This article proposes a lightweight two-way authentication scheme, PUF-ILM, that integrates Physical Unclonable Function (PUF) and Improved Logistic Map (ILM). The PUF generates unique challenge-response pairs (CRPs) for each device using its inherent physical randomness, thereby achieving reliable device identity authentication. The ILM encrypts and obfuscates the transmitted CRPs by enhancing their chaotic characteristics, expanding the parameter range, and eliminating periodic windows, effectively resisting modeling and eavesdropping attacks. Security analysis indicates that this scheme possesses several known security attributes, including anti-cloning, anonymity, two-way authentication, forward/backward security, and anti-machine-learning modeling. Performance evaluation shows that PUF-ILM maintains low communication overhead while maintaining a simple system structure and does not rely on complex hardware or external trusted institutions, offering high security and strong practicality, making it especially suitable for large-scale deployment in resource-constrained Internet of Vehicles environments.

A data-efficient intrusion detection framework tailored for Wireless Sensor Networks (WSNs) is proposed by leveraging active learning and metaheuristic optimization techniques. This framework addresses three major limitations of traditional models: data imbalance, inefficient hyperparameter tuning, and the need for large labeled datasets. To handle class imbalance, adaptive synthetic sampling generates synthetic instances for minority classes, particularly enhancing learning in complex regions of the feature space. For hyperparameter optimization, the Sandpiper Optimization (SO) algorithm is employed to fine-tune the regularization parameter of Logistic Regression (LR), leading to improved generalization. The issue of limited labeled data is tackled using Active Learning Uncertainty (ALU) and Entropy-based Active Learning (ALE), which query the most informative samples from the unlabeled pool, maximizing learning with minimal annotation effort. Simulation results show that LRALU, LRALE, and LRSO outperform traditional models with improvements of 18.18%, 19.48%, and 9.09% in accuracy; 9.30%, 1.16%, and 9.30% in precision; 18.18%, 19.48%, and 9.09% in recall; 12.20%, 8.54%, and 7.32% in F1-score; and 14.63%, 12.20%, and 9.76% in ROC-AUC, respectively. Additionally, log loss is reduced by 6.45%, 6.45%, and 35.48% for LRALU, LRALE, and LRSO, respectively. These results demonstrate that integrating intelligent sampling, active learning, and nature-inspired optimization significantly enhances intrusion detection performance in WSNs, providing an annotation-efficient solution for practical deployment.

Underwater Wireless Sensor Networks (UWSNs) are among the most promising research areas these days due to their unique characteristics and diverse underwater applications. Though a number of routing protocols have been designed and implemented for UWSNs over the past few years, the researchers face several challenges, e.g., low speed of propagation, small bandwidth, limited battery power, etc., while designing routing protocols for communication in UWSNs. Acoustic sensor nodes are equipped with batteries with limited power and it is quite costly to replace or recharge them. The network will not survive for the desired period of time if the power of node batteries is not efficiently used. To effectively resolve this issue, this paper proposes a Shifted Energy Efficiency and Priority (SHEEP) routing protocol for UWSNs. The proposed protocol aims to enhance the efficiency of the state-of-the-art Energy Balanced Efficient and Reliable Routing (EBER2) protocol for UWSNs. SHEEP is built upon the depth and energy of the current forwarding node, the depth of the expected next forwarding node, and the average energy difference among the expected forwarders. Simulation results demonstrate that SHEEP improves the energy efficiency and packet delivery ratio of EBER2 by 7.4% and 13% respectively.

In the era of the Internet of Things (IoT), connected objects produce an enormous amount of data traffic that feed big data analytics, which could be used in discovering unseen patterns and identifying anomalous traffic. In this paper, we identify five key design principles that should be considered when developing a deep learning-based intrusion detection system (IDS) for the IoT. Based on these principles, we design and implement Temporal Convolution Neural Network (TCNN), a deep learning framework for intrusion detection systems in IoT, which combines Convolution Neural Network (CNN) with causal convolution. TCNN is combined with Synthetic Minority Oversampling Technique-Nominal Continuous (SMOTE-NC) to handle unbalanced dataset. It is also combined with efficient feature engineering techniques, which consist of feature space reduction and feature transformation. TCNN is evaluated on Bot-IoT dataset and compared with two common machine learning algorithms, i.e., Logistic Regression (LR) and Random Forest (RF), and two deep learning techniques, i.e., LSTM and CNN. Experimental results show that TCNN achieves a good trade-off between effectiveness and efficiency. It outperforms the state-of-the-art deep learning IDSs that are tested on Bot-IoT dataset and records an accuracy of 99.9986% for multiclass traffic detection, and shows a very close performance to CNN with respect to the training time.

