The advancement of big data has paved the way for the development of intelligent and smart applications; however, privacy concerns often hinder fully realizing their benefits. Federated Learning (FL) has emerged as a promising framework for enhancing privacy while training models collaboratively across decentralized data sources. However, it remains susceptible to poisoning attacks, severely undermining its effectiveness. Existing robust aggregation techniques often struggle with the sensitivity of data distributions, and cluster-based strategies often fail to cluster poisoned model updates correctly. The direction obtained from the signs of the gradient mostly solves these problems but remains vulnerable to dying ReLU problems and usually becomes sensitive to outliers. In this paper, we introduce SignDefence, a sign direction and LeakyReLU-based aggregation technique, which considers the direction of the gradients and overcomes the performance issues related to the dying ReLU problem. Moreover, the proposed SignDefence computes Jaccard Similarity over binary encoded model weights and remains robust across sparse data. The experimental results suggest that the proposed technique shows consistently better accuracy and F1 score than the state-of-the-art techniques, without attack and under different attack scenarios.

Federated Learning (FL) is an emerging paradigm that enables multiple clients to train a global model collaboratively without sharing their privacy-sensitive data. However, one of the significant challenges in FL is the aggregation of the model updates from different client devices, as malicious participants acting as Byzantine attackers can craft the model update and poison the global model. The state-of-the-art defence mechanisms mostly rely on aggregation-based security defences to improve the degraded accuracy. However, preventing attacker's participation in the training can have an impact on improving the global model's accuracy. Therefore, in this paper, FedChallenger, a dual-layer defence mechanism, is proposed, which attempts to detect and prevent malicious participation in the FL training process in its first layer. The other layer incorporates a trimmed-mean aggregation strategy, where pairwise cosine similarity identifies malicious updates and removes entire client updates from federated averaging. Extensive experiments using the BloodMNIST dataset validate that the FedChallenger gains nearly 85%, 80%, 15%, and 4% accuracy with more than 1.2 times faster convergence rate over the state-of-the-art Byzantine resilient aggregation strategies called FedAvg, Fang, Krum, and Trimmed-Mean approach, respectively, on 40% compromised devices. Above all, it shows consistently better results than them in both attack and non-attack scenarios.

A major problem of wireless devices is the detection of security threats in an efficient manner. Several recent incidents show that malicious applications (apps) can find their ways to online markets (e.g., Google Play Store) and be available for download and installation. Such malicious apps can collect sensitive data from millions of users and send them to a third-party servers. In this paper, we propose a methodology that leverages the power consumption of wireless devices to build a model that makes them more robust to the presence of malicious apps. The method consists of two stages: (i) Feature Extraction where stacked Restricted Boltzmann Machine (RBM) AutoEncoders (AE) and Principal Component Analysis (PCA) are used to extract features vector based on AE’s reconstruction errors. (ii) Classifier where One-Class Support Vector Machine is trained to perform the classification task. The validation of the methodology is performed on a real measurements dataset. The obtained results show a good potential and prove that AEs’ reconstruction error can be used as a good discriminating feature. The obtained detection accuracy surpasses previously reported techniques, where it reaches up to ~ 98% in some scenarios.

In this paper the technology of collecting the logs and the logs message format is considered in addition to investigating multiple log data sources as an input to the cloud management system. A comparison between message exchange technologies (JSON, XML) is evaluated with the latest message format technology (Google Protocol Buffer) when used in combination with the message transmission protocols (XML-RPC, REST, Network Socket). In addition, the sampling rate that gives the accurate reading of resource usage is investigated, which is used to select among different log data sources to achieve the accurate log update time. Logs sampling rate of 1.0 second is found to be the best with "xentop" data source. The result of the experiment shows using Protocol Buffer with Socket protocol gives the best results in reducing message size. Network socket with JSON gives the best processing delay and traveling time.

Cloud computing model is the most complex computing model that requires implementing effective techniques to manage infrastructure resources of datacenters. Unproductive tasks scheduling can lead to an increase in the operational cost of cloud provider side, which in turn increases the cloud services cost at cloud consumer side. One of the effective techniques to address these issues in cloud datacenters is the elasticity by allowing dynamic resource provisioning based on the current demand and varying workload running upon virtual machines (VMs) over time. This leads to an increase in the resource utilization, and reduced power consumption by turning off the idle physical machines. However, the dynamic resource provisioning due to the growing service demand and higher quality of service requirements of the users can cause a violation of service level agreement. In this paper, we propose a model based on linear regression to manage and reformulate cloud users requests and dynamically generating rules based on historical data of their requests in order to update association functions to address and adapt the changes of different types of workloads running on the cloud provider datacenter. The experiments and simulation results based on dynamic workloads show the proposed algorithm significantly increases the resource utilization on cloud datacenter.

