In the rapidly evolving landscape of smartphones and handheld devices, Android malware stands as a substantial security concern. Employing static analysis for mobile malware presents a proactive approach to understanding and unveiling potential threats within Android applications without the need for execution. Most of the existing studies on static analysis rely on machine learning. However, the black-box nature of machine learning models and their lack of explainability often hinder trust, transparency, and the ability to understand or justify their predictions. Counterfactual explanations enable security analysts to grasp the reasoning behind the decisions of black-box machine learning models (the “why?”) and also offer a way to pinpoint specific data instances whose alteration would lead to different prediction results (the “why not?”). In this paper, we investigate the use of the counterfactual explanation method to explain the predictions made by a machine learning model for Android malware classification. We assessed the quality of counterfactual explanations using a stability metric as well as investigating their feasibility by defining feature-based constraints.

A promising solution to alleviate the mobile traffic burden on the Internet is to cache the most popular content at the heterogeneous wireless network edge. However, due to the vast content stored at the remote server, and to cache effectively, it concerns the file popularity profile that may not be known by the network operators in advance. Therefore, online learning techniques are used to tackle the challenges brought by the unknown knowledge. We present an effective and efficient algorithm based on the stochastic combinatorial multi-armed bandits with locked-up slots to address the content caching problem. Our work particularly addresses the scenario where dense small cells with diverse user populations are deployed. Additionally, this network is only given limited external resources such as computational resource to learn the caching policies and wireless backhaul resource to refresh the caches. Our algorithm learns the caching policies online which is to decide which files to be cached sequentially. Despite sharing the limited external resources, the proposed algorithm guarantees the performance of each small cell to approach the optimum. Experiments are conducted to cross-validate the theorem presented in this work.

Currently, one of the main enablers for network evolution is software-defined networking (SDN), where the control plane is decoupled from the data plane. A controller, as a (logically) centralized entity in the control plane, is the Achilles' heel of SDN resilience since its failure would affect the proper functioning of the entire network. The resilience of the control plane is strongly linked to the controller placement problem, which deals with the positioning and assignment of controllers to the forwarding devices (i.e., switches). A resilient controller placement problem needs to assign more than one controller to a switch while it satisfies certain quality of service requirements. In this paper, we propose a solution for such a problem that, unlike most of the former studies, takes both the switch-controller/inter-controller latency requirements and the capacity of the controllers into account to meet the traffic load of switches. The proposed algorithms, one of which has a polynomial-time complexity, adopt a clique-based approach in graph theory to find high-quality solutions heuristically. It is evaluated with real wide area network (WAN) topologies and the corresponding results are extensively analyzed. The resultant studies equip the service providers with helpful insights into the design of a resilient software-defined WAN.

In recent years, dramatic growth of mobile data traffic has left the operators no choice but to consider Wi-Fi networks as an economic complementary solution. To achieve this, WLANs require to adopt some of the key features of carrier-grade operators, such as centralized resource management. As an emerging paradigm, Software Defined Networking (SDN) can be used to provide salient centralized network solutions for Wi-Fi infrastructures. In fact, applying SDN to different wireless platforms, e.g., Wi-Fi Mesh Networks (WMNs), brings unprecedented opportunities to improve the network performance by employing more sophisticated algorithms at SDN controllers. Moreover, it should be noted that traffic engineering over WMNs incorporates tightly correlated steps including association control, gateway selection and flow routing which are individually NP-hard problems. In this paper, we present an agile and fine-grained access provisioning solution via bridging the cellular and Wi-Fi technologies that empowers us to address the users demand by steering data flows on different tiers of WMNs. In contrast to the prior work, we present a detailed unified formulation for joint gateway selection and flow routing in Multi-Channel Multi-Radio (MCMR) WMNs that considers the key attributes of wireless networks. The functionality of the presented solution is evaluated through various experiments with extensive numerical results.

