Goto

Collaborating Authors

Results


Three Things to Consider in Emerging AI and ML Cybersecurity Landscape

#artificialintelligence

Cyber threats continue to escalate in both sophistication and volume. Traditional approaches to threat detection, however, are no longer sufficient to ensure protection. Correspondingly, machine learning (ML) has proven highly effective at identifying and warding off cyber attacks. Machine learning's power is the result of three factors: data, compute power and algorithms. Due to its very nature, the cyber field produces substantial amounts of data.


Thwarting Side-Channel Attacks

Communications of the ACM

The same attributes that give deep learning its ability to tell images apart are helping attackers break into the cryptoprocessors built into integrated circuits that were meant improve their security. The same technology may provide the tools that will let chip designers find effective countermeasures, but it faces an uphill struggle. Side-channel attacks have been a concern for decades, as they have been used in the hacking of smartcard-based payment systems and pay-TV decoders, as well as in espionage. Yet the rise of Internet of Things (IoT) and edge systems and their use in large-scale, commercially sensitive applications makes such attacks a growing worry for chipmakers. The innate connectivity of IoT devices means success in obtaining private encryption keys from them may open up network access on cloud-based systems that rely on their data.


Limitations

#artificialintelligence

AI algorithms namely machine learning and deep learning algorithms are powerful tools. However, they suffer from some limitations which require that human analytics should work with AI tools collaboratively. In this post, we will look at the most important shortcoming of Artificial Intelligence in the cybersecurity domain. Though Benefits are more, AI also comprises limits [4]. Cybercriminals are creative and come up with new ways to conduct cyberattacks.


When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network

arXiv.org Artificial Intelligence

Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e.g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes. Thus, we first propose an intelligent ultra-dense edge computing (I-UDEC) framework, which integrates blockchain and Artificial Intelligence (AI) into 5G ultra-dense edge computing networks. First, we show the architecture of the framework. Then, in order to achieve real-time and low overhead computation offloading decisions and resource allocation strategies, we design a novel two-timescale deep reinforcement learning (\textit{2Ts-DRL}) approach, consisting of a fast-timescale and a slow-timescale learning process, respectively. The primary objective is to minimize the total offloading delay and network resource usage by jointly optimizing computation offloading, resource allocation and service caching placement. We also leverage federated learning (FL) to train the \textit{2Ts-DRL} model in a distributed manner, aiming to protect the edge devices' data privacy. Simulation results corroborate the effectiveness of both the \textit{2Ts-DRL} and FL in the I-UDEC framework and prove that our proposed algorithm can reduce task execution time up to 31.87%.


Three Things to Consider in the Emerging AI and ML Cybersecurity Landscape

#artificialintelligence

Cyber threats continue to escalate in both sophistication and volume. Traditional approaches to threat detection, however, are no longer sufficient to ensure protection. Correspondingly, machine learning (ML) has proven highly effective at identifying and warding off cyber attacks. Machine learning's power is the result of three factors: data, compute power and algorithms. Due to its very nature, the cyber field produces substantial amounts of data.


Project STAMINA Uses Deep Learning for Innovative Malware Detection - AnalyticsWeek

#artificialintelligence

You're familiar with the phrase, "A picture is worth 1,000 words." Well, Microsoft and Intel are applying this philosophy to malware detection--using deep learning and a neural network to turn malware into images for analysis at scale. The post Project STAMINA Uses Deep Learning for Innovative Malware Detection appeared first on TechSpective .


Project STAMINA Uses Deep Learning for Innovative Malware Detection - Security Boulevard

#artificialintelligence

You're familiar with the phrase, "A picture is worth 1,000 words." Well, Microsoft and Intel are applying this philosophy to malware detection--using deep learning and a neural network to turn malware into images for analysis at scale. Project STAMINA--an acronym for STAtic Malware-as-Image Network Analysis--converts malware samples into two-dimensional grayscale images that can be analyzed based on their unique criteria. Researchers from the two companies have worked together to develop this interesting approach to malware detection. STAMINA uses deep learning--a type of machine learning designed to create an intelligent system capable of learning on its own from unstructured and unlabeled input data.


Physically Unclonable Functions and AI: Two Decades of Marriage

arXiv.org Artificial Intelligence

The current chapter aims at establishing a relationship between artificial intelligence (AI) and hardware security. Such a connection between AI and software security has been confirmed and well-reviewed in the relevant literature. The main focus here is to explore the methods borrowed from AI to assess the security of a hardware primitive, namely physically unclonable functions (PUFs), which has found applications in cryptographic protocols, e.g., authentication and key generation. Metrics and procedures devised for this are further discussed. Moreover, By reviewing PUFs designed by applying AI techniques, we give insight into future research directions in this area.


A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations

arXiv.org Artificial Intelligence

Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to the serious concerns over user privacy and data security. This situation is further exacerbated by the strict government regulations such as Europe's General Data Privacy Regulations(GDPR). Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy. Thus many federated recommendation(FedRec) algorithms have been proposed to realize personalized privacy-preserving recommendations. However, existing FedRec algorithms, mostly extended from traditional collaborative filtering(CF) method, cannot address cold-start problem well. In addition, their performance overhead w.r.t. model accuracy, trained in a federated setting, is often non-negligible comparing to centralized recommendations. This paper studies this issue and presents FL-MV-DSSM, a generic content-based federated multi-view recommendation framework that not only addresses the cold-start problem, but also significantly boosts the recommendation performance by learning a federated model from multiple data source for capturing richer user-level features. The new federated multi-view setting, proposed by FL-MV-DSSM, opens new usage models and brings in new security challenges to FL in recommendation scenarios. We prove the security guarantees of \xxx, and empirical evaluations on FL-MV-DSSM and its variations with public datasets demonstrate its effectiveness. Our codes will be released if this paper is accepted.


Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy

arXiv.org Artificial Intelligence

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.