File Name: software test attacks to break mobile and embedded devices .zip
Designed for testers working in the ever-expanding world of smart devices driven by software, the book focuses on attack-based testing that can be used by individuals and teams. The numerous test attacks show you when a software product does not work i.
Designed for testers working in the ever-expanding world of smart devices driven by software, the book focuses on attack-based testing that can be used by individuals and teams.
With the spread of IoT, embedded devices are also connected to the Internet, which is in danger of security attacks. If a security attack stopped the control of embedded devices, substantial damage is expected. Therefore, a measure is demanded that does not lose control even if the device is attacked. If the device can immediately classify the type of attack when it experiences a security attack, it can automatically respond to the attack.
As a result, it is possible not to lose control and to minimize any damage. That is why we developed and implemented a lightweight attack detection and classification algorithm and confirmed that it was possible to be run in real time on embedded devices. Following the penetration of IoT into diverse fields, not only personal computers or servers but also various embedded devices have come to be connected to the Internet. As a result, embedded devices are now exposed to security attack risks in much the same way as personal computers or servers have been.
The above is particularly true with embedded devices, such as automotive vehicle control units and controllers for factory machines. These embedded devices that perform physical actions; therefore, security attacks on them may lead to serious personal and material damage. The basic principle of a security measure is to analyze security risks to a target system in the identification phase and protect the target system from security attacks in the protection phase.
Security attacks are, however, in constant evolution. Attacks able to break through the protection will emerge sooner or later. What then becomes necessary are the detection of security attacks, including ones that broke through the protection and an appropriate response to the detected attack for damage minimization. This paper presents a report on the development and implementation of a detection algorithm designed to run in real time on embedded devices, even on low-resource embedded devices, and minimizes damage through responses.
A proper response to a detected attack requires determination of the nature of the attack. This requirement aims to select appropriate countermeasures for the types of attacks encountered. Attack detection and classification are supposed to occur simultaneously. In the case of control equipment for machines that perform physical actions, any problem that may occur to it must be addressed without delay to prevent damage to nearby personnel and the machinery itself.
The same holds for responses to security attacks. Hence, the detection and classification function needs to be executable in real time. Accordingly, the challenge herein is to develop a method of executing the attack detection-and-classification function on an embedded device in real-time. Security measures at the embedded device level are just beginning to emerge.
Particular note should be taken that no products exist that can execute the attack detection-and-classification function in real time. Meanwhile, some IT security software products for personal computers, servers, and other computing equipment can execute the attack detection-and-classification function in real time.
In IT attack detection and classification technologies, very minute details are identified, including, for example. Some technologies use both machine learning and AI for detecting unknown attack methods, and hence need massive amounts of data along with massive amounts of computation for data retrieval.
AppGuard is software that runs on the Windows series of operating systems. One of its selling points is that it runs very light. Though considerably light for an application for Windows, AppGuard is well beyond the specs of embedded devices. This platform is far from an environment that allows direct import of the technology of AppGuard.
This technology is intended for embedded devices used as control units and is designed to perform attack detection and classification on embedded devices in real time. A security attack on an embedded device comes from an external source. In other words, external inputs include anomalous ones. Our basic idea of attack detection is based on this sequence of events. The combination of anomalies that constitute an attack depends on the type of attack.
Therefore, the detection of a specific combination of anomalies means that of a specific attack. Such detection of specific attacks for each type of attack of interest leads to the simultaneous execution of attack detection and classification.
This algorithm only judges simple combinations of occurrences and non-occurrences of anomalies and seems to meet the two requirements of high-speed processing capability and low required data capacity, which are imposed by the hardware limitations of embedded devices.
Table 1 shows the six types of attacks to be classified. Moreover, when coming under a complex attack consisting of multiple attacks, the embedded device must be able to determine the priority order for dealing with the attacks. Therefore, we modeled the progress of an attack as an irreversible state transition process and proposed an approach that deals with attacks in the descending order of their progress.
The technology presented in Chapter 4 classifies attacks into six types. The six-type classification is, however, still insufficient to achieve the purpose of making appropriate actions for the types of attacks encountered.
The action to be taken for, for example. Hence, attacks should be more finely distinguished to be reliably detected and classified. The attack detection-and-classification algorithm explained in Chapter 4 assumes that the anomalies to be detected are external inputs.
