On the other hand, if multiple processors are available for synchronization, parallel aggregation of sender-receiver pairs can be done without negatively affecting channel bandwidth.
Uncovering the Secrets of Special Covert Channels?
Uncovering the Secrets of Real-Time Covert Channel Detection Systems?
Real-time covert channel detection systems are computer systems designed to identify and monitor covert channel traffic. These channels, undetectable to legitimate traffic, often carry sensitive information like credit card data or personal medical records.
Accuracy in detecting covert channels is determined by observing their inter-arrival times, or the intervals between packet arrivals. A variety of techniques have been developed to detect covert channels, including machine learning and statistical features. In one recent study, deep neural networks were employed to identify and detect covert timing channels, and a decision tree classifier was utilized for detection purposes.
Typically, covert channels have a bandwidth limit (i.e., they can transmit only so much data in one unit of time) defined by the TCP/IP Security Policy Framework and varying between different types of covert channels. Thermal covert channels tend to have the most limited bandwidth requirements.
Another type of covert channel is a transient one, which sends only a fixed amount of data before ceasing to exist. Although this type of channel is the most prevalent, it can be harmful as it allows an attacker to leak sensitive information that may later be recovered or analyzed.
For example, a covert channel could be utilized to leak the password of an account payable application to an untrusted user logged into a system with lower integrity levels. If this password is known to an attacker, they could utilize it to take control of the accounts payable application and produce checks for questionable purposes.
These channels are highly vulnerable, as they allow an attacker to circumvent a system’s security policy and potentially expose sensitive data. Unfortunately, it can be difficult to identify these channels.
Furthermore, these channels can be challenging to track and detect due to the fact they often utilize variables not commonly viewed as data objects. For instance, a channel might use an array of synchronization variables in order to establish and maintain communication between two processes.
Variables not part of a given nondiscretionary security model in an operating system cannot be detected through syntactic flow-analysis methods and thus cannot be exploited by an attacker to violate any nondiscretionary integrity boundary.
Uncovering the Secrets of Real-Time Covert Channel Detection Systems?
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