Live VM migration is a technique that consists of a selection process and a migration process to migrate a VM from one host to another in the same data center without changing the IP address, or in a different data center with necessity to the VM to get a new IP address. The changing of IP address results into a mobility problem, which may render the service unreachable. In this paper, we propose a system model that selects data center randomly for VM placement while reducing this IP address reconfiguration. A new metric is proposed to indicate number of users that need IP reconfiguration. We extended CloudSim to simulate our work to identify the number of IP reconfigurations required for VM migration across the data centers on random workload.

Different applications in smartphones result in different power consumption patterns. The fact that every application has been coded to perform different tasks leads to the claim that every action onboard (whether software or hardware) will consequently have a trace in the power consumption of the smartphone. Even though the power consumed by the application might not be the same every time it is used, there still remains a similarity in the power consumption pattern. An anomalous behavior on the smartphone would result in a reduction in the similarity of the power consumption pattern. This change in similarity can be used to detect the presence of anomalous behavior of smartphones. We have proposed two approaches to detecting anomalous behavior on smartphones based on the power consumption pattern. The first approach is based on signal processing and the second approach explores the area of statistical learning in detecting malware. The two approaches have been analysed, evaluated, and compared. It has been observed that the signal processing method of detection performed better for anomalous behavior of lower intensity and the statistical learning method performed better for higher intensity anomalous behavior. It was also observed that both the methods are complementary.

A private cloud is maintained by an enterprise forits internal use. In such a scenario instead of buying the resources the enterprise can acquire the resources from a public cloud such as the ones provided by Amazon and Microsoft. On conventional systems rigorous analysis of the system and its workload is performed for determining the appropriate number of resources to be deployed on the private cloud. This paper presents a middleware framework that avoids this step of a priori capacity analysis and allows such private cloud owners to provision resources automatically such that a specified grade of service is maintained. The proposed framework performs dynamic resource provisioning that also leads to a reduction of operational cost. Additional resources are acquired during high traffic periods and released during low traffic periods such that the desired grade of service is always maintained. The paper describes the architecture of the framework and the experience gained from a prototype implementation including a preliminary analysis of its performance.

Delay tolerant networks (DTN) are characterized by a lack of continuous end-to-end connections due to node mobility, constrained power sources, and limited data storage space of some or all of its nodes. To overcome the frequent disconnections, DTN nodes are required to store data packets for long periods of time until they come near other nodes. Moreover, to increase the delivery probability, they spread multiple copies of the same packet on the network so that one of them reaches the destination. Given the limited storage and energy resources of many DTN nodes, there is a tradeoff between maximizing delivery and minimizing storage and energy consumption. In this paper, we study the routing problem in DTN with limited resources. We formulate a mathematical model for optimal routing, assuming the presence of a global observer that can collect information about all the nodes in the network. Next, we propose a new protocol based on social grouping among the nodes to maximize data delivery while minimizing network overhead by efficiently spreading the packet copies in the network. We compare the new protocol with the optimal results and the existing well-known routing protocols using real life simulations. Results show that the proposed protocol achieves higher delivery ratio and less average delay compared to other protocols with significant reduction in network overhead.

Networks in which nodes are intermittently connected, and have limited storage space and power, are termed Delay Tolerant Networks (DTN). To overcome these conditions, DTN routing protocols require nodes to store data packets for long periods of time until they contact with each other. In addition, they spread multiple copies of the same packet in the network to increase the probability of one of them reaching the destination. Long-term storage and multiple transmissions require large buffer space and non-restricted power availability which is hard to exist in DTN. In this paper, we study the routing problem in DTN with limited resources. We formulate a mathematical model for optimal routing, assuming the knowledge of present and future nodes contact and buffer space. After that, we analyze the previously developed heuristic protocols, and we propose a new protocol based on social relations between the nodes to avoid redundant copying of packets. Simulation results show that the proposed protocol significantly reduces energy consumption and provides better delivery ratio compared to other protocols.

In this paper, we propose a new intersection-based geographical routing protocol, called delay tolerant routing protocol (DTRP) that adapts to the changes in the local topology within city environments. DTRP is based on an effective selection of road intersections through which a packet must pass to reach the gateway to the Internet. The selection, in such delay tolerant VANETs, is made in a way that maximizes the connectivity probability of the route between mobile nodes and the gateway while maintaining a threshold for the end-to-end delay and the hop count within the network. To achieve this, we formulate the QoS routing problem mathematically as a constrained optimization problem. Specifically, analytical expressions for the connectivity probability as well as the delay and hop count of a route in a two-way road scenario are derived. Then, we propose a genetic algorithm to solve the optimization problem. Numerical and simulation results show that the proposed approach gives optimal or near-optimal solutions and improves significantly the VANETs performance when compared with several prominent routing protocols, such as GPSR, GPCR and OLSR.

