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Crypto-ransomware is a type of ransomware that encrypts the victim's files and demands a ransom to return the files. This type of attack has been on the rise in recent years, as it offers a lucrative business model for threat actors. Research into developing solutions for detecting and halting the spread of ransomware is vast, and it uses different approaches. Some approaches rely on analyzing system calls made via processes to detect malicious behavior, while other methods focus on the affected files by creating a file integrity monitor to detect rapid and abnormal changes in file hashes. In this paper, we present a novel approach that utilizes hashing and can accommodate large files and dynamically take into account the amount of change within each file. Mainly, our approach relies on dividing each file into partitions and then performing selective hashing on those partitions to rapidly detect encrypted partitions due to ransomware. Our new approach addresses the main weakness of a previous implementation that relies on hashing files, not file partitions. This new implementation strikes a balance between the detection time and false positives based on the partition size and the threshold of partition changes before issuing an alert. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop a reinforcement agent capable of effectively exploiting a specific vulnerability. Automating pentesting can reduce the cost and time of the operation. While there are existing tools like Metasploit Pro that offer automated exploitation capabilities, they often require significant execution times and resources due to their reliance on exhaustive payload testing. In this paper, we have created a deep reinforcement agent specifically configured to exploit a targeted vulnerability. Through a training phase, the agent learns and stores payloads along with their corresponding reward values in a neural network. When encountering a specific combination of a target operating system and vulnerability, the agent utilizes its neural network to determine the optimal exploitation options. The novelty of this work lies in employing Deep Reinforcement Learning in vulnerability exploitation analysis. To evaluate our proposed methodology, we conducted training and testing on the Metasploitable platform. The training phase of the reinforcement agent was conducted on two use cases: the first one has one vulnerability, and the second one has four vulnerabilities. Our approach successfully achieved the attacker's primary objective of establishing a reverse shell with a maximum accuracy of 96.6% and 73.6% for use cases one and two, respectively. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have demonstrated remarkable performance in many areas but require significant computation and storage resources. Quantization is an effective method to reduce CNN complexity and implementation. The main research objective is to develop a scalable quantization algorithm for CNN hardware design and model the performance metrics for the purpose of CNN implementation in resource-constrained devices (RCDs) and optimizing layers in deep neural networks (DNNs). The algorithm novelty is based on blending two quantization techniques to perform full model quantization with optimum accuracy, and without additional neurons. The algorithm is applied to a selected CNN model and implemented on an FPGA. Implementing CNN using broad data is not possible due to capacity issues. With the proposed quantization algorithm, we succeeded in implementing the model on the FPGA using 16-, 12-, and 8-bit quantization. Compared to the 16-bit design, the 8-bit design offers a 44% decrease in resource utilization, and achieves power and energy reductions of 41% and 42%, respectively. Models show that trading off one quantization bit yields savings of approximately 5.4K LUTs, 4% logic utilization, 46.9 mW power, and 147 μ J energy. The models were also used to estimate performance metrics for a sample DNN design. [ABSTRACT FROM AUTHOR]

In the future, automated demand response mechanisms will be used as spinning reserve. Demand response in the smart grid must be resilient to cyber-physical threats. In this paper, we evaluate the resilience of demand response when used as spinning reserve in the presence of cyber-physical threats. We quantify this evaluation by correlating the stability of the system in the presence of attacks measured by system frequency (Hz) and attack level measured by the amount of load (MW) that responds to the demand response event. The results demonstrate the importance of anticipating the dependability of demand response before it can be relied upon as spinning reserve. [ABSTRACT FROM AUTHOR]