Biblio

The security and privacy concerns along with the amount of data that is required to be processed on regular basis has pushed processing to the edge of the computing systems. Deploying advanced Neural Networks (NN), such as deep neural networks (DNNs) and spiking neural networks (SNNs), that offer state-of-the-art results on resource-constrained edge devices is challenging due to the stringent memory and power/energy constraints. Moreover, these systems are required to maintain correct functionality under diverse security and reliability threats. This paper first discusses existing approaches to address energy efficiency, reliability, and security issues at different system layers, i.e., hardware (HW) and software (SW). Afterward, we discuss how to further improve the performance (latency) and the energy efficiency of Edge AI systems through HW/SW-level optimizations, such as pruning, quantization, and approximation. To address reliability threats (like permanent and transient faults), we highlight cost-effective mitigation techniques, like fault-aware training and mapping. Moreover, we briefly discuss effective detection and protection techniques to address security threats (like model and data corruption). Towards the end, we discuss how these techniques can be combined in an integrated cross-layer framework for realizing robust and energy-efficient Edge AI systems.

Cloud computing has helped in managing big data and providing resources remotely and ubiquitously, but it has some latency and security concerns. Fog has provided tremendous advantages over cloud computing which include low latency rate, improved real-time interactions, reduced network traffic overcrowding, and improved reliability, however, security concerns need to be addressed separately. Another major issue in the cloud is Cloud Lock-in/Vendor Lock-in. Through this research, an effort has been made to extend fog computing and Searchable Encryption technologies. The proposed system can reduce the issue of cloud lock-in faced in traditional cloud computing. The SE schemes used in this paper are Symmetric Searchable Encryption (SSE) and Multi-keyword Ranked Searchable Encryption (MRSE) to achieve confidentiality, privacy, fine-grained access control, and efficient keyword search. This can help to achieve better access control and keyword search simultaneously. An important use of this technique is it helps to prevent the issue of cloud/vendor lock-in. This can shift some computation and storage of index tables over fog nodes that will reduce the dependency on Cloud Service Providers (CSPs).

Reducing latency in inter-process/inter-thread communication is one of the key challenges in parallel and distributed computing. This is because as the number of threads in an application increases, the communication overhead also increases. Moreover, the presence of background load further increases the latency. Reducing communication latency can have a significant impact on multi-threaded application performance in multi-core environments. In a wide-range of applications that utilize queueing mechanism, inter-process/ inter-thread communication typically involves enqueuing and dequeuing. This paper presents a queueing techniques called eLCRQ, which is a lock-free block-when-necessary multi-producer multi-consumer (MPMC) FIFO queue. It is designed for scenarios where the queue can randomly and frequently become empty during runtime. By combining lock-free performance with blocking resource efficiency, it delivers improved performance. Specifically, it results in a 1.7X reduction in latency and a 2.3X reduction in CPU usage when compared to existing message-passing mechanisms including PIPE and Sockets while running on multi-core Linux based systems. The proposed scheme also provides a 3.4X decrease in CPU usage while maintaining comparable latency when compared to other (MPMC) lock-free queues in low load scenarios. Our work is based on open-source Linux and support libraries.

Network slicing is considered a key enabler to 5th Generation (5G) communication networks. Mobile network operators may deploy network slices-complete logical networks customized for specific services expecting a certain Quality of Service (QoS). New business models like Network Slice-as-a-Service offerings to customers from vertical industries require negotiated Service Level Agreements (SLA), and network providers need automated enforcement mechanisms to assure QoS during instantiation and operation of slices. In this paper, we focus on ultra-reliable low-latency communication (URLLC). We propose a software architecture for security functions based on off-the-shelf hardware and open-source software and demonstrate, through a series of measurements, that the strict requirements of URLLC services can be achieved. As a real-world example, we perform our experiments using the intrusion prevention system (IPS) Snort to demonstrate the impact of security functions on latency. Our findings lead to the creation of a model predicting the system load that still meets the URLLC latency requirement. We fully disclose the artifacts presented in this paper including pcap traces, measurement tools, and plotting scripts at https://gallenmu.github.io/low-latency.

Despite the increased accuracy of intrusion detection systems (IDS) in identifying cyberattacks in computer networks and devices connected to the internet, distributed or coordinated attacks can still go undetected or not detected on time. The single vantage point limits the ability of these IDSs to detect such attacks. Due to this reason, there is a need for attack characteristics' exchange among different IDS nodes. Researchers proposed a cooperative intrusion detection system to share these attack characteristics effectively. This approach was useful; however, the security of the shared data cannot be guaranteed. More specifically, maintaining the integrity and consistency of shared data becomes a significant concern. In this paper, we propose a blockchain-based solution that ensures the integrity and consistency of attack characteristics shared in a cooperative intrusion detection system. The proposed architecture achieves this by detecting and preventing fake features injection and compromised IDS nodes. It also facilitates scalable attack features exchange among IDS nodes, ensures heterogeneous IDS nodes participation, and it is robust to public IDS nodes joining and leaving the network. We evaluate the security analysis and latency. The result shows that the proposed approach detects and prevents compromised IDS nodes, malicious features injection, manipulation, or deletion, and it is also scalable with low latency.

pubcrawl, Network on Chip Security, Scalability, resiliency, resilience, metrics, Tasks on NoC (Network-on-Chip) are less efficient because of long-distance data synchronization. An inefficient task schedule strategy can lead to a large number of remote data accessing that ruins the speedup of parallel execution of multiple tasks. Thus, we propose an energy efficient task schedule to reduce task traffic with a diffusional pattern. The task mapping algorithm can optimize traffic distribution by limit tasks into a small area to reduce NoC activities. Comparing to application-layer optimization, our task mapping can obtain 20% energy saving and 15% latency reduction on average.