In this paper, we propose a practical implementation of a location identification approach. In this approach, only the basic assumptions that are realistic in most types of ad hoc/sensor networks are required, which means that this system is implementable in most kinds of ad hoc/sensor networks. By introducing the idea of cell-based location identification method into the system, some drawbacks to the TDOA (time difference of arrival) system are resolved. More importantly, by employing the new theory, the location estimation accuracy of the proposed system is improved without costing extra resources. Finally, simulations show that the proposed Hybrid TDOA (HTDOA) location estimation system performs better in different environments compared with the current TDOA method.

A vehicular ad hoc network (VANET) is a network of intelligent vehicles that communicate with other vehicles in the network. The main objective of VANET is to provide comfort and safety for passengers. In addition, various transaction based services, such as information about gas prices, restaurant menu, and discount sale, can be provided to drivers. In order to make these services available, there is a need for an efficient service discovery protocol. In this paper, we propose a new protocol called Address Based Service Resolution Protocol (ABSRP) to discover services in vehicular ad-hoc networks. As most of the transaction based services are provided by roadside units, we exploit their presence to perform service discovery. We utilize the unique address assigned to each service provider in order to discover a route to that service provider. Our technique proactively distributes the service provider's address along with its servicing capabilities to other roadside units within a particular area. Each roadside unit will then utilize this information to service the request placed by the vehicles. If the service provider (destination node) is not reachable over the vehicular network, we propose to use a backbone network to service requests. Our approach is independent of the network layer routing protocol. We have evaluated the performance of our approach by using the Qualnet simulation tool.

Ad hoc, actuator and sensor wireless networks normally have critical connectivity properties before becoming fault intolerant. Existing algorithms for testing k-connectivity are centralized. In this article, we introduce localized algorithms for testing A-connectivity. In localized protocols, each node makes its own decision based on the information available in its local neighborhood. In the first proposed local neighbor detection (LND) algorithm, each node verifies whether or not itself and each of its p-hop neighbors have at least k neighbors. In the second local critical node detection (LCND) protocol, it also tests if the subgraph of its p-hop neighbours of a given node is k-connected. The third local subgraph connectivity detection (LSCD) protocol is based on communications between neighboring nodes to exchange the local decisions starting from k=l. All nodes declare themselves locally 1-connected. For k=2,3,..., iteratively, local decisions are propagated to p-hop neighbors. If node A is (k-1,)-connected, all its p-hop neighbors are (k-1)-connected, and the graph consisting of p-hop neighbors of A (excluding A) is (k-1,)-connected, then node A declares its neighborhood as k-connected. The experiments are carried with two ways of uniform generation of connected unit disk graphs. They show low percentage of false 'alarms', ability to locate critical areas in k-disconnected networks, and increased accuracy with increased local knowledge.

This report presents a reflection on software architecture practices based on our past ten year’s industrial experiences, particularly in the area of telecommunications. The report summarizes the methods, tools, and techniques that we have used on various projects. We also discuss, based on our experiences, what methods are useful, what remains to be validated, and what the gaps are between the state of practices and our wishes.

A growing body of literature suggests that there is an optimal size for software components. This means that components that are too small or too big will have a higher defect content (i.e., there is a U-shaped curve relating defect content to size). The U-shaped curve has become known as the "Goldilocks Conjecture." Recently, a cognitive theory has been proposed to explain this phenomenon and it has been expanded to characterize object-oriented software. This conjecture has wide implications for software engineering practice. It suggests 1) that designers should deliberately strive to design classes that are of the optimal size, 2) that program decomposition is harmful, and 3) that there exists a maximum (threshold) class size that should not be exceeded to ensure fewer faults in the software. The purpose of the current paper is to evaluate this conjecture for object-oriented systems. We first demonstrate that the claims of an optimal component/class size (1) above) and of smaller components/classes having a greater defect content (2) above) are due to a mathematical artifact in the analyses performed previously. We then empirically test the threshold effect claims of this conjecture (3) above). To our knowledge, the empirical test of size threshold effects for object-oriented systems has not been performed thus far. We performed an initial study with an industrial C++ system and repeated it twice on another C++ system and on a commercial Java application. Our results provide unambiguous evidence that there is no threshold effect of class size. We obtained the same result for three systems using four different size measures. These findings suggest that there is a simple continuous relationship between class size and faults, and that, optimal class size, smaller classes are better and threshold effects conjectures have no sound theoretical nor empirical basis.