Meanwhile, however, as shown by the example given in Section 5. We added this finer classification to the attack state-transition diagram in Fig. Being a lower resource among embedded devices and, as explained in Chapter 1, exposed to security attack risks, in-vehicle ECUs are suitable for the technology presented herein to be applied to.
Table 2 shows the hardware specifications. This hardware includes an in-vehicle one-chip microcontroller as its CPU and has a multiple number of channels compliant with CAN, one of the standard in-vehicle network protocols. Additionally, the hardware performs transmission and reception of specified messages as its intended function for ECUs. The attack classification algorithm developed this time makes separate judgments on attacks based on their types and hence can perform high-speed processing by bit operations.
As explained in Chapter 4, in connection with attack classification, attacks are modeled as irreversible state transitions. Hence, the algorithm judges that a certain attack A has occurred when the following conditions are both met: all transition conditions leading up to A are met IN Conditions , and none of the conditions for the transition from A to the next are met OUT Conditions. The data set with a value of 1 for the bit corresponding to the anomaly constituting an IN Condition and a value of 0 for the other bits in a bit string is called an IN Condition bitmask.
An OUT Condition can similarly be expressed using a bitmask. If for a detected anomaly, a bit string is available with the value of the bit for the detected anomaly being 1 and that of other bits being 0, the algorithm can tell whether an IN or an OUT Condition has occurred by performing a single AND operation with each of their respective bitmasks.
In other words, two bit operations suffice to determine that a single attack has been detected. Then, for the algorithm thus implemented, the CPU computational load and the data capacity need to be estimated.
Let us here estimate the CPU computational load. Similarly, let us estimate the required data capacity. For the model intended for in-vehicle ECUs, we designed and developed a security measure. Using this security measure, we identified the attacks to be classified and the anomalies to be detected for attack detection.
As a result, the number of types of attacks to be classified increased to Table 3 and Fig. Let us apply these functional specifications, together with the hardware specifications in Section 6. The parameters and in Section 6. The estimated CPU computational load is 44 bit operations.
From the above, our algorithm is expected to be implementable in embedded devices, such as in-vehicle ECUs, with no problem with the computational load and the data capacity. The requirements for the attack detection-and-classification function are defined as follows:. The points of attack corresponding to the test case numbers in Table 4 are indicated by the numbers in the balloons in the figure.
Table 5 shows the results of running the test cases in Table 4 with the system configuration in Fig. For each of Test Cases Nos. Although some types of attacks were first detected as another type of attack, they were eventually detected and classified as the expected attack, hence resulting in no problem. The elapsed times for Cases Nos.
The above results confirm that the implemented attack detection-and-classification function correctly worked as supposed to. This section first shows the confirmed results on the memory usage. The flash ROM capacity used by the attack detection-and-classification function is the total of the data capacity required for the bitmasks shown in Section 6. The RAM capacity to be used is the amount of space required to save the execution state and is statically reserved.
Table 6 shows the results of memory usage measurement. Thus, the requirements are met. The confirmed results on the processing time are as follows: We measured the processing time by observing the digital signal waveforms using the software we modified so that a digital signal output would occur at each switchover of the internal processing.
The modification affected the processing time only to a negligible degree. Even when the maximum processing time was reached and hence the communication load was percent, the requirement of 1 ms or less was met with a safe margin.
These results confirm that the implemented attack detection-and-classification function has performance sufficient to run in real time without disrupting the inherent function of the embedded device.
From the above, we believe that the algorithm implemented this time is a practically feasible technical solution to detect and classify attacks without compromising the inherent function of even a low-resource embedded device. As a solution to the challenge of performing attack detection and classification on embedded devices in real time, we developed a high-speed, lightweight attack detection-and-classification algorithm able to run on embedded devices.
Additionally, we improved the attack detection-and-classification algorithm for a finer classification of attacks and the proper selection of appropriate actions.
Moreover, we implemented the developed algorithm in actual embedded-device hardware to run functional and performance tests. The test results have shown that the algorithm has performance sufficient to run in real time while achieving a finer classification of attacks. With this technology implemented in an embedded device, all the necessary steps up to emergency response actions would be taken immediately after the encounter with a security attack.