One of the effective ways of detecting malicious traffic in computer networks is intrusion detection systems (IDS). Though IDS identify malicious activities in a network, it might be difficult to detect distributed or coordinated attacks because they only have single vantage point. To combat this problem, cooperative intrusion detection system was proposed. In this detection system, nodes exchange attack features or signatures with a view of detecting an attack that has previously been detected by one of the other nodes in the system. Exchanging of attack features is necessary because a zero-day attacks (attacks without known signature) experienced in different locations are not the same. Although this solution enhanced the ability of a single IDS to respond to attacks that have been previously identified by cooperating nodes, malicious activities such as fake data injection, data manipulation or deletion and data consistency are problems threatening this approach. In this paper, we propose a solution that leverages blockchain's distributive technology, tamper-proof ability and data immutability to detect and prevent malicious activities and solve data consistency problems facing cooperative intrusion detection. Focusing on extraction, storage and distribution stages of cooperative intrusion detection, we develop a blockchain-based solution that securely extracts features or signatures, adds extra verification step, makes storage of these signatures and features distributive and data sharing secured. Performance evaluation of the system with respect to its response time and resistance to the features/signatures injection is presented. The result shows that the proposed solution prevents stored attack features or signature against malicious data injection, manipulation or deletion and has low latency.

Future networks require fast information response time, scalable content distribution, security and mobility. In order to enable future Internet many key enabling technologies have been proposed such as Edge computing (EC) and Named Data Networking (NDN). In EC substantial compute and storage resources are placed at the edge of the network, in close proximity to end users. Similarly, NDN provides an alternative to traditional host centric IP architecture which seems a perfect candidate for distributed computation. Although NDN with EC seems a promising approach for enabling future Internet, it can cause various challenges such as expiry time of the Pending Interest Table (PIT) and non-trivial computation of the edge node. In this paper we discuss the expiry time and non-trivial computation in NDN based EC. We argue that if NDN is integrated in EC, then the PIT expiry time will be affected in relation with the processing time on the edge node. Our analysis shows that integrating NDN in EC without considering PIT expiry time may result in the degradation of network performance in terms of Interest Satisfaction Rate.

Fog computing provides a new architecture for the implementation of the Internet of Things (IoT), which can connect sensor nodes to the cloud using the edge of the network. This structure has improved the latency and energy consumption in the cloud. In this heterogeneous and distributed environment, resource allocation is very important. Hence, scheduling will be a challenge to increase productivity and allocate resources appropriately to the tasks. Programs that run in this environment should be protected from intruders. We consider three parameters as authentication, integrity, and confidentiality to maintain security in fog devices. These parameters have time and computational overhead. In the proposed approach, we schedule the modules for the run in fog devices by heuristic algorithms based on data mining technique. The objective function is included CPU utilization, bandwidth, and security overhead. We compare the proposed algorithm with several heuristic algorithms. The results show that our proposed algorithm improved the average energy consumption of 63.27%, cost 44.71% relative to the PSO, ACO, SA algorithms.

A low latency is a fundamental timeliness requirement to reduce the potential risks of cyber sickness and to increase effectiveness, efficiency, and user experience of Virtual Reality Systems. The effects of uniform latency degradation based on mean or worst-case values are well researched. In contrast, the effects of latency jitter, the distribution pattern of latency changes over time has largely been ignored so far although today's consumer VR systems are extremely vulnerable in this respect. We investigate the applicability of the Walsh, generalized ESD, and the modified z-score test for the detection of outliers as one central latency distribution aspect. The tests are applied to well defined test cases mimicking typical timing behavior expected from concurrent architectures of today. We introduce accompanying graphical visualization methods to inspect, analyze and communicate the latency behavior of VR systems beyond simple mean or worst-case values. As a result, we propose a stacked modified z-score test for more detailed analysis.

While power grid systems benefit from utilizing communication network through networked control and protection, the addition of communication exposes the power system to new security vulnerabilities and potential attacks. To mitigate these attacks, such as denial of service, intrusion detection systems (IDS) are often employed. In this paper we investigate the relationship of IDS accuracy performance to the stability of power systems via its impact on communication latency. Several IDS machine learning algorithms are implemented on the NSL-KDD dataset to obtain accuracy performance, and a mathematical model for computing the latency when incorporating IDS detection information during network routing is introduced. Simulation results on the New England 39-bus power system suggest that during a cyber-physical attack, a practical IDS can achieve similar stability as an ideal IDS with perfect detection. In addition, false positive rate has been found to have a larger impact than false negative rate under the simulation conditions studied. These observations can contribute to the design requirements of future embedded IDS solutions for power systems.