Hardware components, such as memory and arithmetic units, are integral part of every computer-controlled system, for example, Unmanned Aerial Vehicles (UAVs). The fundamental requirement of these hardware components is that they must behave as desired; otherwise, the whole system built upon them may fail. To determine whether or not a component is behaving adequately, the desired behaviour of the component is often specified in the Boolean algebra. Boolean algebra is one of the most widely used mathematical tools to analyse hardware components represented at gate level using Boolean functions. To ensure reliable computer-controlled system design, simulation and testing methods are commonly used to detect faults; however, such methods do not ensure absence of faults. In critical systems’ design, such as UAVs, the simulation-based techniques are often augmented with mathematical tools and techniques to prove stronger properties, for example, absence of faults, in the early stages of the system design. In this paper, we define a lightweight mathematical framework in computer-based theorem prover Coq for describing and reasoning about Boolean algebra and hardware components (logic circuits) modelled as Boolean functions. To demonstrate the usefulness of the framework, we (1) define and prove the correctness of principle of duality mechanically using a computer tool and all basic theorems of Boolean algebra, (2) formally define the algebraic manipulation (step-by-step procedure of proving functional equivalence of functions) used in Boolean function simplification, and (3) verify functional correctness and reliability properties of two hardware components. The major advantage of using mechanical theorem provers is that the correctness of all definitions and proofs can be checked mechanically using the type checker and proof checker facilities of the proof assistant Coq.

The following topics are dealt with: security of data; cloud computing; business data processing; Internet of Things; data analysis; groupware; information retrieval; Internet; natural language processing; data protection.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.

In this paper, the performance analysis of a hierarchical routing protocol for mobile ad hoc networks (MANETs) called Location‐aware Grid‐based Hierarchical Routing (LGHR) is performed. In LGHR, the network comprises nonoverlapping zones and each zone is further partitioned into smaller grids. Although LGHR is a location‐aware routing protocol, the routing mechanism is similar to the link‐state routing. The protocol overcomes some of the weaknesses of other existing location‐based routing protocols such as Zone‐based Hierarchical Link State (ZHLS) and GRID. A detailed analysis of the LGHR routing protocol is performed and its performance is compared with both the above‐mentioned protocols. The comparison shows that LGHR works better than ZHLS in terms of storage overhead as well as communication overhead, whereas LGHR is more stable than GRID especially in scenarios where wireless nodes are moving with very high velocities.

The goal of SSTM'17 was to attract young researchers, Ph.D. students, practitioners, and industry experts to bring contributions in the area of Security, Safety and Trust Management, especially in the developments of computing, management and programming models, technologies, framework and middleware.

Abstract OpenID is widely being used for user centric identity management in many Web applications. OpenID provides Web users with the ability to manage their identities through third party identity providers while remaining independent of the subject that actually uses the identities to authenticate individuals. Starting from the early stages of its inception, OpenID has received a large amount of acceptance and use in the current Web community because of its flexibility and ease of use. However, in addition to its benefits and flexibilities, OpenID faces its own share of vulnerabilities and threats, which have made its future and large‐scale use in cyberspace questionable. OpenID Phishing is one such attack that has received much attention and that requires a comprehensive solution. This paper aims at identifying and discussing a solution to OpenID Phishing by proposing a user authentication scheme that allows OpenID providers to identify a user using publicly known entities. The research will help in next‐generation cyber security innovations by reducing the authentication dependency on user credentials, that is, login name/password. The authentication scheme is also validated through detailed descriptions of use cases and prototype implementation. Copyright © 2014 John Wiley & Sons, Ltd.

