Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rewriting rules. Inspired by neuronal activities affected by enzymes, a numerical variant of SN P systems called enzymatic numerical spiking neural P systems (ENSNP systems) is proposed wherein each neuron has a set of variables with real values and a set of enzymatic activation-production spiking rules, and each synapse has an assigned weight. By using spiking rules, ENSNP systems can directly implement mathematical methods based on real numbers and continuous functions. Furthermore, ENSNP systems are used to model ENSNP membrane controllers (ENSNP-MCs) for robots implementing wall following. The trajectories, distances from the wall, and wheel speeds of robots with ENSNP-MCs for wall following are compared with those of a robot with a membrane controller for wall following. The average error values of the designed ENSNP-MCs are compared with three recently fuzzy logical controllers with optimization algorithms for wall following. The experimental results showed that the designed ENSNP-MCs can be candidates as efficient controllers to control robots implementing the task of wall following.

Enzymatic Numerical Spiking Neural Membrane Systems and their Application in Designing Membrane Controllers

Scalable and Energy Efficient Cluster Based Anomaly Detection Against Denial of Service Attacks in Wireless Sensor Networks

Spiking neural P systems with cooperative synapses

Satellite Internet (SI) is an essential component of 6G as an effective supplement to terrestrial Internet in the future. Owing to its relatively limited processing power and bandwidth, distributed denial of service (DDoS) attacks may cause substan-tial damage. Moreover, the complexity and difficulty of defense also increase due to the constantly changing topology scale of SI when supporting various applications. Therefore, path tracking must be maintained with high precision at different scales to realize protection at the boundary. However, the marking rate limits traditional packet marking methods, causing a decrease in the tracking accuracy when the topology is expanded. Therefore, we propose a scalable traffic path tracking (TPT) scheme. Firstly, a lightweight description of malicious traffic in the entire network is realized by improving a multi-dimensional traffic feature digesting method. Secondly, the connection relationship sparse matrix of nodes is obtained based on the Hidden Markov Model modeling of the network topology and attack scenarios. These generate the observation probability matrix B and transition probability matrix A of the traditional Viterbi algorithm. Finally, we optimize the Viterbi algorithm by adding an index matrix of the next-largest probability value to eliminate tracking loops, thereby achieving high tracking accuracy for topologies of different scales. The Keysight Ixia platform is used to generate malicious traffic in the experiments. The results demonstrate that the scheme can maintain a tracking accuracy of over 99 % against various DDoS attacks in topologies of different scales, which is more accurate and scalable than existing methods. 

TPT: A Scalable Traffic Path Tracking Scheme Using Improved Viterbi Algorithm in Satellite Internet

Homogenous spiking neural P systems (HSNP systems) are a class of neuron-inspired computing models, where each neuron contains an identical set of rules. It remains open how to design universal asynchronous HSNP systems. In this work, we introduce local rule synchronization into asynchronous HSNP systems, and such systems are abbreviated as AHSNPR systems. Specifically, a family of the rule sets is specified; a rule in a specified rule set is applied synchronously with all the other rules in the same set, and a rule not in any specified rule set is applied asynchronously. We investigate the number generating power of AHSNPR systems. It is proved that general and unbounded AHSNPR systems are universal, and bounded AHSNPR systems are only able to characterize the semilinear sets of numbers achieving the corresponding properties of decidability and closure. The results show that the local rule synchronization is useful in constructing universal asynchronous HSNP systems. 

Asynchronous homogenous spiking neural P systems with local rule synchronization

Control languages accepted by labeled spiking neural P systems with rules on synapses

Distributed Denial of Service (DDoS) ranks among the top ten threats to the cloud computing environment. DDoS mainly targets limited resources of cloud like bandwidth and CPU thereby denying access to legitimate clients. DDoS attacks are initiated by a vast network of remotely controlled nodes called zombies. New forms of DDoS are invented every day. Therefore, DDoS preventive measures do not fully guarantee its mitigation. Detecting an attack and defending it as early as possible is critical for reducing the attack impact. The real solution to mitigate any attack is tracing back the attacker and punishing him. However, a real attacker will masquerade his identity using a spoofed address to avoid being traced back. The routing mechanism used on the internet does not have any memory of its own making traceback further difficult. Many businesses are reluctant to enter the cloud due to these DDoS vulnerabilities of the cloud. DDoS will affect network performance and may disrupt configuration information available in the system. In the event of DDoS, businesses will have to suffer reputation damage, customer agitation, and legal repercussions. Unless cloud is made secure, we cannot benefit from its full potential. Research on DDoS attacks and defense is in its infancy. DDoS defense and traceback is still an open and challenging problem. This paper presents basic types of DDoS and focuses more on DDoS prevention, detection, and traceback techniques.

A Survey on DDoS Prevention, Detection, and Traceback in Cloud

The applications of deep learning extend to many aspects of daily life and are not confined to the domains of computer science alone. From face recognition to the smart grid domain, deep learning has proved itself to be an effective tool in producing state-of-the-art results. This chapter discusses how the usage of a high-level language like Python and its compatibility with deep learning frameworks and its collection of utility libraries facilitates practitioners in the development process. The chapter further dwells into details of the implementation of deep learning techniques applied to different applications, namely, facial recognition, fingerprint recognition, character recognition, smart grids, and renewable energy, by providing a brief history of how the technology has influenced the domain and details regarding the common datasets used as well as a code implementation in Python thoroughly covering the different steps.

Deep Learning Applications with Python

Internet censorship exists in more than 65 counties of the world including India. The government of these countries is closely monitoring the internet activity of its citizens though they publicly deny it. India uses selective filtering to safeguard its political, social, and security interests. Indian authorities realized the importance of censorship only after Mumbai bombings of 2008, which was organized with the help of internet. There is no fixed criteria or rule to censor the internet. However, removing/blocking sensitive content has become more common in recent years. This type of censoring was not completely successful as citizens found various ways to bypass them including alternate DNS servers, mirror/proxy websites, and virtual private networks. Irreversible damage would have already occurred by the time authorities completely remove malicious content manually. Removing entire malicious content manually takes time within which irreversible damage would have occurred. This paper proposes a censorware based on machine learning. It uses a modified version of the Naive Bayes algorithm to analyze captured packets and censor it. It is designed as a continuous learning tool to adapt itself to new types of circumvention techniques. The development is still in early stages. The results achieved demonstrates the effectiveness of proposed techniques compared to traditional methods. This improved version of censorware will be more helpful to the government as well as the citizens.

Ajeesh Ramanujan

Papers

Control languages accepted by labeled spiking neural P systems with rules on synapses

A Survey on DDoS Prevention, Detection, and Traceback in Cloud

Deep Learning Applications with Python

Internet Censorship Based on Bayes Learning Model