Intelligent intrusion detection in low power IoTs

Security and privacy of data are one of the prime concerns in today’s Internet of Things (IoT). Conventional security techniques like signature-based detection of malware and regular updates of a signature database are not feasible solutions as they cannot secure such systems effectively, having limited resources. Programming languages permitting immediate memory accesses through pointers often result in applications having memory-related errors, which may lead to unpredictable failures and security vulnerabilities. Furthermore, energy efficient IoT devices running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate IoT in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e., sensor nodes and base station) is destabilised by the attackers. In this article, we have presented an intrusion detection and prevention mechanism by implementing an intelligent security architecture using random neural networks (RNNs). The application’s source code is also instrumented at compile time in order to detect out-of-bound memory accesses. It is based on creating tags, to be coupled with each memory allocation and then placing additional tag checking instructions for each access made to the memory. To validate the feasibility of the proposed security solution, it is implemented for an existing IoT system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node within the system operating range and anomalous activity in the base station with an accuracy of 97.23%. Overall, the proposed security solution has presented a minimal performance overhead.</jats:p

What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing

Driven by new software development processes and testing in clouds, system and integration testing nowadays tends to produce enormous number of alarms. Such test alarms lay an almost unbearable burden on software testing engineers who have to manually analyze the causes of these alarms. The causes are critical because they decide which stakeholders are responsible to fix the bugs detected during the testing. In this paper, we present a novel approach that aims to relieve the burden by automating the procedure. Our approach, called Cause Analysis Model, exploits information retrieval techniques to efficiently infer test alarm causes based on test logs. We have developed a prototype and evaluated our tool on two industrial datasets with more than 14,000 test alarms. Experiments on the two datasets show that our tool achieves an accuracy of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per cause analysis. Due to the attractive experimental results, our industrial partner, a leading information and communication technology company in the world, has deployed the tool and it achieves an average accuracy of 72% after two months of running, nearly three times more accurate than a previous strategy based on regular expressions.Comment: 12 page