Arrhythmia is caused by improper and irregular sinus rhythm or heartbeats. In order to diagnose cardiac arrhyth- mia, electrocardiogram (ECG) beat classification and analysis is very necessary. The efficiency and accuracy of any classification model highly depends on selecting the most relevant features. The aim of this study is to classify different arrhythmic beats with a reduced set of relevant-only ECG features. To optimize the ECG feature selection process and increase the classification accuracy, a Mahalanobis-Taguchi System (MTS) based classifica- tion and analysis scheme is proposed. MTS is a multi-dimensional pattern recognition system which dynamically selects important features for further analysis. Arrhythmia can occur at any time and thus requires proper and continuous monitoring of the patient to reduce sudden heart attacks. The proposed MTS- based classification scheme is integrated with a Wireless Body Area Network (WBAN) for pervasive monitoring. The proposed scheme is analyzed and compared with a state-of-the-art scheme in terms of sensitivity, specificity, and accuracy. The results show that the proposed scheme performs significantly better than the other scheme by achieving high sensitivity, specificity, and classification accuracy for different arrhythmic heartbeats i.e., Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC), Right Bundle Branch Block (RBBB), and Atrial Premature Contraction (APC).

Abstract Network security risks grow with increase in the network size. In recent past, the attacks on computer networks have increased tremendously and require efficient network intrusion detection mechanisms. Data mining and machine‐learning techniques have been used for network intrusion detection during the past few years and have gained much popularity. In this paper, we propose an intrusion detection mechanism based on binary particle swarm optimization (PSO) and random forests (RF) algorithms called PSO‐RF and investigate the performance of various dimension reduction techniques along with a set of different classifiers including the proposed approach. Binary PSO is used to find more appropriate set of attributes for classifying network intrusions, and RF is used as a classifier. In the preprocessing step, we reduce the dimensions of the dataset by using different state‐of‐the‐art dimension reduction techniques, and then this reduced dataset is presented to the proposed PSO‐RF approach that further optimizes the dimensions of the data and finds an optimal set of features. PSO is an optimization method that has a strong global search capability and is used here for dimension optimization. We perform extensive experimentation to prove the worth of the proposed approach by using different performance metrics. The standard benchmark, that is, KDD99Cup dataset, is used that contains the information about various kinds of network intrusions. The experimental results indicate that the proposed approach performs better than the other approaches for the detection of all kinds of attacks present in the dataset. Copyright © 2012 John Wiley & Sons, Ltd.

Particle Swarm Optimization (PSO), a population based optimization technique, has two intrinsic problems of slow convergence and tendency to converge prematurely. In order to overcome these problems, we propose an improvement to the velocity update equation of the standard PSO algorithm in which particles of a swarm tend to move towards the global best position more rapidly as compared to the local best position. Two different non-linear weight factors are multiplied with the two parts of the velocity update equation; one that tends to move the particle to the global best position, while the other tends to move the particle back to its local best position achieved so far. By introducing the separate weight factors, a significant improvement in the results is seen. We test the proposed algorithm on six benchmark functions and the simulation results are presented. The results indicate that the proposed algorithm does not converge prematurely and its convergence speed is faster than the standard PSO algorithm.

Group Counseling Optimizer (GCO) is a new heuristic inspired by human behavior in problem solving during counseling within a group. GCO has been found to be successful in case of single-objective optimization problems, but so far it has not been extended to deal with multi-objective optimization problems. In this paper, a Pareto dominance based GCO technique is presented in order to allow this approach to handle multi-objective optimization problems. In order to compute change in decision for each individual, we also incorporate a selfbelief counseling probability operator in the original GCO algorithm that enriches the exploratory capabilities of our algorithm. The proposed Multi-objective Group Counseling Optimizer (MOGCO) is tested using several standard benchmark functions and metrics taken from the literature for multiobjective optimization. The results of our experiments indicate that the approach is highly competitive and can be considered as a viable alternative to solve multi-objective optimization problems.

In this paper, we propose a new evolutionary optimization algorithm called Welfare State Optimization (WSO) for solving optimization problems. In this algorithm, we emulate the behavior of welfare states to improve the lives of their citizens. The work is motivated by the fact that the welfare state optimally uses its resources (optimization) and restricts a group to lead the whole nation to a specific direction (local trap). So, the behavior of a welfare state is quite suitable for optimization algorithms. The proposed WSO algorithm is validated using ten standard benchmark functions and its performance is compared with five different variants of Particle Swarm Optimization (PSO) available in the literature. The results of our experiments are very promising and confirm the validity of the proposed approach. Hence, WSO algorithm can be considered as a strong alternative to solve optimization problems.

