Millions Of Devices Using LoRaWAN Exposed To Hacker Attacks [NEW]
Generally, the following techniques can be considered to prevent or slow down hardware related attacks as the first layer of the defense; obfuscation: data (deletion or nulling out, encryption, masking, substitution, shuffling and other complex techniques, etc.), layout (by using 3D stacking and complex architectural design to protect chip integrity) and metal mesh (to prevent probing attacks). Finally, using physical shields to protect hardware from outside noises to prevent fault injection attacks. Table 3 shows security risk analysis for hardware cyber-threats vs. common security properties for fog-computing based IoT end-devices including attack vector classification [40]. For instance, a security risk analysis of rogue end-device (i.e., a captured end-device by the adversaries) with respect to common security properties is as follows: C has moderate, I has moderate, A has moderate, and Auth* has significant impact. The associated attack vector is In or S.
Millions of Devices Using LoRaWAN Exposed to Hacker Attacks
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3. Maintain your devices. Update software on all devices & apps every week. Manufacturers regularly release software updates with security fixes that improve the security of their devices. A highly common tactic used by hackers is to wage large campaigns based on known vulnerabilities, attempting to break in anywhere where the updates have not been installed. By regularly updating your software, you can eliminate the risk posed by such attacks altogether.
5. Separate your devices. Hackers will often use unsecured devices as an "in" through which to breach other parts of a network. Maintaining a dedicated IOT network or a specific IOT only wireless network at home is a way to prevent hackers from using a hacked device as a foot in the door.
Moreover, Wagner et al. presented TIM, which is a large-scale flexible and transportable robotic timber construction platform [253]. TIM is location independent, reconfigurable and rapidly integrated, offering higher levels of quality and productivity. However, it lacked the security level testing, and requires further testing performance-wise. Diab et al. presented SkillMaN as a planning and execution framework using a module with experiential knowledge to integrate perception, planning, knowledge-based reasoning, and to execute various skills such as robot trajectories [254]. However, further study is also required from a cyber-security perspective. Choi et al. presented to recover robotic vehicles (RVs) from various (physical) sensor attacks, using a technique that builds a predictive state-space model based on the generic system identification technique and using sensor measurement prediction [255]. Upon attack, sensors can isolate and recover the compromised sensors to prevent further damage. The experimental results, conducted on a quad-rotor and a rover, reveal the ability to safely recover the vehicle from various attacks and prevent crashing. Beaudoin et al. presented an original software/hardware solution to obtain a universal low-level architecture for agile and easily replicable close-range remote sensing robots in different environments and on different platforms (land, surface, submarine and air) [256]. Beaudoin et al. also discussed the wise choice of Ardupilot as an autopilot and presented the ESP32 as an effective new hardware solution in terms of price and energy consumption. The experimental results revealed the easiness of tracking and achieving levels of autonomy except for flying devices. Huang et al. presented ScatterID as a lightweight system that attaches feather-light and battery-less back-scatter tags to single-antenna robots to overcome Sybil attacks [257]. The experimental results on the iRobot Create platform reveal a 96.4% accuracy level for identity verification.