Much effort has been devoted to the development and empirical validation of object-oriented metrics. The empirical validations performed thus far would suggest that a core set of validated metrics is close to being identified. However, none of these studies allow for the potentially confounding effect of class size. We demonstrate a strong size confounding effect and question the results of previous object-oriented metrics validation studies. We first investigated whether there is a confounding effect of class size in validation studies of object-oriented metrics and show that, based on previous work, there is reason to believe that such an effect exists. We then describe a detailed empirical methodology for identifying those effects. Finally, we perform a study on a large C++ telecommunications framework to examine if size is really a confounder. This study considered the Chidamber and Kemerer metrics and a subset of the Lorenz and Kidd metrics. The dependent variable was the incidence of a fault attributable to a field failure (fault-proneness of a class). Our findings indicate that, before controlling for size, the results are very similar to previous studies. The metrics that are expected to be validated are indeed associated with fault-proneness.

A practical application of object oriented measures is to predict which classes are likely to contain a fault. This is contended to be meaningful because object oriented measures are believed to be indicators of psychological complexity, and classes that are more complex are likely to be faulty. Recently, a cognitive theory was proposed suggesting that there are threshold effects for many object oriented measures. This means that object oriented classes are easy to understand as long as their complexity is below a threshold. Above that threshold their understandability decreases rapidly, leading to an increased probability of a fault. This occurs, according to the theory, due to an overflow of short-term human memory. If this theory is confirmed, then it would provide a mechanism that would explain the introduction of faults into object oriented systems, and would also provide some practical guidance on how to design object oriented programs. The authors empirically test this theory on two C++ telecommunications systems. They test for threshold effects in a subset of the Chidamber and Kemerer (CK) suite of measures (S. Chidamber and C. Kemerer, 1994). The dependent variable was the incidence of faults that lead to field failures. The results indicate that there are no threshold effects for any of the measures studied. This means that there is no value for the studied CK measures where the fault-proneness changes from being steady to rapidly increasing. The results are consistent across the two systems. Therefore, we can provide no support to the posited cognitive theory.

Software quality classification models seek to predict quality factors such as whether a module will be fault prone, or not. Case based reasoning (CBR) is a modeling technique that seeks to answer new questions by identifying similar "cases" from the past. When applied to software reliability, the working hypothesis of our approach is this: a module currently under development is probably fault prone if a module with similar product and process attributes in an earlier release was fault prone. The contribution of the paper is application of case based reasoning to software quality modeling. To the best of our knowledge, this is the first time that case based reasoning has been used to identify fault prone modules. A case study illustrates our approach and provides evidence that case based reasoning can be the basis for useful software quality classification models that are competitive with discriminant models. The case study revisits data from a previously published nonparametric discriminant analysis study. The Type II misclassification rate of the CBR model was substantially better than that of the discriminant model. Although the Type I misclassification rate was slightly greater and the overall misclassification rate was only slightly less, the CBR model was preferred when costs of misclassification were considered.

Society has become so dependent on reliable telecommunications, that failures can risk loss of emergency service, business disruptions, or isolation from friends. Consequently, telecommunications software is required to have high reliability. Many previous studies define the classification fault prone in terms of fault counts. This study defines fault prone as exceeding a threshold of debug code churn, defined as the number of lines added or changed due to bug fixes. Previous studies have characterized reuse history with simple categories. This study quantified new functionality with lines of code. The paper analyzes two consecutive releases of a large legacy software system for telecommunications. We applied discriminant analysis to identify fault prone modules based on 16 static software product metrics and the amount of code changed during development. Modules from one release were used as a fit data set and modules from the subsequent release were used as a test data set. In contrast, comparable prior studies of legacy systems split the data to simulate two releases. We validated the model with a realistic simulation of utilization of the fitted model with the test data set. Model results could be used to give extra attention to fault prone modules and thus, reduce the risk of unexpected problems.

Telecommunications software is known for its high reliability. Society has become so accustomed to reliable telecommunications, that failures can cause major disruptions. This is an experience report on application of discriminant analysis based on 20 static software product metrics, to identify fault prone modules in a large telecommunications system, so that reliability may be improved. We analyzed a sample of 2000 modules representing about 1.3 million lines of code, drawn from a much larger system. Sample modules were randomly divided into a fit data set and a test data set. We simulated utilization of the fitted model with the test data set. We found that identifying new modules and changed modules mere significant components of the discriminant model, and improved its performance. The results demonstrate that data on module reuse is a valuable input to quality models and that discriminant analysis can be a useful tool in early identification of fault prone software modules in large telecommunications systems. Model results could be used to identify those modules that would probably benefit from extra attention, and thus, reduce the risk of unexpected problems with those modules.