Moreover, this technology allows the fine classification of attacks down to an appropriate degree of granularity and hence enables automatic selection and execution of an emergency response action matching the type of attack encountered. When, in the future, we can set appropriate emergency response actions for the types of attacks, we will be able to minimize damage from security attacks on embedded devices. The development presented here was intended for implementation in in-vehicle ECUs and hence was customized to the needs of in-vehicle ECUs with the focus mainly on the anomaly detection method and scheduling.
Facebook Twitter. Table 1 Types of attacks to be classified Type of attack Example Spoofing Sending in fake control commands disguised as an authentic device Tampering Tampering firmware and configuration data Repudiation Erasing or overwriting logs to erase the traces of intrusion Information disclosure Acquiring confidential information, such as special communication commands or configuration data Denial of service Sending a huge amount of communication messages to a system to force the system out of service Elevation of privilege Illicitly obtaining a privileged access right.
Table 3 Specifications for the attack detection-and-classification function Item Specification Types of detectable and classifiable attacks 11 Types of anomalies used for attack detection and classification
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Some people also refer to this as the internet of everything, but we will stick with IoT. Well in IoT the basic idea is to take every day physical objects, devices, systems, and systems of systems and connect them with software as well as communications to the internet. The obvious starting point has been existing electronic devices since these object already had power, which is necessary to support computers and communication.
Embedded systems security provides mechanisms to protect an embedded system from all types of malicious behavior. Embedded systems security is a cybersecurity field focused on preventing malicious access to and use of embedded systems. Embedded systems security provides mechanisms to protect a system from all types of malicious behavior.
Jon Hagar Jon Hagar is a systems-software engineer and tester consultant supporting software product integrity and verification and validation, with a specialization in embedded and mobile software systems. For more than thirty years Jon has worked in software engineering, particularly testing, supporting projects including control system avionics and auto , spacecraft, mobile-smart devices, IT, and attack testing of smart phones. Definitions and introductions will help here Why do we test and what do we need to test? Examples Avionics systems: planes, cars, rockets, military,..
With the spread of IoT, embedded devices are also connected to the Internet, which is in danger of security attacks. If a security attack stopped the control of embedded devices, substantial damage is expected. Therefore, a measure is demanded that does not lose control even if the device is attacked.
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Беккер проехал уже половину пути, когда услышал сзади металлический скрежет, прижался к рулю и до отказа открыл дроссель. Раздался приглушенный звук выстрела. Мимо. Он резко свернул влево и запетлял по дороге в надежде сбить преследователя и выиграть время. Все было бесполезно.
Беккер зашагал по комнате. - На руке умершего было золотое кольцо. Я хочу его забрать. - У м-меня его. Беккер покровительственно улыбнулся и перевел взгляд на дверь в ванную.
Беккер закрыл глаза, стиснул зубы и подтянулся. Камень рвал кожу на запястьях.
И в следующее мгновение не осталось ничего, кроме черной бездны. ГЛАВА 102 Стратмор спустился на нижний этаж ТРАНСТЕКСТА и ступил с лесов в дюймовый слой воды на полу. Гигантский компьютер содрогался мелкой дрожью, из густого клубящегося тумана падали капли воды. Сигналы тревоги гремели подобно грому.
Люди на экране вроде бы сидели в каком-то автобусе, а вокруг них повсюду тянулись провода. Включился звук, и послышался фоновой шум. - Установлена аудиосвязь. Через пять секунд она станет двусторонней.
И в тот же миг ей открылась ужасающая правда: Грег Хейл вовсе не заперт внизу - он здесь, в Третьем узле. Он успел выскользнуть до того, как Стратмор захлопнул крышку люка, и ему хватило сил самому открыть двери. Сьюзан приходилось слышать, что сильный страх парализует тело, - теперь она в этом убедилась. Ее мозг мгновенно осознал происходящее, и она, вновь обретя способность двигаться, попятилась назад в темноте с одной только мыслью - бежать. И сразу же услышала треск.
Компания получает электронные сообщения, адресованные на подставное имя, и пересылает их на настоящий адрес клиента. Компания связана обязательством ни при каких условиях не раскрывать подлинное имя или адрес пользователя. - Это не доказательство, - сказал Стратмор.
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Address Errors before Users Find ThemUsing a mix-and-match approach, Software Test Attacks to Break Mobile and Embedded Devices presents an attack.