As the Internet of Things expands, the need to embed artificial intelligence algorithms into resource-constrained devices for real-time applications is growing. These systems require efficient and scalable algorithms to perform rapid and autonomous decision-making without relying on centralized cloud servers. Hyperdimensional Computing (HDC) has recently emerged as a compelling paradigm for learning tasks in such environments, offering computational efficiency, exceptional parallelism, and scalability.In this work, we present HD-CBBIN, a lightweight and efficient implementation of the HD-CB framework for modeling and automating sequential decision-making Contextual Bandits (CB) problems on embedded systems. By introducing modifications to the original algorithm, HD-CBBINexclusively uses binary hypervectors, significantly reducing computational demands. We benchmark the performance of HD-CBBINon synthetic datasets, comparing it to the real-valued counterpart and the traditional state-of-the-art LinUCB algorithm. We also evaluate its execution time, computational complexity, and memory requirements on various embedded platforms and demonstrate additional gains through hardware acceleration. The results show that our approach achieves linear execution time with respect to the context vector size and up to a 141× speedup over LinUCB while maintaining competitive performance, establishing a new milestone in contextual bandit algorithms for time-critical, resource-constrained applications.

This work presents a methodology to improve machine-generated signatures for Android Malware detection. The technique relies on a population-less evolutionary algorithm and uses an unorthodox fitness function that incorporates unsystematic human expert knowledge in the form of a set of rules of thumb. The proposed optimization algorithm does not require to rank the individuals and the resulting population of candidate solutions is not a totally ordered set. Experimental results show that the optimized signatures are more accurate than the original ones, lowering both false positives and false negatives.

During the last decade, network providers are faced by a growing problem regarding the distribution of bandwidth and computing resources. Recently, the mobile edge computing paradigm was proposed as a possible solution, mainly in consideration of the provided possibility of transferring service demands at the edge of the network. This solution heavily relies on the dynamic allocation of resources, depending on the user needs and network connection, therefore it becomes essential to correctly predict user movements and activities. This paper proposes an unsupervised methodology to define meaningful user locations from noninvasive user information, captured by the user terminal with no computing or battery overhead. The data is analyzed through a conjoined clustering algorithm to build a stochastic Markov chain to predict the users’ movements and their bandwidth demands. Such a model could be used by network operators to optimize network resources allocation. To evaluate the proposed methodology, we tested it on one of the largest public community’s labeled mobile and sensor dataset, developed by the “CrowdSignals.io” initiative, and we present positive and promising results concerning the prediction capabilities of the model.

New threats, grouped under the name of hardware attacks, became a serious concern in recent years. In a global market, untrusted parties in the supply chain may jeopardize the production of integrated circuits with intellectual-property piracy, illegal overproduction and hardware Trojan-horses (HT) injection. While one way to protect from overproduction is to encrypt the design by inserting logic gates that prevents the circuit from generating the correct outputs unless the right key is used, reducing the number of poorly-controllable signals is known to minimize the chances for an attacker to successfully hide the trigger for some malicious payload. Several approaches successfully tackled independently these two issues. This paper proposes a novel technique based on a multi-objective evolutionary algorithm able to increase hardware security by explicitly targeting both the minimization of rare signals and the maximization of the efficacy of logic encryption. Experimental results demonstrate the proposed method is effective in creating a secure encryption schema for all the circuits under test and in reducing the number rare signals on six circuits over nine, outperforming the current state of the art.

Heap exploits are one of the most advanced, complex and frequent types of attack. Over the years, many effective techniques have been developed to mitigate them, such as data execution prevention, address space layout randomization and canaries. However, if both knowledge and control of the memory allocation are available, heap spraying and other attacks are still feasible. This paper presents HAIT, a memory profiler that records critical operations on the heap and shows them graphically in a clear and comprehensible format. A prototype was implemented on top of Triton, a framework for dynamic binary analysis. The experimental evaluation demonstrates that HAIT can help identifying the essential information needed to carry out heap exploits, providing valuable knowledge for an effective attack.