Nowadays, Wireless Mesh Networks (WMNs) have received worldwide acceptance as an attractive paradigm on top of the existing and upcoming wireless technologies. Up to now, a lot of research studies have been conducted on the channel assignment in multi-radio WMNs. To address the destructive impact of interference as an inevitable element of WMNs, it is necessary to reconfigure the wireless radios for switching to the less-congested channels, frequently. Although this problem has been investigated through extensive modeling and simulations, the consequences of channel switching for popular WMN routing schemes have not been assessed sufficiently. In this paper, we present a concise analysis of channel switching latency in multi-hop multi-radio scenarios for OLSR, BATMAN-adv and Open80211s protocols through experimentation testbed. Moreover, we evaluate the functionality of SDN-based WMNs in terms of channel switching delay. The acquired results substantiate that although by using SDN it is feasible to improve the network responsiveness, still the channel switching latency is not negligible.

Software Defined Networking (SDN) is an emerging paradigm for network design and management. By providing network programmability and separation of control and data planes, SDN offers salient features such as simplified and centralized management and control, reduced complexity and accelerated innovation. However, SDN introduces new challenges that should be addressed properly in order to benefit from its unprecedented capabilities. Due to the (logically) centralized control in SDN, the resilience of the control plane has a great impact on the functioning of the whole system. In this case, resilient controller placement problem (how many controllers are needed and where to place them to provide higher reliability) is a hot research topic that affects the reliability and performance of SDN in Wide Area Networks (WANs). Thus, we define a resilient controller placement problem, which satisfies a set of constraints, some of which are missing in the existing solutions. The acquired results on real tier-1 US service provider network topologies demonstrate the effectiveness of the approach. This can give helpful insights to the network operators for designing or modifying their network topologies to enhance the resilience of the control plane in SDN.

Chaotic deployment of Wireless Local Area Networks (WLANs) in dense urban areas is one of the common issues of many Internet Service Providers (ISPs) and Wi-Fi users. It results in a substantial reduction of the throughput and impedes the balanced distribution of bandwidth among the users. Most of these networks are managed independently and there is no cooperation among them. Moreover, the conventional association mechanism that selects the Access Points (APs) with the strongest Received Signal Strength Indicator (RSSI) aggravates this situation. In this paper, we present a versatile near-optimal solution for the fair bandwidth distribution over virtualized WLANs through dynamic association control. The proposed scheme is called ACO-PF, which is developed on top of Ant Colony Optimization (ACO) as a meta-heuristic technique to provide Proportional Fairness (PF) among the greedy clients. In fact, it presents a generic and centralized solution for ISPs that are using a common, virtualized or overlapped WLAN infrastructure for serving their customers. We have evaluated the efficacy of ACO-PF through numerical analysis versus popular existing schemes for both downlink and uplink scenarios. Our proposed technique has less complexity in terms of the implementation and running time for largescale WLANs and it can be easily developed and customized for different objective functions. In addition, it is implemented in a testbed environment to investigate the key challenges of real deployment scenarios.

Modern wireless broadband networks are crucial for different mission-critical applications and public safety agencies. Various natural disasters and physical attacks would result in the malfunction or failure of wireless and cellular infrastructures. This subsequently affects the correct functioning of the dependent mission- critical applications. Thus, disaster management and response is of great importance. In this paper, we focus on disaster response using D2D communications to extend the coverage of base stations, and on controller-assisted routing to maximize the total end-to-end throughput for all of the current flows from the area without network coverage using the ant colony optimization. The proposed routing scheme outperforms the schemes based on the shortest path routing in terms of the total throughput as well as fairness in allocating the rates to the flows.

Node mobility and end-to-end disconnections in Delay Tolerant Networks (DTNs) greatly weaken the effectiveness of data transmission. Although social-based strategies can be used to deal with the problem, most existing approaches adopt multicopy strategy to forward messages which inevitably add more unnecessary cost. One of the most important issues is the selection of the best intermediate node to forward messages to the destination node. In this paper, we focus on finding a quality metric associated with better relays which is evaluated by Reachable Probability Centrality (RPC) as we proposed. RPC combines the contact matrix and multi-hop forwarding probability based on the weighted social network, thus ensuring an effective relay selection. We also propose a distributed RPC-based routing algorithm, which demonstrates the applicability of our scheme in the decentralized environment of DTNs. Extensive trace-driven simulations show that RPC outperforms other centrality measures and our proposed routing algorithm can significantly reduce the data forwarding cost while having comparable delivery ratio and delay to those of the Epidemic routing.