A system connected to a network is an open choice for network intrusions unless a powerful intrusion detection or prevention system is implemented. Network security has become a serious issue due to increased unauthorized access and manipulation of network resources. Evolutionary approaches play an important role in identifying attacks with high detection rates and low false discovery rates. In this paper, a binary version of multi-objective particle swarm optimization (PSO) approach is used to detect PROBE attacks in a network. A vector evaluated PSO approach is used in the proposed technique with two objectives i.e., intrusion detection rate and false discovery rate, to guide the process of feature selection. The experiments are performed using the well-known KDD99Cup dataset. Multi-objective PSO approach is used for feature selection from a set of 41 features and Random Forests (RF), a highly accurate and fast algorithm, is used for classification. Empirical results show that the proposed technique outperforms well-known classification and regression techniques in most of the cases.

Location information of sensor nodes deployed in the mission field plays an important role on the performance of Wireless Sensor Networks (WSNs). It is highly desirable to develop localization systems by keeping in mind WSN constraints and its location estimation capability. Optimization algorithms have proven to be good candidates for quality of position estimation. Flip ambiguity is one of the major challenges in such techniques. In this paper two types of constraints are proposed to overcome this problem. Particle Swarm Optimization (PSO) in conjunction with the proposed constraints is used iteratively in distributed manners to localize blind nodes in the WSN. Simulation results show that the proposed technique overcomes the problem of flip ambiguity and is resource efficient as well. The proposed technique mitigates 95 percent (worst-case) to 100 percent (best-case) flips and saves 80 percent (worst-case) to 87 percent (best-case) energy as compared to the previous technique available in the literature.

From the security perspective Mobile Ad hoc Networks (MANETs) are amongst the most challenging research areas and one of the key reasons for this is the ambiguous nature of insider attacks in these networks. In recent years, many attempts have been made to study the intrinsic attributes of these insider attacks but the focus has generally been on the analysis of one or very few particular attacks, or only the survey of various attacks without any performance analysis. Therefore, a major feature that research has lately lacked is a detailed and comprehensive study of the effects of various insider attacks on the overall performance of MANETs. In this paper we investigate, in detail, some of the most severe attacks against MANETs namely the blackhole attack, sinkhole attack, selfish node behavior, RREQ flood, hello flood, and selective forwarding attack. A detailed NS-2 implementation of launching these attacks successfully using Ad hoc On-Demand Distance Vector (AODV) routing protocol has been presented and a comprehensive and comparative analysis of these attacks is performed. We use packet efficiency, routing overhead, and throughput as our performance metrics. Our simulation-based study shows that flooding attacks like RREQ flood and hello flood drastically increase the routing overhead of the protocol. Route modification attacks such as sinkhole and blackhole are deadly and severely affect the packet efficiency and bring down the throughput to unacceptable ranges.

During the past few years, huge amount of network attacks have increased the requirement of efficient network intrusion detection techniques. Different classification techniques for identifying various attacks have been proposed in the literature. In this paper we propose and implement a hybrid classifier based on binary particle swarm optimization (BPSO) and random forests (RF) algorithm for the classification of PROBE attacks in a network. PSO is an optimization method which has a strong global search capability and is used for fine-tuning of the features whereas RF, a highly accurate classifier, is used here for classification. We demonstrate the performance of our technique using KDD99Cup dataset. We also compare the performance of our proposed classifier with eight other well-known classifiers and the results show that the performance achieved by the proposed classifier is much better than the other approaches.

A highly efficient Binary PSO based cryptanalysis approach for four-rounded DES is presented. Several optimum keys are generated in different runs of the algorithm on the basis of their fitness value and finally, the real key is found by guessing every individual bit. The robustness of the proposed technique is also checked for eight-rounded DES. Our proposed approach has shown promising results over cryptanalysis of DES performed using other methods such as Genetic Algorithm.