So far, we have not yet found the satisfied answers of several problems, as indicated in Chapter 3, Chapter 4 and Chapter 5. Here we list several typical challenges as follows:
Challenge #1: The privacy preservation in a complex environment like IoT is never an easy task and will continue to be an Achilles heel of the safe Internet Privacy preservation is not only a problem of user information breach due to the security attacks but also potentially abused by the security defense applications themselves. In our research, unfortunately, the detection engines still require the user identity or linkability to track and analyze the behavior of a potential attacker. However, that can accidentally make them become tracking tools, if the attacker can control such systems. Therefore, a big challenge in security defense is to protect the user information against the potential exploitation, and even the security protection applications have no exception.
Specifically, for security services such as misbehavior detection to deter user privacy leakage, the accessing service types should be protected against curious MEC apps or outside attackers. The mapping of allowed credentials and configured slice types, i.e., part of the configured/allowed certificates applicable for the subscribed services, should be learned by fog nodes (owned by the network operator) to select proper slices for packet forwarding or redirection to the proper MEC apps. Note that a requirement for later resolution of law enforcement can remarkably complicate the efforts of proposing a robust security defense, including misbehavior detection, which is strict with privacy issues.
Challenge #2: Energy depletion attacks in IoT low-power networks
Per our summary in [8], various EDA patterns are common to LPW technologies, each of which is vulnerable to at least one of the variants of EDAs. Besides the lack of strong security schemes and a holistic defense tactic, diversity of exploitation tools and
vulnerabilities also make the system easily attacked by EDAs. However, the aforementioned attacks and defenses on new and dominant LPW technologies such as NB-IoT have not yet thoroughly been addressed. For example, the potential influence of the vulnerabilities is inherited from conventional LTE networks, and therefore this is a potential topic of concern for further study. Moreover, the EDAs in the PHY/MAC layers are the most effective and proven to bring serious damage to LPW technologies. Therefore, thorough research on these layers for dominant LPW technologies, even only through the technical specification and protocol standards, can reveal unknown issues. Continuing on this topic, using the standard Dolev-Yao attacker model [127] and tools such as ProVerif[128] to verify the vulnerabilities of security protocols in such technologies can be also another promising approach.
Conducting EDAs and exploiting relevant vulnerabilities in IoT heterogeneous networks are also challenging. In the future, heterogeneous implementations for various technologies, including LPW devices from various providers, may introduce potential vulnerabilities such as bugs of firmware, defects of hardware and flaws in the protocol design. Potentially valuable work is to assess commercial LPW products, including IoT platforms and firmware, to get a clearer view of the security weaknesses and whether or not the vulnerabilities as mentioned earlier have been fixed. The quantifiable results are preferred in all cases. The assessment results may reveal many interesting results, e.g., whether all products have been updated with the highest security standards, or the manufacturers may pull out several required security features due to the cost or are incredibly careless in initializing the default values for the security setting of their products.
For EDA defense solutions, prior proposals have simulated promising results, mainly in specific contexts, but that does not mean they are deployed in practice for certain reasons, e.g., infeasible to upgrade LPW deployed devices’ firmware. Therefore, we conclude that deploying IDSs is a comprehensive and highly effective approach to mitigate and defeat EDAs. In such a system, the approach of on-demand anti-EDA services in the fog layer is promising. However, for the IDS-based solutions, particularly, those relying on traffic analysis, the difficulties in collecting massive IoT available data incur the major obstacles to improve the detection accuracy. Thus, generating/collecting a qualified IoT security dataset (e.g., energy usage, attack log), no matter where it comes – a particular IoT industry (critical infrastructures such as a smart city) or a generic source – will be beneficial for the research community.
For EDA evaluation, the preferable scenario is to use real devices and a large topology as possible. However, there may be obstacles to do that (e.g., due to a limited budget on the project). In this context, simulation is a natural option. Although simulation/emulation has significant benefits (e.g., easy to deploy a large-scale network model), the main obstacle is the lack of implementations of fundamental components such as the energy and error model in the physical and MAC layers of LPWAN technologies, e.g., NB-IoT. Therefore, we believe that a simulation implementation for new technologies (e.g., an NB-IoT module for NS3 [129]) relying on a reliable power consumption/error model will potentially be a significant contribution to the community.
Finally, the future of the Internet may rely on the connectivity of both LPW networks and next-generation networks such as 5G [130]. In a connected world, EDAs can be initiated from not only equipment connected to LPW networks but also traditional network devices.
Hence, a promising approach is to exploit vulnerabilities of interoperability communication schemes or weak links in the network to launch attacks, including EDAs. For example, a remote application may control inter-operable IoT gateways. An attacker can find an indirect approach to obtain the privilege to control these gateways by exploiting known vulnerabilities of the computer that installed the remote program. For LPWAN such as LoRa, massive devices connected with a centralized server prove the feasibility of this approach. If the adversary can compromise the application server, he can easily adjust the duty-cycle of the whole end-devices in the network. Although a strong and secure communication method may solve the problem; however, such a mechanism for interoperability communication among network technologies with various characteristics are under development and unlikely to be done soon. A MEC-based defense accommodates with traffic filtering features for IoT devices at 5G mMTC slice could be a starting point.
Challenge #3: An interoperable and scalable security solution, e.g., misbehavior detection, for V2X to work in the environment with and without 5G coverage
We believe that several technologies for both 5G NR and C-V2X promise to be partially deployed and more accessible to end-users in the next three years. For instance, an autonomous vehicle with Advanced Driver Assistance Systems (ADAS) will be capable of sensing the surrounding environment and navigating without human input. However, that does not mean the complexity of security protection for the V2X system will be decreased. In contrast, the booming data from a large number of such sensors and assistive
components of V2X can put pressure on data acquisition and fusion, i.e., more data for prediction in misbehavior detection. Moreover, due to the economic interests and a large investment of 5G infrastructure, the network operators may prefer a gradual replacement of 5G, while the enhancement versions of LTE networks such as LTE-A may still serve well major requirements of many users. In this context, as our research pointing out, interoperable and scalable security solutions, e.g., misbehavior detection, for V2X to work in the environment with and without 5G coverage is absolutely vital and so far there are still lots of work to obtain that.
Challenge #4: A unified solution for verifying the attackers in the era of autonomous driving moving on the roads with regular civilian cars
In the future, the regular civilian cars will move along with autonomous vehicles for a certain period of transition time. In this case, detecting the attacker in this environment is much more complicated, since the road is mixed up with various moving behaviors, including the irregular steering changes of the human drivers, e.g., drunk or sleeping. To realize autonomous driving, including the misbehavior detection model, it is required to enrich the cases in which the vehicle can recognize the approaching threats and then trigger a safe action. To the best of our knowledge, there is little research on this topic to date.
Last but not least, while deep learning and AI crept into every aspect of academic research and the dominant topics for years, we believe that conducting such approaches in human- related applications like autonomous driving still needs many efforts and years, since an accident of incomplete techniques can cost the reputation of the whole industry and cast doubt on the reliability of the relevant technologies.
Appendices
Appendix
Illustration of 5G Authentication and 5G beamforming analysis
Figure A.1: The same usage of uplink TEID in control data and uplink packets in the initial stage of 5G authentication reinforces our theory to verify the spoofing sources in 5G networks.
Figure A.2: Channel beamspace in 5G with multiple path interference existence.
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